I. Chamatidis, M. Stoumpos, G. Kazakis, N.A. Kallioras, S. Triantafyllou, V. Plevris and N.D. Lagaros, "Overview on Machine Learning Assisted Topology Optimization Methodologies", in Machine Learning in Modeling and Simulation (Part of the book series: Computational Methods in Engineering & the Sciences (CMES)), T. Rabczuk and K-J. Bathe (Eds.), Springer, pp. 373-394, 2023.

Abstract:
The past two decades saw tremendous developments in artificial intelligence (AI). Advancements in software, algorithms, and hardware led to the development of significantly more accurate and versatile artificial intelligence models. This rendered artificial intelligence a powerful tool that is used in diverse scientific areas, e.g. medicine and drug design, economics, and self-driving cars, among many others. These methods, having been successfully implemented in the simulation and modeling of structures, found their way to topology optimization problems, where artificial intelligence appears to have great potential for successful implementation. In conventional topology optimization, the optimal design of a specific domain must be calculated subject to specific constraints and the objective is to minimize the total compliance of the structure and use a specific amount of material. This is typically an iterative process that involves large matrices and can be very timeconsuming. By means of artificial intelligence models, referred to also as surrogate models (or surrogates), the computing time can be reduced significantly. The surrogate model is apriori trained offline. Following, during the optimization process the model is inferred based on input data, which is a lot faster due to limited matrix multiplications that the surrogate performs. The usual process involves either an artificial intelligence surrogate that complements the conventional procedure to reduce computational costs or a standalone surrogate which calculates the whole optimized structures by itself. The AI surrogates that are used belong to two main categories, i.e. Surrogates that use density and surrogates that use images. The surrogates that use density have similar inputs as the conventional method since the optimization process uses the density of the structure and is updated in each iteration of the AImodel. The surrogates that perform optimization on images are a bit different because they use techniques like image segmentation and filtering to output the optimized image (structure) which then is mapped into density. Most surrogates can be used for 2D and 3D structures and they are transferable, meaning that once trained they can be used in another topology optimization problem (thermodynamics or different material). The Background section contains an introduction to artificial intelligence, the surrogate models that will be used and an introduction to conventional topology optimization. The Literature Survey section provides areview ofrecent advancements of topology optimization using artificial intelligence models. This section is divided into two parts, the first describing the models that use density and the second the models that use image-based approaches.

K.G.M. Kandethanthri, M. Nikoo, G. Hafeez, A. Bagchi and V. Plevris, "Estimating the In-Plane Lateral Resistance of Reinforced Log Wall Employing Soft Modelling Techniques", in Advanced Optimization Applications in Engineering, A. Ahmad and C.V. Camp (Eds.), IGI Global, pp. 1-21, 2024.

Abstract:
The popularity of log houses has been on the rise in numerous regions worldwide. In the context of log construction, the stability of log walls is notably influenced by the friction existing between the layers of logs and the openings designated for windows and doors. This study endeavors to comprehensively evaluate the lateral resistance of log walls through an extensive parametric analysis utilizing finite element (FE) methods. To construct a robust dataset, a total of 71 distinct samples were generated employing FE analysis, where the shuffled frog-leaping algorithm (SFLA) was incorporated in conjunction with a feed-forward (FF) neural network. Within this framework, the accuracy of the SFLA-based informational model was juxtaposed against that of an artificial neural network (ANN) coupled with particle swarm optimization (PSO), genetic algorithm (GA), and statistical models including multiple linear regression (MLR).

Book: "Insights: Frontiers in Built Environment", Eds: Z. Chen, A. Matsumoto, J.R. Casas, V. Plevris, G. Tsiatas, H. Guo, Y. Li and S. Kaewunruen, Frontiers Media SA, Lausanne, 2024.

Description

We are now entering the third decade of the 21st Century, and, especially in recent years, the achievements made by researchers and professionals have been exceptional, leading to major advancements in the fast-growing field of the Built Environment.Frontiers has organised a series of Research Topics to highlight the latest advancements in research across the field of Built Environment with articles from the members of our accomplished Editorial Boards. This editorial initiative in question, led by Dr. Zhen Chen, Specialty Chief Editor of Frontiers Construction Management is focused on new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives in the field of Built Environment. The Research Topic solicits brief, forward-looking articles from the Editorial Board Members that describe the state of the art, outlining recent developments and major accomplishments that have been achieved and that need to occur to move the field forward. Authors are encouraged to identify the greatest challenges in the sub-disciplines, and how to address those challenges.The goal of this special edition Research Topic is to shed light on the progress made in the past decade in the Built Environment field, and on its future challenges to provide a thorough overview of the field. This article collection will inspire, inform, and provide direction and guidance to researchers and practitioners in the field. We welcome original research, reviews, perspective, outstanding achievements in the Built Environment field and thought-provoking opinion pieces to this Research Topic.

Book: "Advanced Concrete and Construction Materials", Eds: M. Kioumarsi and V. Plevris, ISBN-13: 978-3-7258-0446-7 (hardcover), E-ISBN-13: 978-3-7258-0445-0 (ebook), DOI: 10.3390/books978-3-7258-0445-0, MDPI, Basel, Switzerland, 182 pages, 2024.

Description

The reprint Advanced Concrete and Construction Materials offers a comprehensive exploration of cutting-edge research and critical insights into the dynamic field of construction materials and structural engineering. This meticulously curated volume delves beyond the traditional perceptions of concrete, examining transformative microstructures and alternative binders, which hold immense potential for revolutionizing construction practices and reducing the carbon footprint of concrete. Comprising ten diverse studies, the reprint tackles pivotal sustainability, durability, and innovation issues within the construction industry. From enhancing the strength and durability of fly ash aggregate concrete with nanosilica to exploring the utilization of waste tires for reinforcing concrete columns, each contribution offers valuable insights and showcases innovative approaches. Additionally, the reprint delves into numerical modeling techniques for specialized cementitious composites and evaluates the viability of utilizing water-treatment sludge as a sustainable alternative to clay in fired clay bricks. A valuable resource for academic professionals and researchers, this collection underscores the significance of advancing construction materials. By providing a platform for groundbreaking research and critical reviews, it encourages scholars to contribute to the ongoing discourse, thereby shaping the future of construction.

G. Papazafeiropoulos and V. Plevris, "OpenSeismoMatlab: New Features, Verification and Charting Future Endeavors", Buildings, 14(1), Article ID 304, 31 pages (DOI: 10.3390/buildings14010304), 2024.

Abstract:
To facilitate the precise design of earthquake-resistant structures, it is imperative to accurately evaluate the impact of seismic events on these constructions and predict their responses. OpenSeismoMatlab, a robust, free ground motion data processing software, plays a pivotal role in this endeavor. It empowers users to compute a wide array of outcomes using input acceleration time histories, encompassing time histories themselves, as well as linear and nonlinear spectra. These capabilities are instrumental in supporting structural design initiatives. This study provides a comprehensive exposition of the latest version (v 5.05) of OpenSeismoMatlab. It delves into intricate facets of the software, encompassing a detailed exploration of the input and output variables integral to each operational category. Comprehensive calculation flowcharts are presented to elucidate the software’s organizational structure and operational sequences. Furthermore, a meticulous verification assessment is conducted to validate OpenSeismoMatlab’s performance. This verification entails a rigorous examination of specific cases drawn from existing literature, wherein the software’s outcomes are rigorously compared against corresponding results from prior studies. The examination not only underscores the reliability of OpenSeismoMatlab but also emphasizes its ability to generate outcomes that closely align with findings documented in the established body of literature. Concluding the study, the paper outlines potential directions for future research, shedding light on avenues where further development and exploration can enhance the utility and scope of OpenSeismoMatlab in advancing seismic engineering and structural design practices.

Keywords:
OpenSeismoMatlab; earthquake; seismic design; nonlinear spectra; pulse; resampling.

T.G. Wakjira, A. Abushanab, M. Shahria Alam, W. Alnahhal* and V. Plevris, "Explainable Machine Learning-Aided Efficient Prediction Model and Software Tool for Bond Strength of Concrete with Corroded Reinforcement", Structures, 59, Article ID 105693 (DOI: 10.1016/j.istruc.2023.105693), 2024.

Abstract:

The bond strength between concrete and reinforcement is crucial for the composite action and serviceability of reinforced concrete (RC) structures. However, it is vulnerable to deterioration from the corrosion of reinforcement bars, especially in marine structures. Thus, a precise and reliable model for the bond strength in corrosive environments is necessary to evaluate the serviceability and structural performance of corroded RC members. This study employs explainable machine learning (ML) techniques to assess the bond strength between concrete and corroded bars. Eight ML models are developed to establish the best predictive model for bond behavior, considering seven input parameters: corrosion level (CL), steel yield strength, compressive strength of concrete, concrete cover-to-bar diameter ratio, bar diameter-to-bonded length ratio, reinforcement type, and test type. The super learner (SL) model, integrating three ML models, outperforms other models and analytical methods with a large R² value (98% on the test set) and minimal statistical errors. The SHapley Additive exPlanation (SHAP) technique identifies CL as the most influential parameter on bond strength, while the reinforcement and test types have the least effect. Finally, a user-friendly graphical user interface (GUI) tool is established to facilitate the practical implementation of the developed model and support accurate bond strength prediction in concrete with steel reinforcement under corrosive environments.

