Peer-reviewed journal articles in structural engineering, computational mechanics,
artificial intelligence, and related fields.
J80. An n-order refined plate theory for Bending and Buckling of Functionally Graded Polymer Composite Plates Reinforced with Graphene Nanoplatelets
Authors:
V. Plevris*, L. Hadji and H. Ait Atmane
Journal:
Advances in Nano Research
Publisher:
Techno-Press
Volume: 19, Issue: 1
Pages:
41–51
Year: 2025
(First online: 24 July 2025)
This study investigates the bending and buckling behavior of functionally graded multilayer graphene nanoplatelet (GPL)/polymer composite plates using an n-order refined plate theory. The theory introduces a higher-order polynomial displacement field that ensures variational consistency and eliminates the need for shear correction factors. In this formulation, shear stresses vary parabolically through the plate thickness, and stress-free conditions are satisfied at both the top and bottom surfaces, resulting in improved accuracy compared to conventional plate theories. A key innovation of this work lies in the layer-wise variation of GPL weight fractions, enabling the design of functionally graded nanocomposites with both uniform and non-uniform reinforcement patterns-specifically, UD, FG-O, FG-X, and FG-A. While most existing studies are limited to uniformly distributed GPLs or rely on lower-order theories, this study addresses these limitations by proposing an analytically tractable higher-order model that can accurately capture shear deformation effects and by systematically analyzing the mechanical influence of different GPL distribution patterns. This dual advancement fills an important gap in the literature, particularly in understanding the performance of non-uniformly graded nanocomposites under bending and buckling. The effective Young's modulus is predicted using the Halpin-Tsai micromechanics model, and the rule of mixtures is used to determine the effective Poisson's ratio and mass density. Analytical solutions for static deflection and buckling are derived for simply supported plates using the Navier solution technique. The results show that non-uniform GPL distributions, particularly FG-X and FG-O, significantly enhance bending stiffness and buckling resistance by concentrating reinforcement near high-stress regions. Additionally, increasing the GPL weight fraction and optimizing GPL geometry further improve structural performance. This study offers new insights into the tailored design of functionally graded nanocomposite plates and provides practical guidance for lightweight, high-performance structural components in aerospace, automotive, and civil engineering applications.
J79. Can Quantum Computing Surpass Classical Algorithms in Optimizing Building Performance? A Benchmark Study with 15000 Simulations
Authors:
H. Hosamo*, V. Plevris, D. Kraniotis and C.N. Rolfsen
Journal:
Energy & Buildings
Publisher:
Elsevier
Volume: 346, Issue: 1
Article ID:
116156
Year: 2025
(First online: 15 July 2025)
Optimizing building performance is essential for enhancing energy efficiency and occupant comfort. This study evaluates the applicability of quantum computing–based optimization methods in the Architecture, Engineering, and Construction (AEC) industry by comparing the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) with classical multi-objective optimization algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). A dataset of 15,000 building simulations was used to train an Extreme Gradient Boosting (XGBoost) model for predicting total energy consumption (kWh/m2/year) and Predicted Percentage of Dissatisfied (PPD) occupants. These predictions were then used in the optimization phase. NSGA-II produced the best trade-offs, achieving energy consumption between 17.84 and 19.84 kWh/m2/year and PPD below 5.2 %, with strong diversity and convergence. QAOA executed faster (0.54 min) than NSGA-II (18.9 min) but resulted in higher energy values (31.85–55.62 kWh/m2/year) and weaker solution quality. Quantum Annealing completed in 0.37 min but returned clustered solutions near 45.88 kWh/m2/year. While the current limitations of quantum methods constrain their effectiveness, the findings indicate their potential as fast solvers in future building performance optimization workflows, particularly as hardware and algorithmic capabilities mature.
J78. Responsible AI in Structural Engineering: A Framework for Ethical Use
Authors:
V. Plevris* and H. Hosamo
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 11
Article ID:
1612575
Year: 2025
(First online: 11 July 2025)
The integration of Artificial Intelligence (AI) into structural engineering holds great promise for advancing analysis, design, and maintenance. However, it also raises critical ethical and governance challenges—including bias, lack of transparency, accountability gaps, and equity concerns—which are particularly significant in a discipline where public safety is paramount. This study addresses these issues through eight fictional but realistic case studies that illustrate plausible ethical dilemmas, such as algorithmic bias in predictive models and tensions between AI-generated recommendations and human engineering judgment. In response, the study proposes a structured framework for responsible AI implementation, organized into three key domains: (i) Technical Foundations (focusing on bias mitigation, robust validation, and explainability); (ii) Operational and Governance Considerations (emphasizing industry standards and human-in-the-loop oversight); and (iii) Professional and Societal Responsibilities (advocating for equity, accessibility, and ethical awareness among engineers). The framework offers actionable guidance for engineers, policymakers, and researchers seeking to align AI adoption with ethical principles and regulatory standards. Beyond offering practical tools, the study explores broader theoretical and institutional implications of AI, including risks associated with model drift, the need for lifecycle oversight, and the importance of cultural and geographic adaptability. It also outlines future challenges and opportunities, such as incorporating AI ethics into engineering education and considering the ethical impact of emerging technologies like quantum computing and digital twins. Rather than offering prescriptive answers, the study aims to initiate an essential dialogue on the evolving role of AI in structural engineering, equipping stakeholders to manage its benefits and risks while upholding trust, fairness, and public safety.