Keywords:
Machine learning; Bond strength; Concrete; Corrosion; SHAP; Graphical user interface.

R. Ehtisham, W. Qayyum, C.V. Camp, V. Plevris*, J. Mir, Q.Z. Khan and A. Ahmad, "Computing the Characteristics of Defects in Wooden Structures using Image Processing and CNN", Automation in Construction, 158, Article ID 105211 (DOI: 10.1016/j.autcon.2023.105211), 2023.

Abstract:

Wood, a time-honored construction material prized for its exceptional properties, has been in use for millennia. Its enduring popularity is attributed to its remarkable strength, aesthetic appeal, and favorable environmental footprint. However, wooden structures are susceptible to various defects and imperfections that pose threats to their structural integrity, durability, and safety. These issues encompass knots, cracks, warping, twisting, decay, insect infestations, and more, all of which, if left unaddressed, can culminate in structural failures. Thus, a comprehensive strategy involving inspection, maintenance, and remediation is indispensable for safeguarding wooden structures. Traditional manual inspections, while effective, are characterized by their resource-intensive nature, entailing significant time and cost investments. This study presents a pioneering approach that leverages Convolutional Neural Networks (CNNs) and Image Processing techniques to revolutionize the assessment of damage in wooden structures using digital imagery. Initially, CNNs are employed to categorize images into three fundamental classes: cracks, knots, and undamaged sections. Subsequently, Image Processing techniques are harnessed to compute precise characteristics of these defects, including parameters such as crack length, width, angle, and the extent of the defective area within knots. The Inception-ResNet-V2 pre-trained model is utilized, fine-tuned and validated with a robust dataset comprising 9000 wooden defect images, evenly distributed across the three aforementioned categories. A prudent division allocates 70% of the dataset for model training, with the remaining 30% reserved for validation. Following successful training, the model demonstrates an impressive overall accuracy of 92% when classifying an independent test set comprising 100 new images. To illustrate the model's performance, two images from each damage category are selected and tested to compute the characteristics of the defects. The quantification error for crack angle is only 0.15%, while it is 0.99% for crack length, and 2% for crack width, demonstrating the high performance of the model. The practical implications of this work are profound. By automating defect assessment in wooden structures, our approach offers significant advantages to industry professionals. It expedites inspections, reduces labor costs, and enhances the accuracy of defect quantification.

Keywords:
Wooden structures; Characteristics of defects; CNN; Image processing.

V. Plevris, A. Jimenéz Rios* and G. Papazafeiropoulos, "Chatbots put to the test in math and logic problems: A comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard", AI, 4(4), Article ID 4040048 (DOI: 10.3390/ai4040048), 2023.

Abstract:
In an age where artificial intelligence is reshaping the landscape of education and problem solving, our study unveils the secrets behind three digital wizards, ChatGPT-3.5, ChatGPT-4, and Google Bard, as they engage in a thrilling showdown of mathematical and logical prowess. We assess the ability of the chatbots to understand the given problem, employ appropriate algorithms or methods to solve it, and generate coherent responses with correct answers. We conducted our study using a set of 30 questions. These questions were carefully crafted to be clear, unambiguous, and fully described using plain text only. Each question has a unique and well-defined correct answer. The questions were divided into two sets of 15: Set A consists of “Original” problems that cannot be found online, while Set B includes “Published” problems that are readily available online, often with their solutions. Each question was presented to each chatbot three times in May 2023. We recorded and analyzed their responses, highlighting their strengths and weaknesses. Our findings indicate that chatbots can provide accurate solutions for straightforward arithmetic, algebraic expressions, and basic logic puzzles, although they may not be consistently accurate in every attempt. However, for more complex mathematical problems or advanced logic tasks, the chatbots’ answers, although they appear convincing, may not be reliable. Furthermore, consistency is a concern as chatbots often provide conflicting answers when presented with the same question multiple times. To evaluate and compare the performance of the three chatbots, we conducted a quantitative analysis by scoring their final answers based on correctness. Our results show that ChatGPT-4 performs better than ChatGPT-3.5 in both sets of questions. Bard ranks third in the original questions of Set A, trailing behind the other two chatbots. However, Bard achieves the best performance, taking first place in the published questions of Set B. This is likely due to Bard’s direct access to the internet, unlike the ChatGPT chatbots, which, due to their designs, do not have external communication capabilities.

Keywords:
Chatbot; AI; logic; mathematics; ChatGPT; GPT-3.5; GPT-4; Google Bard.

R. Ehtisham, W. Qayyum, C.V. Camp, V. Plevris*, J. Mir, Q.Z. Khan and A. Ahmad, "Classification of defects in wooden structures using pre-trained models of convolutional neural network", Case Studies in Construction Materials, 19, Article ID e02530 (DOI: 10.1016/j.cscm.2023.e02530), 2023.

Abstract:
Wooden structures, over time, are challenged by different types of defects. Due to mechanical and weathering effects, these defects can occur in the form of cracks, live and dead knots, dampness, and others. Because of the risk of damage or complete failure, treatment of these defects is necessary, but doing so necessitates their proper identification and classification (categorization). Crack identification and categorization must be part of the inspection procedure for engineering structures in the built environment. Convolutional neural networks (CNNs), a sub-type of Deep Learning (DL), can automatically classify the images of wooden structures to identify such defects. In this study, ten pre-trained models of CNN, namely ResNet18, ResNet50, ResNet101, ShuffleNet, GoogLeNet, Inception-V3, MobileNet-V2, Xception, Inception-ResNet-V2, and NASNet-Mobile are evaluated for the tasks of classification and prediction of defects in wooden structures. Each pre-trained CNN model is additionally trained and validated on an image dataset of 9000 images, equally divided into three classes: cracks, knots, and intact (undamaged). A smaller dataset of 300 images is separately used for testing purposes. Statistical parameters such as accuracy, precision, recall, and F1-score are computed for each CNN model. The Inception-V3 model proved to be the best CNN model for classifying defects in wooden structures based on the model’s accuracy, processing time and overall performance.

Keywords:
Wooden defects; Defects Classification; Pre-trained Models; CNN.

A. de Macêdo Wahrhaftig, V. Plevris, B.A. Mohamad and D.L. Pereira, "Minimum design bending moment for systems of equivalent stiffness", Structures, 57, Article ID 105224 (DOI: 10.1016/j.istruc.2023.105224), 2023.

Abstract:
In this article, the minimum design bending moment of concrete slender columns is studied by assuming a system of equivalent stiffness. For concrete structural parts such as slender columns, their stiffness is dependent on the loading, originated from lumped and distributed masses, and the rheological behavior of the material. The latter alters the concrete’s modulus of elasticity, introducing changes over time. Basically, the desired transformation is from a one-dimensional non-prismatic system to another prismatic one which exhibits an equivalent bending stiffness. As the bending stiffness changes due to the change of the problem’s independent variables, the geometric characteristics of the transformed system reflect the same dependence as the original system. This implies changes in the minimum design moment since it is linked to the dimensions of the equivalent section. To assess the hypothesis proposed, a numerical simulation is conducted over a real structural system using a vertical loading ranging from zero up to the critical buckling force, taking into account the change in the modulus of deformation of concrete and assuming a certain level of cracking of the material. The results obtained showed that the strategy of using a system of equivalent stiffness simplifies the analysis of non-prismatic elements because the problem is reduced to a prismatic element of equivalent properties. Besides that, due to the incorporation of the concrete creep in the problem, the maximum moment obtained in the equivalent system needs to be multiplied by a factor of 2.94 in order to equal the maximum moment occurring in the original system.

Keywords:
Minimum design moment; Equivalent stiffness; Buckling; Rheological behavior; Reinforced concrete column; Rayleigh method.

L. Hadji*, V. Plevris and G. Papazafeiropoulos, “Investigation of the Static Bending Response of FGM Sandwich Plates”, Journal of Applied and Computational Mechanics, 10(1), pp. 26-37, 2024. DOI: 10.22055/jacm.2023.44278.4194

Abstract:
In the present work, a displacement-based high-order shear deformation theory is introduced for the static response of functionally graded plates. The present theory is variationally consistent and strongly similar to the classical plate theory in many aspects. It does not require the shear correction factor, and gives rise to the transverse shear stress variation so that the transverse shear stresses vary parabolically across the thickness to satisfy free surface conditions for the shear stress. By dividing the transverse displacement into the bending and shear parts and making further assumptions, the number of unknowns and equations of motion of the present theory is reduced a and hence makes them simple to use. The material properties of the plate are assumed to be graded in the thickness direction according to a simple power-law distribution in terms of volume fractions of material constituents. The equilibrium equations of a functionally graded plate are given based on the higher order shear deformation theory. The numerical results presented in the paper are demonstrated by comparing the results with solutions derived from other higher-order models found in the literature and the present numerical results of Finite Element Analysis (FEA). In the numerical results, the effects of the grading materials, lay-up scheme and aspect ratio on the normal stress, shear stress and static deflections of the functionally graded sandwich plates are presented and discussed. It can be concluded that the proposed theory is accurate, elegant and simple in solving the problem of the bending behavior of functionally graded plates.

Keywords:
Sandwich Plates, Functionally Graded Materials, Higher-Order Plate Theory, Stress, FEA.

G. Solorzano and V. Plevris, “An Open-Source Framework for Modeling RC Shear Walls Using Deep Neural Networks”, Advances in Civil Engineering, vol. 2023, Article ID 7953869, 17 pages (DOI: 10.1155/2023/7953869), 2023.

Abstract:
Reinforced concrete (RC) shear walls macroscopic models are simplified strategies able to simulate the complex nonlinear behavior of RC shear walls to some extent, but their efficacy and robustness are limited. In contrast, microscopic models are sophisticated finite element method (FEM) models that are far more accurate and reliable. However, their elevated computational cost turns them unfeasible for most practical applications. In this study, a data-driven surrogate model for analyzing RC shear walls is developed using deep neural networks (DNNs). The surrogate model is trained with thousands of FEM simulations to predict the characteristic curve obtained when a static nonlinear pushover analysis is performed. The surrogate model is extensively tested and found to exhibit a high degree of accuracy in its predictions while being extremely faster than the detailed FEM analysis. The complete framework that made this study possible is provided as an open-source project. The project is developed in Python and includes a parametric FEM model of an RC shear wall in OpenSeesPy, the training and validation of the DNN model in TensorFlow, and an application with an interactive graphical user interface to test the methodology and visualize the results.

Keywords:
Shear Wall, Surrogate Model, Deep Neural Network, Pushover Analysis, OpenSees, Open-Source.

M. Shabani, M. Kioumarsi and V. Plevris, “Performance-based seismic assessment of a historical masonry arch bridge: Effect of pulse-like excitations”, Frontiers of Structural and Civil Engineering (DOI: 10.1007/s11709-023-0972-z), 2023.

Abstract:
Seismic analysis of historical masonry bridges is important for authorities in all countries hosting such cultural heritage assets. The masonry arch bridge investigated in this study was built during the Roman period and is on the island of Rhodes, in Greece. Fifteen seismic records were considered and categorized as far-field, pulse-like near-field, and non-pulse-like near-field. The earthquake excitations were scaled to a target spectrum, and nonlinear time-history analyses were performed in the transverse direction. The performance levels were introduced based on the pushover curve, and the post-earthquake damage state of the bridge was examined. According to the results, pulse-like near-field events are more damaging than non-pulse-like near-field ground motions. Additionally the bridge is more vulnerable to far-field excitations than near-field events. Furthermore, the structure will suffer extensive post-earthquake damage and must be retrofitted.

Keywords:
Masonry arch bridges, seismic behavior, modal properties, pulse-like records, nonlinear time history analysis.

M. Georgioudakis* and V. Plevris, "Response Spectrum Analysis of Multi-Story Shear Buildings Using Machine Learning Techniques", Computation, 11(7), Article ID 11070126, 22 pages (DOI: 10.3390/computation11070126), 2023.

Abstract:
The dynamic analysis of structures is a computationally intensive procedure that must be considered, in order to make accurate seismic performance assessments in civil and structural engineering applications. To avoid these computationally demanding tasks, simplified methods are often used by engineers in practice, to estimate the behavior of complex structures under dynamic loading. This paper presents an assessment of several machine learning (ML) algorithms, with different characteristics, that aim to predict the dynamic analysis response of multi-story buildings. Large datasets of dynamic response analyses results were generated through standard sampling methods and conventional response spectrum modal analysis procedures. In an effort to obtain the best algorithm performance, an extensive hyper-parameter search was elaborated, followed by the corresponding feature importance. The ML model which exhibited the best performance was deployed in a web application, with the aim of providing predictions of the dynamic responses of multi-story buildings, according to their characteristics.

Keywords:
Response spectrum analysis, ensemble algorithms, machine learning, shear building, SHAP explainability.

A. Jiménez Rios, S. Ruiz-Capel, V. Plevris and M. Nogal, “Computational Methods Applied to Earthen Historical Structures”, Frontiers in Built Environment, 9:1219108 (DOI: 10.3389/fbuil.2023.1219108), 2023.

Abstract:
Earthen structures have an important representation among the UNESCO World Heritage List sites as well as among the built environment in general. Unfortunately, earthen heritage structures are also numerous within the UNESCO List of World Heritage in Danger whereas other existing common earthen structures are extremely vulnerable to seismic and climate change events. Within the field of heritage conservation, structural analysis contributes to the safety evaluation of the structure, the diagnosis of the causes of damage and decay, and to the validation of interventions. Thus, the need to develop effective and accurate computational methods suitable for the study of both monumental and vernacular earthen structures becomes evident. This paper compiles, summarizes, and highlights the latest developments and implementations of computational methods for the study of such structure typologies. The literature has been explored following the PRISMA-S checklist methodology and a narrative synthesis was used for the presentation of results. Finally, future trends, opportunities, and challenges are discussed.

Keywords:
Adobe, rammed earth, cob, finite element method, discrete element method, limit analysis.

M. Nikoo, G. Hafeez, G. Doudak and V. Plevris, "Predicting the Fundamental Period of Light-Frame Wooden Buildings by Employing Bat Algorithm-Based Artificial Neural Network", in Artificial Intelligence and Machine Learning Techniques for Civil Engineering, V. Plevris, A. Ahmad and N.D. Lagaros (Eds.), IGI Global, pp. 139-162, 2023.

Abstract:
The study utilizes an artificial neural network model for determining the fundamental period of Light-Frame Wooden Buildings, employing the Bat algorithm on a data set of 71 measured periods of wooden buildings. The number of stories, floor area, storey height, maximum length, and maximum width are selected as input parameters to estimate the fundamental period of light-frame wooden buildings. The accuracy and the competitiveness of the developed model were evaluated by comparing it with a similar particle swarm optimization (PSO)- ANN scheme, the formulas provided in the National Building Code of Canada, an equation obtained from the Eureqa software, and a non-linear regression (NLR) model. The results of the research show that the bat-ANN model exhibited the best overall performance with the lowest RMSE and MAE error values and the highest values of the Coefficient of determination, R², in comparison to the other examined models. Therefore, the proposed Bat-ANN model can be considered as a reliable, robust, and accurate tool for predicting the fundamental period of wooden buildings.

Book: "Artificial Intelligence and Machine Learning Techniques for Civil Engineering", Eds: V. Plevris, A. Ahmad, N.D. Lagaros, IGI Global, 2023. DOI: 10.4018/978-1-6684-5643-9

Description

In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering.

Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.

G. Nekouei, A. Vatani Oskouei, S. Gharehbaghi and V. Plevris*, "Seismic response modification, over-strength, and displacement amplification factors of steel special truss moment frames", Asian Journal of Civil Engineering (DOI: 10.1007/s42107-023-00661-x), 2023.

Abstract:
In this paper, the seismic design parameters of steel special truss moment frames (STMFs), including the response modification factor (R), over-strength factor (Ω) and displacement modification factor (C_d) are evaluated for two performance levels, namely life safety (LS) and collapse prevention (CP). The effects of geometrical dimensions of the special segment located at the middle part of truss girder and the number of stories are investigated. Twelve steel STMFs with 3, 5, 7, and 9 stories with three bays are considered to evaluate the parameters. In addition, three special segment lengths are considered for each STMF. The truss-girder members are made of hollow structural sections (HSSs) and W-sections are used for columns, which are designed based on ASCE7-16 recommendations. The results show that the number of stories and the different special segment lengths affect the seismic parameters significantly. Moreover, the values obtained for R, Ω and C_d show some differences compared to the ones recommended by ASCE7-16.

Keywords:
Special truss moment frames, Behavior factor, Over-strength factor, Displacement modification factor, Hollow steel section

G. Solorzano* and V. Plevris, "DNN-MLVEM: A Data-Driven Macromodel for RC Shear Walls Based on Deep Neural Networks", Mathematics, 11(10), Article ID 2347, 19 pages (DOI: 10.3390/math11102347), 2023.