J77. Nondestructive Evaluation of Hybrid Concrete Properties Using Image Processing and Machine Learning
Authors:
V. Plevris*, A.T. Al-Sayegh, J. Mir and A. Ahmad
Journal:
Structures
Publisher:
Elsevier
Volume: 79
Article ID:
109423
Year: 2025
(First online: 30 June 2025)
Advancements in informatics, such as image processing (IP) and machine learning (ML), are increasingly being utilized to evaluate the mechanical properties of reinforced concrete structures. This study focuses on hybrid concrete (HC), which incorporates cement replacement materials (CRM) like fly ash and silica fume to enhance its mechanical performance while promoting sustainability. A novel methodology combining IP with supervised ML models—Support vector machine (SVM), boosted ensemble regression (BRE), and Gaussian process regression (GPR)—was developed to predict the compressive and tensile strengths of HC. A comprehensive dataset was created using 162 cylindrical specimens prepared with various mix ratios, CRM replacement levels, and curing durations. High-resolution images of both horizontal and vertical cuts of the specimens were analyzed, and statistical features were extracted to train the ML models. The results demonstrated the models’ high accuracy in predicting mechanical properties, with GPR emerging as the most reliable method. The findings confirm the effectiveness of integrating IP with ML as a nondestructive testing approach for concrete evaluation, offering a fast, cost-effective, and environmentally friendly alternative to traditional methods. This study underscores the potential of combining advanced computational techniques with sustainable materials to innovate in concrete technology.
J76. Deriving analytical solutions using symbolic matrix structural analysis for continuous beams
Authors:
V. Plevris* and A. Ahmad
Journal:
Scientific Reports
Publisher:
Springer Nature
Volume: 15
Article ID:
15897
Year: 2025
(First online: 07 May 2025)
This study investigates the use of symbolic computation in Matrix Structural Analysis (MatSA) for continuous beams, using the MATLAB Symbolic Math Toolbox. By employing symbolic MatSA, analytical expressions for displacements, support reactions, and internal forces are derived, offering deeper insights into structural behavior. This approach facilitates efficient and scalable sensitivity analysis, where partial derivatives of outputs concerning input parameters can be directly computed, enhancing design exploration. The development includes an open-source MATLAB program, hosted on GitHub, enabling symbolic analysis of continuous beams subjected to point and uniform loads. This approach is valuable for both engineering practice and pedagogy, enriching the understanding of structural mechanics and aiding in education by illustrating clear parameter relationships. The program supports deriving influence lines and identifying maximum response values.
J75. From Integrity to Inflation: Ethical and Unethical Citation Practices in Academic Publishing
Authors:
V. Plevris*
Journal:
Journal of Academic Ethics
Publisher:
Springer Nature
Year: 2025
(First online: 21 April 2025)
Citation counts are a key metric in academic success, influencing career advancement and funding. However, the pressure to increase these counts has led to unethical practices such as citation inflation through manipulation. This paper examines strategies such as excessive self-citation, coercive citation demands by reviewers, and overuse of unpublished works, which distort the academic record and undermine scholarly integrity. The paper also explores ethical approaches to increasing citation counts, emphasizing high-quality research, appropriate journal selection, and active dissemination through reputable channels. A quantitative analysis of self-citation practices across different countries and fields revealed significant disparities, with some nations exhibiting high levels of self-citation among top scientists, while others showed more restrained behaviors. These findings suggest that citation practices may be influenced by various factors, including national research policies, cultural norms and others. The study highlights the potential long-term consequences of these behaviors for academic careers and the scientific community. Practical solutions to curb citation manipulation, such as stricter editorial oversight and improved journal collaboration, are proposed. The study aims to raise awareness of ethical challenges in academic publishing and offers strategies to maintain integrity in citation practices, ensuring that metrics reliably measure scholarly impact.