Abstract:
This study proposes the DNN-MVLEM, a novel macromodel for the non-linear analysis of RC shear walls based on deep neural networks (DNN); while most RC shear wall macromodeling techniques follow a deterministic approach to find the right configuration and properties of the system, in this study, an alternative data-driven strategy is proposed instead. The proposed DNNMVLEM is composed of four vertical beam-column elements and one horizontal shear spring. The beam-column elements implement the fiber section formulation with standard non-linear uniaxial material models for concrete and steel, while the horizontal shear spring uses a multi-linear force–displacement relationship. Additionally, three calibration factors are introduced to improve the performance of the macromodel. The data-driven component of the proposed strategy consists of a large DNN that is trained to predict the force–displacement curve of the shear spring and the three calibration factors. The training data is created using a parametric microscopic FEM model based on the multi-layer shell element formulation and a genetic algorithm (GA) that optimizes the response of the macromodel to match the behavior of the microscopic FEM model. The DNN-MVLEM is tested in two types of examples, first as a stand-alone model and then as part of a two-bay multi-story frame structure. The results show that the DNN-MVLEM is capable of reproducing the results obtained with the microscopic FEM model up to 100 times faster and with an estimated error lower than 5%.

Keywords:
shear wall, macromodel, deep neural network, genetic algorithm, OpenSees.

G. Papazafeiropoulos and V. Plevris, "Kahramanmaraş—Gaziantep, Türkiye Mw 7.8 Earthquake on 6 February 2023: Strong Ground Motion and Building Response Estimations", Buildings, 13(5), Article ID 1194, 30 pages (DOI: 10.3390/buildings13051194), 2023.

Abstract:
The effects on structures of the earthquake with the magnitude of 7.8 on the Richter scale (moment magnitude scale) that took place in Pazarcık, Kahramanmaraş, Türkiye at 04:17 a.m. local time (01:17 UTC) on 6 February 2023, are investigated by processing suitable seismic records using the open-source software OpenSeismoMatlab. The earthquake had a maximum Mercalli intensity of XI (Extreme) and it was followed by a Mw 7.5 earthquake nine hours later, centered 95 km to the north–northeast from the first. Peak and cumulative seismic measures as well as elastic response spectra, constant ductility (or isoductile) response spectra, and incremental dynamic analysis curves were calculated for two representative earthquake records of the main event. Furthermore, the acceleration response spectra of a large set of records were compared to the acceleration design spectrum of the Turkish seismic code. Based on the study, it is concluded that the structures were overloaded far beyond their normal design levels. This, in combination with considerable vertical seismic components, was a contributing factor towards the collapse of many buildings in the region. Modifications of the Turkish seismic code are required so that higher spectral acceleration values can be prescribed, especially in earthquake-prone regions.

Keywords:
earthquake; Türkiye; design; collapse; ductility; reinforcement; concrete.

A. Jimenez Rios, V. Plevris and M. Nogal, “Bridge management through digital twin-based anomaly detection systems: A systematic review”, Frontiers in Built Environment, 9:1176621 (DOI: 10.3389/fbuil.2023.1176621), 2023.

Abstract:
Bridge infrastructure has great economic, social, and cultural value. Nevertheless, many of the infrastructural assets are in poor conservation condition as has been recently evidenced by the collapse of several bridges worldwide. The objective of this systematic review is to collect and synthesize state-of-the-art knowledge and information about how bridge information modeling, finite element modeling, and bridge health monitoring are combined and used in the creation of digital twins (DT) of bridges, and how these models could generate damage scenarios to be used by anomaly detection algorithms for damage detection on bridges, especially in bridges with cultural heritage value. A total of 76 relevant studies from 2017 up to 2022 have been taken into account in this review. The synthesis results show a consensus toward the future adoption of DT for bridge design, management, and operation among the scientific community and bridge practitioners. The main gaps identified are related to the lack of software interoperability, the required improvement of the performance of anomaly-detection algorithms, and the approach definition to be adopted for the integration of DT at the macro scale. Other potential developments are related to the implementation of Industry 5.0 concepts and ideas within DT frameworks.

Keywords:
Bridges, digital twins, anomaly detection algorithms, finite element method, cultural heritage conservation, bridge information modeling, bridge health monitoring.

M.E.A. Ben Seghier, V. Plevris and Α. Malekjafarian, “Development of Hybrid Adaptive Neural Fuzzy Inference System-Based Evolutionary Algorithms for Flexural Capacity Prediction in Corroded Steel Reinforced Concrete Beam”, Arabian Journal for Science and Engineering (DOI: 10.1007/s13369-023-07708-w), 2023.

Abstract:
The damages in reinforced concrete (RC) beams due to reinforcement corrosion is a major problem in the RC industry. Accurate prediction of the residual bearing capacity of RC beams can effectively prevent structural failures or unwanted over-costs of inspections and rehabilitations. This paper proposes a novel machine learning-based prediction framework that combines the adaptive neural fuzzy inference system (ANFIS) with several metaheuristic algorithms for the effective estimation of the flexural strength capacity. Five optimization algorithms are employed for auto-selection of the optimum ANFIS parameters, including differential evolution (DE), genetic algorithm, particle swarm optimization, artificial bee colony, and firefly algorithm (FFA). A comprehensive experimental database of the flexural capacity of corroded steel reinforced concrete beams obtained from the literature, consisting of 177 tests, is used as a case study to evaluate the prediction performance of the proposed hybrid models. The results demonstrate that the proposed hybrid models transcend the previously developed models, while the optimized ANFIS using FFA represents the highest accuracy and strong stability among the proposed models. It is concluded that the proposed framework using ANFIS-FFA can be effectively employed as a useful tool for the accurate estimation of the flexural strength capacity of corroded reinforced concrete beams.

Keywords:
Flexural strength capacity; Prediction; Machine learning; Adaptive neural fuzzy inference system; Nature-inspired algorithms; Firefly algorithm.

S.V.R. Tosee, I. Faridmehr, M.L. Nehdi, V. Plevris and K.A. Valerievich, "Predicting Crack Width in CFRP Strengthened RC One-Way Slabs Using Hybrid Grey Wolves Optimizer Neural Network Model", Buildings, 12(11), Article ID 1870, 26 pages (DOI: 10.3390/buildings12111870), 2022.

Abstract:
This study deploys a hybrid Grey Wolf Optimizer Neural Network Model for predicting the crack width in reinforced concrete slabs strengthened with carbon fiber-reinforced polymers (CFRP). Reinforced concrete (RC) one-way slabs (1800 × 400 × 120 mm in size) were strengthened with CFRP with various lengths (1800, 1100, and 700 mm) and subjected to four-point bending. The experimental results were compared to corresponding values for conventional RC slabs. The observed crack width results were recorded, and subsequently examined against the expression recommended by Eurocode 2. To estimate the crack width of CFRP-reinforced slabs, ANN combined with the Grey Wolf Optimizer algorithm was employed whereby the applied load, CFRP width/length, X/Y crack positions, and stress in steel reinforcement and concrete were defined as the input parameters. Experimental results showed that the larger the length and width of the carbon fiber, the smaller the maximum crack width in the tensile area of the slab at the final load step. On average, the crack width in slabs retrofitted with CFRP laminates increased by around 80% compared to a slab without CFRP. The results confirm that the equation provided by Eurocode 2 provides an unconservative estimation of crack widths for RC slabs strengthened with CFRP laminates. On the other hand, the results also confirm that the proposed informational model could be used as a reliable tool for estimating the crack width in RC slabs. The findings provide valuable insight into the design approaches for RC slabs and rehabilitation strategies for existing deficient RC slabs using CFRP.

Keywords:
crack width; CFRP; artificial intelligence; neural networks; concrete slab.

G. Solorzano* and V. Plevris, “Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps”, Frontiers in Built Environment, 8:1049616, 2022. DOI: 10.3389/fbuil.2022.1049616

Abstract:
The modeling and simulation of structural systems is a task that requires high precision and reliable results to ensure the stability and safety of construction projects of all kinds. For many years now, structural engineers have relied on hard computing strategies for solving engineering problems, such as the application of the Finite Element Method (FEM) for structural analysis. However, despite the great success of FEM, as the complexity and difficulty of modern constructions increases, the numerical procedures required for their appropriated design become much harder to process using traditional methods. Therefore, other alternatives such as Computational Intelligence (CI) techniques are gaining substantial popularity among professionals and researchers in the field. In this study, a data-driven bibliometric analysis is presented with the aim to investigate the current research directions and the applications of CI-based methodologies for the simulation and modeling of structures. The presented study is centered on a self-mined database of nearly 8000 publications from 1990 to 2022 with topics related to the aforementioned field. The database is processed to create various two-dimensional bibliometric maps and analyze the relevant research metrics. From the maps, some of the trending topics and research gaps are identified based on an analysis of the keywords. Similarly, the most contributing authors and their collaborations are assessed through an analysis of the corresponding citations. Finally, based on the discovered research directions, various recent publications are selected from the literature and discussed in detail to set examples of innovative CI-based applications for the modeling and simulation of structures. The full methodology that is used to obtain the data and generate the bibliometric maps is presented in detail as a means to provide a clearer interpretation of the bibliometric analysis results.

Keywords:
computational intelligence, structural analysis, soft computing, finite element method, structural engineering, bibliometric analysis, bibliometric maps.

D. Koutsantonis, K. Koutsantonis, N.P. Bakas*, V. Plevris, A. Langousis and S.A. Chatzichristofis, "Bibliometric Literature Review of Adaptive Learning Systems", Sustainability, 14(19) (DOI: 10.3390/su141912684), 2022.

Abstract:
In this review paper, we computationally analyze a vast volume of published articles in the field of Adaptive Learning, as obtained by the Scopus Database. Particularly, we use a query with search terms targeting the area of Adaptive Learning Systems by utilizing a combination of specific keywords. Accordingly, we apply a multidimensional scaling algorithm to construct bibliometric maps for keywords, authors, and references. Subsequently, we present the computational results for the studied dataset, reveal significant patterns appearing in the field of adaptive learning and the inter-item associations, and interpret the findings based on the current state-of-the-art literature in the area. Furthermore, we demonstrate the time-series of the evolution of the research terms, their trends over time, as well as their prevalent statistical associations.

Keywords:
Adaptive learning, intelligent tutoring systems, personalized learning, machine learning, bibliometrics.

Book: "Artificial Intelligence (AI) Applied in Civil Engineering", Eds: N.D. Lagaros and V. Plevris, MDPI, Basel, Switzerland, 698 pages, 2022.

Description

In recent years, the application of artificial intelligence (AI) in several scientific fields, varying from big data handling to medical diagnosis, has drawn significant attention. The use of AI is already present in our daily lives, as exemplified by personalized ads, virtual assistants, autonomous driving, etc. Not surprisingly, AI methodologies have demonstrated impressive results through a wide range of uses and applications in engineering fields, including civil and structural engineering. The increase in AI studies shows that the use of AI in civil engineering is gaining momentum and will keep increasing in the coming years, bringing new innovations and applications. This book collection contains applications and recent advances of AI with regard to civil engineering problems, promoting cross-fertilization between these scientific fields. In particular, the focus is on hybrid studies and applications related to structural engineering, transportation engineering, geotechnical engineering, hydraulic engineering, environmental engineering, coastal and ocean engineering, structural health monitoring, and construction management. The book contains 35 contributions in total from 19 different countries around the world, covering a broad range of topics related to the applications of AI in civil engineering.

M.E.A. Ben Seghier, V. Plevris* and G. Solorzano, "Random forest-based algorithms for accurate evaluation of ultimate bending capacity of steel tubes", Structures, 44, pp. 261-273 (DOI: 10.1016/j.istruc.2022.08.007), 2022.

Abstract:
Despite the existence of methods for estimating the behavior of steel circular tubes subjected to pure bending, analytical models are still restricted due to the problem’s complexity and significant nonlinearity. Using the random forest (RF) as the basic model, novel intelligent models are constructed to estimate the ultimate pure bending capacity of circular steel tubes in this study. The RF model’s parameters are optimized using three nature inspired optimization algorithms, namely, the particle swarm optimization (PSO), ant colony optimization (ACO) and whale optimization algorithm (WOA). In the experimental part, a database of 104 tests that comprise 49 and 55 pure bending tests conducted on fabricated and cold-formed steel circular tubes, respectively, are evaluated and utilized to investigate the applicability of the hybrid RF-models. A single RF model is also built for comparative reasons in order to estimate the ultimate pending capacity. Various statistical and graphical measures are used to evaluate the performance of the developed models. The results show that the proposed RF-based nature-inspired algorithms can outperform the original RF predictive model. When the hybrid-RF models were assessed, it was discovered that the RF-WOA performed best. In addition, the influence of each parameter on the prediction findings based on the best RF-model is investigated via sensitivity analysis. Taking into account the overall findings, the hybrid RF-models may be used as powerful tools to predict the ultimate bending capacity of circular steel tubes and may be viable to aid technicians in making proper judgments.

Keywords:
Ultimate bending capacity, Circular steel tubes, Prediction, Random forest, Whale optimization algorithm, Performance index.

N.D. Lagaros and V. Plevris*, "Artificial Intelligence (AI) Applied in Civil Engineering", Applied Sciences, 12(15), pp 1-7 (DOI: 10.3390/app12157595), 2022.

Abstract:
In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. The use of AI is already present in our daily lives with several uses, such as personalized ads, virtual assistants, autonomous driving, etc. Not surprisingly, AI methodologies have found a wide range of uses and applications in engineering fields, including civil and structural engineering, with impressive results. The increase in AI studies with great acceleration shows that the use of AI in civil engineering is gaining momentum and will keep increasing in the coming years, bringing new innovations and applications. This research topic contains applications and recent advances of AI in civil engineering problems, promoting cross-fertilization between these scientific fields. In particular, the focus is on hybrid studies and applications related to structural engineering, transportation engineering, geotechnical engineering, hydraulic engineering, environmental engineering, coastal and ocean engineering, structural health monitoring, as well as construction management.

N.D. Lagaros, V. Plevris and N.A. Kallioras, "The mosaic of metaheuristic algorithms in structural optimization", Archives of Computational Methods in Engineering (State of the art reviews), 2022.

Abstract:
Metaheuristic optimization algorithms (MOAs) represent powerful tools for dealing with multi-modal nonlinear optimization problems. The considerable attention that MOAs have received over the last decade and especially when adopted for dealing with several types of structural optimization problems can be mainly credited to the advances achieved in computer science and computer technology rendering possible, among others, the solution of real-world structural design optimization cases in reasonable computational time. The primal scope of the study is to present a state-of-the-art review of past and current developments achieved so far in structural optimization problems dealt with MOAs, accompanied by a set of tests aiming to examine the efficiency of various MOAs in several benchmark structural optimization problems. For this purpose, 24 population-based state-of-the-art MOAs belonging in four classes, (i) swarm-based; (ii) physics-based; (iii) evolutionary-based; and (iv) human-based, are used for solving 11 single objective benchmark structural optimization test problems of different levels of complexity. The size of the problems employed varies, with the number of unknowns ranging from 3 to 328 and the number of constraint functions ranging from 2 to 264, related to the structural performance of the design with reference to deformation and stress limits.

V. Plevris and G. Solorzano*, “A Collection of 30 Multidimensional Functions for Global Optimization Benchmarking”, Data, 7(4), Article ID 46, 52 pages, 2022. DOI: 10.3390/data7040046

Abstract:
A collection of thirty mathematical functions that can be used for optimization purposes is presented and investigated in detail. The functions are defined in multiple dimensions, for any number of dimensions, and can be used as benchmark functions for unconstrained multidimensional single-objective optimization problems. The functions feature a wide variability in terms of complexity. We investigate the performance of three optimization algorithms on the functions: two metaheuristic algorithms, namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and one mathematical algorithm, Sequential Quadratic Programming (SQP). All implementations are done in MATLAB, with full source code availability. The focus of the study is both on the objective functions, the optimization algorithms used, and their suitability for solving each problem. We use the three optimization methods to investigate the difficulty and complexity of each problem and to determine whether the problem is better suited for a metaheuristic approach or for a mathematical method, which is based on gradients. We also investigate how increasing the dimensionality affects the difficulty of each problem and the performance of the optimizers. There are functions that are extremely difficult to optimize efficiently, especially for higher dimensions. Such examples are the last two new objective functions, F29 and F30, which are very hard to optimize, although the optimum point is clearly visible, at least in the two-dimensional case.

Keywords:
optimization; unconstrained; benchmark functions; objective function; GA; PSO; SQP.

V. Plevris*, N. D. Lagaros and A. Zeytinci, “Blockchain in Civil Engineering, Architecture and Construction Industry: State of the Art, Evolution, Challenges and Opportunities”, Frontiers in Built Environment, 8:840303, 2022. DOI: 10.3389/fbuil.2022.840303

Abstract:
Blockchain is a technology that allows the recording of information in a way that it is difficult or practically impossible to alter, hack, or cheat. It is a new, promising technology, considered by many as a general-purpose technology (GPT). GPTs are technologies that have the potential to affect an entire economy, impacting economic growth and transforming both everyday life and the ways in which we conduct business. We present a bibliometric analysis of the relevant literature, followed by a discussion about monetary mediums and the evolution of bitcoin, as the first digital medium managing to solve the “double-spending” problem and the first successful implementation of blockchain technology. The computational operations involved in blockchain are presented, together with the cryptographic technologies associated with it, its unique characteristics, and the advantages it offers as a technology. A comprehensive literature review is provided, of the current state of the art in blockchain in the fields of civil engineering, architecture and the construction industry. Six important application areas are identified, and the relevant literature is investigated. Namely, building information modelling and computer aided design, contract management and smart contracts, construction project management, smart buildings and smart cities, construction supply chain management, and real estate. Finally, we discuss the future applications, the challenges and the opportunities that blockchain technology brings to these fields.

Keywords:
blockchain, general purpose technology (GPT), distributed ledger, civil engineering, architecture, construction, engineering.

I. Faridmehr, M. Shariq, V. Plevris*, and N. Aalimahmoody, “Novel Hybrid Informational Model for Predicting the Creep and Shrinkage Deflection of Reinforced Concrete Beams Containing GGBFS”, Neural Computing and Applications, 2022. DOI: 10.1007/s00521-022-07150-3

Abstract:
This study investigates a Novel Hybrid Informational model for the prediction of creep and shrinkage deflection of reinforced concrete (RC) beams containing different percentages of ground granulated blast furnace slag (GGBFS) at different ages, varying from 1 to 150 days. The percentage of cement replacement by GGBFS varies from 20 to 60%. In order to examine the effects of the applied load and tensile reinforcement on creep behavior, the magnitude of two-point loading was varied from 200 kg to a maximum of 350 kg while the percentage of tensile reinforcement (ρ) was selected as either 0.77% or 1.2%. The current situation about short-term and long-term deflections due to creep and shrinkage available in the international standards, including ACI, BS and Eurocode 2, is discussed. The results indicate that RC beams containing GGBFS have larger deflections than the ones with conventional concrete (i.e., ordinary Portland cement concrete). After 150 days, the average creep deflection of RC beams containing 20, 40, and 60% GGBFS was 30, 70, and 100% higher than the ones for conventional concrete beams, respectively. A hybrid artificial neural network coupled with a metaheuristic Whale optimization algorithm has been developed to estimate the overall deflection of concrete beams due to creep and shrinkage. Several statistical metrics, including the root mean square error and the coefficient of variation, revealed that the generalized model achieved the most reliable and accurate prediction of the concrete beam’s deflection in comparison with international standards and other models. This novel informational model can simplify the design processes in computational intelligence structural design platforms in future.

Keywords:
GGBFS; Creep and shrinkage deflection; Neural networks; Whale optimization algorithm.

A. Shabani*, M. Skamantzari, S. Tapinaki, A. Georgopoulos, V. Plevris and M. Kioumarsi, “3D simulation models for developing digital twins of heritage structures: challenges and strategies”, Procedia Structural Integrity, 37(2022), pp. 314-320, 2022. DOI: 10.1016/j.prostr.2022.01.090

Abstract:
Structural vulnerability assessment of heritage structures is a pivotal part of a risk mitigation strategy for preserving these valuable assets for the nations. For this purpose, developing digital twins has gained much attention lately to provide an accurate digital model for performing finite element (FE) analyses. Three-dimensional (3D) geometric documentation is the first step in developing the digital twin, and various equipment and methodologies have been developed to facilitate the procedure. Both aerial and terrestrial close-range photogrammetry can be combined with 3D laser scanning and geodetic methods for the accurate 3D geometric documentation. The data processing procedure in these cases mostly focuses on developing detailed, accurate 3D models that can be used for the FE modeling. The final 3D surface or volumes are produced mainly by combining the 3D point clouds obtained from the laser scanner and the photogrammetric methods. 3D FE models can be developed based on the geometries derived from the 3D models using FE software packages. As an alternative, developed 3D volumes provided in the previous step can be directly imported to some FE software packages. In this study, the challenges and strategies of each step are investigated by providing examples of surveyed heritage structures.

Keywords:
3D geometric documentation; cultural heritage; digital twins; 3D laser scanner; photogrammetry; finite element model

M.I. Waris, V. Plevris*, J. Mir, N. Chairman and A. Ahmad, “An Alternative Approach for Measuring the Mechanical Properties of Hybrid Concrete through Image Processing and Machine Learning”, Construction and Building Materials, 328(126899), 2022. DOI: 10.1016/j.conbuildmat.2022.126899

Abstract:
Image processing (IP), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are innovative techniques in computer science that have been widely used to predict the properties of materials in structural engineering. The stability of reinforced concrete structures mainly depends on the mechanical properties of concrete, i.e., its compressive strength fc and tensile strength ft. Different kinds of inexpensive cement replacement materials (CRM) can be used to form hybrid concrete (HC) with enhanced mechanical and other properties. In this study, the IP, ANN, and ANFIS methods are properly combined and used to predict the mechanical properties of hybrid concrete. For this, 162 cylindrical specimens of HC with 0%, 15%, and 25% silica fume and fly ash as replacement material with three mix ratios, 1:3:6, 1:2:4, and 1:1.5:3 were cast at 14 days and 28 days curing. The specimens were divided into three equal sets as follows: (i) the first to find the compressive strength, (ii) the second to find the split cylinder strength, and (iii) the third to develop a database of images. For the image acquisition, each cylinder of the third set is cut into three slices using a stone cutting saw, resulting in six faces and a total of 324 images (6 × 54). Photos (digital images) are then taken in fully controlled lighting conditions from a height of 600 mm between the concrete surface and the camera lens. The acquired images are pre-processed (converted to grayscale, cropped, and resized to 256 × 256 pixels), and the statistical features are extracted to predict the fc and ft by using ANN and ANFIS techniques. Finally, the predicted values are tested and validated through nondestructive testing methods. The actual values of the compressive and the tensile strength of concrete were compared to the corresponding values estimated by the proposed methods, i.e. (IP, ANN) and (IP, ANFIS). The accuracy of the results largely depends on the data set. The accuracy obtained by IP/ANN is 99.7% while the one obtained with IP/ANFIS is 97.8%.

Keywords:
Artificial neural network; Adaptive neuro-fuzzy inference system; Image processing; Hybrid concrete; Silica fumeFly ash.

X. Lu*, V. Plevris, G. Tsiatas, D. De Domenico, “Editorial: Artificial Intelligence-powered Methodologies and Applications in Earthquake and Structural Engineering”, Frontiers in Built Environment, 8:876077, 2022. DOI: 10.3389/fbuil.2022.876077

Abstract:
Earthquake disasters have caused enormous casualties and economic losses so far, threatening the social and economic development of humanity. At present, artificial intelligence (AI) is one of the frontiers and central issues in both academic research and engineering practice. AI refers to the branch of computer science that develops machines and software with human-like intelligence. In recent years, AI techniques are developing rapidly and have been widely adopted in several engineering disciplines. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have recently acquired considerable attention and are establishing themselves as a new class of powerful intelligent methods for use in the earthquake and structural engineering with proven effectiveness, as shown in recent studies. In the future, with the improvement of computational power and data accumulation, the feasibility and necessity of AI-driven technologies are expected to grow quickly. The Research Topic “Artificial Intelligence-Powered Methodologies and Applications in Earthquake and Structural Engineering” was proposed to collect cutting-edge research works combining AI with various scientific fields, such as seismic ground motion studies, structural and city-scale seismic risk, computational methods in structural engineering, structural system identification and damage detection, structural control under seismic action, structural health monitoring, among others.

Keywords:
artificial intelligence, seismic risk, damage assessment, system identification, structural dynamics and control, structural health monitoring, damage detection.

M.E.A. Ben Seghier, J.A.F.O. Corriea, J. Jafari-Asl, A. Malekjafarian, V. Plevris and N-T. Trung, "On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm", Neural Computing and Applications (DOI:10.1007/s00521-021-06199-w), 2021.

Abstract:
Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to ensure their safety and reliability. However, the main cables of suspension bridges inevitably deteriorate over time due to corrosion, as a result of their operational and environmental conditions. Thus, accurate annual corrosion rate predictions are crucial for maintaining reliable structures and optimal maintenance operations. However, the corrosion rate is a chaotic and complex phenomenon with highly nonlinear behavior. This paper proposes a novel predictive model for the estimation of the annual corrosion rate in the main cables of suspension bridges. This is a hybrid model based on the multilayer perceptron (MLP) technique optimized using marine predators algorithm (MPA). In addition, well-known metaheuristic approaches such as the genetic algorithm (GA) and particle swarm algorithm (PSO) are employed to optimize the MLP. In order to implement the proposed model, a comprehensive database composed of 309 sample tests on the annual corrosion rate from all around the world, including various factors related to the surrounding environmental properties, is utilized. In addition, several input combinations are proposed for investigating the trigger factors in modeling the annual corrosion rate. The performance of the proposed models is evaluated using various statistical and graphical criteria. The results of this study demonstrate that the proposed hybrid MLP-MPA model provides stable and accurate predictions, while it transcends the previously developed approaches for solving this problem. The effectiveness of the MLP-MPA model shows that it can be used for further studies on the reliability analysis of the main cables of suspension bridges.

Keywords:
Suspension bridges; Main cables; Annual corrosion rate; Artificial intelligence; Multilayer perceptron; Marine predators algorithm.

S. Gharehbaghi, M. Gandomi, V. Plevris and A.H. Gandomi*, "Prediction of seismic damage spectra using computational intelligence methods", Computers & Structures, 253, Article ID 106584 (DOI: 10.1016/j.compstruc.2021.106584), 2021.

Abstract:
Predicting seismic damage spectra, capturing both structural and earthquake features, is useful in performance-based seismic design and quantifying the potential seismic damage of structures. The objective of this paper is to accurately predict the seismic damage spectra using computational intelligence methods. For this purpose, an inelastic single-degree-of-freedom system subjected to a set of earthquake ground motion records is used to compute the (exact) spectral damage. The Park-Ang damage index is used to quantify the seismic damage. Both structural and earthquake features are involved in the prediction models where multi-gene genetic programming (MGGP) and artificial neural networks (ANNs) are applied. Common performance metrics were used to assess the models developed for seismic damage spectra, and indicated that their accuracy was higher than a corresponding model in the literature. Although the performance metrics revealed that the ANN model is more accurate than the MGGP model, the explicit MGGP-based mathematical model renders it more practical in quantifying the potential seismic damage of structures.

Keywords:
Computational intelligence; Genetic programming; Artificial neural networks; Regression analysis; Seismic damage spectra; Inelastic SDOF systems; Park-Ang damage index; Resiliency.

V. Plevris*, N.P. Bakas and G. Solorzano, "Pure Random Orthogonal Search (PROS): A Plain and Elegant Parameterless Algorithm for Global Optimization", Applied Sciences, 11(11), pp 1-28 (DOI: 10.3390/app11115053), 2021.

Abstract:
A new, fast, elegant, and simple stochastic optimization search method is proposed, which exhibits surprisingly good performance and robustness considering its simplicity. We name the algorithm pure random orthogonal search (PROS). The method does not use any assumptions, does not have any parameters to adjust, and uses basic calculations to evolve a single candidate solution. The idea is that a single decision variable is randomly changed at every iteration and the candidate solution is updated only when an improvement is observed; therefore, moving orthogonally towards the optimal solution. Due to its simplicity, PROS can be easily implemented with basic programming skills and any non-expert in optimization can use it to solve problems and start exploring the fascinating optimization world. In the present work, PROS is explained in detail and is used to optimize 12 multi-dimensional test functions with various levels of complexity. The performance is compared with the pure random search strategy and other three well-established algorithms: genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). The results indicate that, despite its simplicity, the proposed PROS method exhibits very good performance with fast convergence rates and quick execution time. The method can serve as a simple alternative to established and more complex optimizers. Additionally, it could also be used as a benchmark for other metaheuristic optimization algorithms as one of the simplest, yet powerful, optimizers. The algorithm is provided with its full source code in MATLAB for anybody interested to use, test or explore.

Keywords:
optimization; no free lunch; Occam’s razor; orthogonal search; search problems; PROS.

N. P. Bakas*, V. Plevris, A. Langousis and A. Chatzichristofis, "ITSO: a novel inverse transform sampling-based optimization algorithm for stochastic search", Stochastic Environmental Research and Risk Assessment (DOI: 10.1007/s00477-021-02025-w), 2021.

Abstract:
Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods and Engineering and Business applications. Following recent works on AI’s theoretical deficiencies, a rigour context for the optimization problem of a black-box objective function is developed. The algorithm stems directly from the theory of probability, instead of presumed inspiration. Thus the convergence properties of the proposed methodology are inherently stable. In particular, the proposed optimizer utilizes an algorithmic implementation of the n-dimensional inverse transform sampling as a search strategy. No control parameters are required to be tuned, and the trade-off among exploration and exploitation is, by definition, satisfied. A theoretical proof is provided, concluding that when falling into the proposed framework, either directly or incidentally, any optimization algorithm converges. The numerical experiments verify the theoretical results on the efficacy of the algorithm apropos reaching the sought optimum.

J. Jafari-Asl, M.E.A. Ben Seghier*, S. Ohadi, Y. Dong and V. Plevris, "A Comparative Study on the Efficiency of Reliability Methods for the Probabilistic Analysis of Local Scour at a Bridge Pier in Clay-Sand-Mixed Sediments", Modelling, 2(1), pp 63-77 (DOI: 10.3390/modelling2010004), 2021.

Abstract:
In this work, the performance of reliability methods for the probabilistic analysis of local scour at a bridge pier is investigated. The reliability of bridge pier scour is one of the important issues for the risk assessment and safety evaluation of bridges. Typically, the depth prediction of bridge pier scour is estimated using deterministic equations, which do not consider the uncertainties related to scour parameters. To consider these uncertainties, a reliability analysis of bridge pier scour is required. In the recent years, a number of efficient reliability methods have been proposed for the reliability-based assessment of engineering problems based on simulation, such as Monte Carlo simulation (MCS), subset simulation (SS), importance sampling (IS), directional simulation (DS), and line sampling (LS). However, no general guideline recommending the most appropriate reliability method for the safety assessment of bridge pier scour has yet been proposed. For this purpose, we carried out a comparative study of the five efficient reliability methods so as to originate general guidelines for the probabilistic assessment of bridge pier scour. In addition, a sensitivity analysis was also carried out to find the effect of individual random variables on the reliability of bridge pier scour.

Keywords:
bridge pier; scour; reliability analysis; failure probability; simulation; subset simulation (SS).

A.Ahmad, V. Plevris* and Q.Ζ. Khan, "Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks", Crystals, 10(9), Article ID 811, 22 pages (DOI: 10.3390/cryst10090811), 2020.

Abstract:
Recently, the use of fiber-reinforced polymers (FRP)-confinement has increased due to its various favorable effects on concrete structures, such as an increase in strength and ductility. Therefore, researchers have been attracted to exploring the behavior and efficiency of FRP-confinement for concrete structural elements further. The current study investigates improved strength and strain models for FRP confined concrete cylindrical elements. Two new physical methods are proposed for use on a large preliminary evaluated database of 708 specimens for strength and 572 specimens for strain from previous experiments. The first approach is employing artificial neural networks (ANNs), and the second is using the general regression analysis technique for both axial strength and strain of FRP-confined concrete. The accuracy of the newly proposed strain models is quite satisfactory in comparison with previous experimental results. Moreover, the predictions of the proposed ANN models are better than the predictions of previously proposed models based on various statistical indices, such as the correlation coefficient (R) and mean square error (MSE), and can be used to assess the members at the ultimate limit state.

Keywords:
artificial neural networks; confined concrete; strength model; FRP; strain model; RMSE.

A. Shabani*, M. Kioumarsi*, V. Plevris and H. Stamatopoulos, "Structural Vulnerability Assessment of Heritage Timber Buildings: A Methodological Proposal", Forests, 11(8), Article ID 881, 20 pages (DOI: 10.3390/f11080881), 2020.

Abstract:
The conservation of heritage structures is pivotal not only due to their cultural or historical importance for nations, but also for understanding their construction techniques as a lesson that can be applied to contemporary structures. Timber is considered to be the oldest organic construction material and is more vulnerable to environmental threats than nonorganic materials such as masonry bricks. In order to assess the structural vulnerability of heritage timber structures subjected to different types of risk, knowledge about their structural systems and configurations, the nature and properties of the materials, and the behavior of the structure when subjected to different risks, is essential for analysts. In order to facilitate the procedure, different assessment methods have been divided into the categories in situ and ex situ, which are applicable for vulnerability assessments at the element and full-scale level of a case study. An existing methodology for structural vulnerability assessments and conservation of heritage timber buildings is reviewed and a new methodology is proposed.

Keywords:
heritage timber buildings; risks and their effects; structural vulnerability assessment; in situ assessment methods; visual inspection; data analysis; ex situ assessment methods; numerical simulation; experimental test; assessment and conservation methodology.

M.Georgioudakis and V. Plevris*, “A comparative study of differential evolution variants in constrained structural optimization”, Frontiers in Built Environment: Computational Methods in Structural Engineering, 6:102 (DOI: 10.3389/fbuil.2020.00102), 2020.

Abstract:
Differential evolution (DE) is a population-based metaheuristic algorithm that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such algorithms make few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. DE is arguably one of the most versatile and stable population-based search algorithms that exhibits robustness to multi-modal problems. In the field of structural engineering, most real-world optimization problems are associated with one or several constraints. Constrained optimization problems are often challenging to solve due to their complexity and high nonlinearity. In this work we examine the performance of several DE variants, namely the traditional DE, the composite DE (CODE), the adaptive DE with optional external archive (JADE) and the self-adaptive DE (JDE and SADE), for handling constrained structural optimization problems associated with truss structures. The performance of each DE variant is evaluated by using five well-known benchmark structures in 2D and 3D. The evaluation is done on the basis of final optimum result and the rate of convergence. Valuable conclusions are obtained from the statistical analysis which can help a structural engineer in practice to choose the suitable algorithm for these kind of problems.

Keywords:
DE, SADE, JDE, JADE, CODE, differential evolution, structural optimization.

G. Solorzano and V. Plevris*, "Optimum Design of RC Footings with Genetic Algorithms According to ACI 318-19", Buildings, 10(6), Article ID 110, 17 pages (DOI: 10.3390/buildings10060110), 2020.

Abstract:
Engineers usually use trial-and-error approaches for dealing with design problems where they need to find the most economical design of a structural element in terms of its material cost while satisfying all the safety requirements imposed by the design codes. In this study, we employ a genetic algorithm (GA) with a dominance-based tournament selection technique for dealing with this design challenge. The methodology is applied in the design of reinforced concrete rectangular-shaped isolated footings in accordance with the American Concrete Institute ACI 318-19. First, the footing is encoded into a set of decision variables and an objective function is defined to compute the total cost based on the different construction materials. Then, the compliance of the design with the ACI 318-19 code is enforced by a constraint function that takes into consideration all the demand–capacity ratios for the different resistance requirements such as the allowable bearing pressure of the supporting soil, and the shear and flexural capacities of the footing, among others. Two numerical examples are presented where the results show a significant advantage in terms of material-cost and design-time reduction in comparison with the commonly used trial and error approach, proving the applicability of optimization algorithms (OAs) into the everyday design routine of the structural engineer.

Keywords:
structural design, optimization, ACI 318-19, concrete isolated footing, genetic algorithm, GA.

M. Georgioudakis and V. Plevris*, “On the Performance of Differential Evolution Variants in Constrained Structural Optimization”, Procedia Manufacturing, 44, pp. 371-378 (DOI: 10.1016/j.promfg.2020.02.281), 2020.

Abstract:
Constrained optimization is a highly important field of engineering as most real-world optimization problems are associated with one or several constraints. Such problems are often challenging to solve due to their complexity and high nonlinearity. Differential evolution (DE) is arguably one of the most versatile and stable population-based search algorithms that exhibits robustness to multi-modal problems and has shown to be very efficient when solving constrained global optimization problems. In this paper we investigate the performance of several DE variants existing in the literature such as the traditional DE, the composite DE (CoDE), the adaptive DE with optional external archive (JADE) and the self-adaptive DE (jDE and SaDE), for handling constrained structural optimization problems. The performance of each DE variant is quantified by using three well-known benchmark structures in 2D and 3D. It is shown that JADE, which updates control parameters in an adaptive way, truly exhibits superior performance and outperforms the other DE variants in all the cases examined.

Keywords:
structural optimization, differential evolution, de, code, jade, jde, sade.

Book: "Innovative Approaches in Computational Structural Engineering", Eds: G. C. Tsiatas and V. Plevris, Frontiers Media SA, Lausanne, 2020.

Description

Nowadays, numerical computation has become one of the most vigorous tools for scientists, researchers and professional engineers, following the enormous progress made during the last decades in computing technology, in terms of both computer hardware and software development. Although this has led to tremendous achievements in computer-based structural engineering, the increasing necessity of solving complex problems in engineering requires the development of new ideas and innovative methods for providing accurate numerical solutions in affordable computing times.

This collection aims at providing a forum for the presentation and discussion of state-of-the-art innovative developments, concepts, methodologies and approaches in scientific computation applied to structural engineering. It involves a wide coverage of timely issues on computational structural engineering with a broad range of both research and advanced practical applications.

This Research Topic encompasses, but is not restricted to, the following scientific areas: modeling in structural engineering; finite element methods; boundary element methods; static and dynamic analysis of structures; structural stability; structural mechanics; meshless methods; smart structures and systems; fire engineering; blast engineering; structural reliability; structural health monitoring and control; optimization; and composite materials, with application to engineering structures.

G.C. Tsiatas and V. Plevris*, “Editorial: “Innovative Approaches in Computational Structural Engineering”, Frontiers in Built Environment: Computational Methods in Structural Engineering, 6:39 (DOI: 10.3389/fbuil.2020.00039), 2020.

Abstract:
Over the last few decades, tremendous development has been made in the field of computing technology, in terms of both computer hardware and software development. As a result of this progress, numerical computation has now become one of the most effective tools for scientists, researchers, and professional engineers around the world. Although this has led to great achievements in computer-based structural engineering, the increasing necessity to solve complex problems in engineering requires the development of new ideas and innovative methods for providing accurate numerical solutions in affordable computing times. The Research Topic “Innovative Approaches in Computational Structural Engineering” aims to provide a forum for the presentation and discussion of state-of-the-art innovative developments, concepts, methodologies, and approaches in scientific computation applied to structural engineering. It involves a wide coverage of timely issues on computational structural engineering with a broad range of research and practical applications.

Keywords:
structural engineering, innovation, computational, FEM, BEM, meshless methods.

N. Moayyeri, S. Gharehbaghi and V. Plevris*, "Cost-Based Optimum Design of Reinforced Concrete Retaining Walls Considering Different Methods of Bearing Capacity Computation", Mathematics, 7(12), Article ID 1232, 21 pages (DOI: 10.3390/math7121232), 2019.

Abstract:
This paper investigates the effect of computing the bearing capacity through different methods on the optimum construction cost of reinforced concrete retaining walls (RCRWs). Three well-known methods of Meyerhof, Hansen, and Vesic are used for the computation of the bearing capacity. In order to model and design the RCRWs, a code is developed in MATLAB. To reach a design with minimum construction cost, the design procedure is structured in the framework of an optimization problem in which the initial construction cost of the RCRW is the objective function to be minimized. The design criteria (both geotechnical and structural limitations) are considered constraints of the optimization problem. The geometrical dimensions of the wall and the amount of steel reinforcement are used as the design variables. To find the optimum solution, the particle swarm optimization (PSO) algorithm is employed. Three numerical examples with different wall heights are used to capture the effect of using different methods of bearing capacity on the optimal construction cost of the RCRWs. The results demonstrate that, in most cases, the final design based on the Meyerhof method corresponds to a lower construction cost. The research findings also reveal that the difference among the optimum costs of the methods is decreased by increasing the wall height.

Keywords:
reinforced concrete, retaining wall, optimization, bearing capacity, particle swarm optimization, PSO.

V. Plevris and G. Markeset, “Educational Challenges in Computer-based Finite Element Analysis and Design of Structures”, Journal of Computer Science, 14(10), pp. 1351-1362 (DOI: 10.3844/jcssp.2018.1351.1362), 2018.

Abstract:
Computer simulations and computational methods, such as the Finite Element Analysis (FEA) have become essential methodologies in science and engineering during the last decades, in a wide variety of academic fields. Six decades after the invention of the digital computer, advanced FE simulations are used to enhance and leapfrog theoretical and experimental progress, at different levels of complexity. Particularly in Civil and Structural Engineering, significant research work has been made lately on the development of FE simulation codes, methodologies and validation techniques for understanding the behavior of large and complex structures such as buildings, bridges, dams, offshore structures and others. These efforts are aimed at designing structures that are resilient to natural excitations (wind loads, earthquakes, floods) as well as human-made threats (impact, fire, explosion and others). The skill set required to master advanced FEA is inherently interdisciplinary, requiring in-depth knowledge of advanced mathematics, numerical methods and their computational implementation, as well as engineering sciences. In this paper, we focus on the importance of sound and profound engineering education and knowledge about the theory behind the Finite Element Method to obtain correct and reliable analysis results for designing real-world structures. We highlight common mistakes made by structural engineers while simulating complex structures and the risk of structural damage because of human-made mistakes or errors in the model assumptions. The event of the collapse and eventual sinking of a concrete offshore platform in the North Sea is presented as a case study where a serious error in the finite element analysis played a crucial role leading to structural failure and collapse.

Keywords:
Computer methods, Engineering education, Finite Element Method, Finite Element Analysis, FEM, FEA, Simulation error, Modeling error, Structural damage.

G. Papazafeiropoulos and V. Plevris, “OpenSeismoMatlab: A new open-source software for strong ground motion data processing”, Heliyon, 4(9), Article ID e00784, 39 pages (DOI: 10.1016/j.heliyon.2018.e00784), 2018.

Abstract:
OpenSeismoMatlab is an innovative open-source software for strong ground motion data processing, written in MATLAB. The software implements an elastoplastic bilinear kinematic hardening constitutive model and uses a state-of-the-art single step single solve time integration algorithm featuring exceptional speed, robustness and accuracy. OpenSeismoMatlab can calculate various time histories and corresponding peak values, Arias intensity and its time history, significant duration, various linear elastic response spectra and constant ductility inelastic response spectra, as well as Fourier amplitude spectrum and mean period. Due to its open-source nature, the software can be easily extended or modified, having high research and educational value for the professional engineering and research community. In the present paper, the structure, algorithms and main routines of the program are explained in detail and the results for various types of spectra of 11 earthquake strong ground motions are calculated and compared to corresponding results from other proprietary software.

Keywords:
Strong ground motion data processing, open source software, spectrum, Fourier, seismic design, earthquake engineering.