J74. Towards Industry 5.0: A Stakeholder Analysis to Understand the Human Role in the Adoption of a Heritage Bridge Human-Centric Digital Twin Framework
Authors:
A. Jiménez Rios*, V. Plevris and M. Nogal
Journal:
Structure and Infrastructure Engineering
Publisher:
Taylor & Francis
Article ID:
2490126
Year: 2025
(First online: 11 April 2025)
The adoption of a novel industry paradigm is an untamed problem that requires strong social consensus and involves a high degree of technological uncertainty. To solve this problem a multi-actor engagement and agreement are needed. In this article, the methodology and the findings obtained after conducting a stakeholder analysis to understand how different actors could work together towards the adoption of Industry 5.0 principles and enabling technologies are presented. The analysis has been framed within a case study dealing with the conservation of historical bridges in the city of Oslo, Norway. The education institutions of the city were assumed as the problem owners. This research indicates that the Ministry of Transport and the Ministry of Climate and Environment, along with their subordinate agencies (Statens Vegvesen and Riksantikvaren, respectively) together with Oslo Kommune and its Cultural Heritage Office, possess the critical financial and regulatory resources necessary for adopting this paradigm. Their leadership and capacity to mobilise resources are pivotal in incentivising other stakeholders. Such resources should be driven towards a suitable business model, the adoption of human-centric digital twins as enabling technology, the establishment of interdisciplinary collaborations between the identified stakeholders, and the up-skilling/re-skilling of the industry workforce.
J73. Scholarship of Teaching and Learning in Civil and Structural Engineering. A Systematic Literature Review
Authors:
A. Jiménez Rios*, V. Plevris, M. Nogal and W. Admiraal
Journal:
Journal of Civil Engineering Education
Publisher:
ASCE
Volume: 151, Issue: 3
Year: 2025
(First online: 11 March 2025)
The Scholarship of Teaching and Learning (SoTL) pertains to scholarly endeavors centered on the pedagogical aspects of teaching and learning, and its principal objective is the enhancement of students’ educational experiences. This systematic literature review addresses the following questions: (1) In what capacity do educators within the field of civil and structural engineering (CaSE) engage with SoTL?, and (2) What are the benefits of implementing a SoTL for CaSE educators? The scope of the review encompasses SoTL studies specifically developed by CaSE educators and implemented within CaSE teaching and learning environments. Findings are synthesized and disseminated via a bibliometric analysis and a narrative synthesis. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. It was found that CaSE educators participate in SoTL endeavors through diverse approaches; however, such involvement remains more of an exception than a common practice. The insufficiency of existing benefits and incentives, if any, serves as a barrier hindering broader engagement and participation in SoTL activities.
J72. A Glimpse into the Future of Civil Engineering Education: The New Era of Artificial Intelligence, Machine Learning, and Large Language Models
Authors:
V. Plevris*
Journal:
Journal of Civil Engineering Education
Publisher:
ASCE
Volume: 151, Issue: 2
Year: 2025
(First online: 17 February 2025)
In recent years, the integration of machine learning (ML) and artificial intelligence (AI) into education has revolutionized the field, creating opportunities for enhancing teaching and learning. This transformation is significant in the domain of civil engineering, where students must grasp complex theoretical concepts and acquire extensive practical knowledge to succeed in their careers. This demand necessitates innovative approaches that can provide personalized, efficient, and effective learning experiences. AI, with its ability to process vast amounts of data and learn from patterns, is uniquely positioned to meet these demands. Large language models (LLMs), such as ChatGPT, have shown potential in providing personalized learning experiences, offering real-time assistance, and automating tasks. These capabilities can address some persistent challenges in civil engineering education, such as the need for individualized attention, timely feedback, and catering to different learning needs. In this paper we explore the application of AI, ML, and LLMs, in civil engineering education and highlight five key areas where AI can make a significant impact. We show that the integration of AI technologies in civil engineering education offers benefits, including improved learning outcomes (LOs), efficiency in teaching, and enhanced engagement. However, it also presents challenges. Through a comprehensive exploration of these topics, we aim to provide valuable insights into how AI techniques can be leveraged to enhance the education of civil engineers, contributing to the ongoing dialogue about the future of civil engineering education in the age of AI.
J71. Deriving Analytical Solutions Using Symbolic Matrix Structural Analysis: Part 2 – Plane Trusses
Authors:
V. Plevris and A. Ahmad*
Journal:
Heliyon
Publisher:
Cell Press
Volume: 11, Issue: 4
Article ID:
e42372
Year: 2025
(First online: 06 February 2025)
This study extends the use of symbolic computation in matrix structural analysis to plane trusses, expanding on previous work that focused on continuous beams. We present a fully open-source MATLAB program, available on GitHub, that performs symbolic analysis of 2D trusses subjected to point loads, applicable to any truss configuration. Using the Symbolic Math Toolbox, the program derives closed-form analytical expressions for displacements, support reactions, and axial forces, providing deeper insight into structural behavior. A key advantage of the symbolic approach is its ability to perform sensitivity analysis efficiently by computing partial derivatives of structural responses with respect to input parameters. This feature enhances design exploration and optimization by allowing direct evaluation of parameter influences. Moreover, the framework is highly scalable, capable of generating symbolic solutions even for large-scale truss structures, something previously unattainable using traditional methods due to computational limitations. The tool serves both engineering practice and education, offering clear insights into parameter relationships and strengthening conceptual understanding in structural mechanics. To ensure accuracy, the symbolic results were rigorously validated against two commercial finite element software programs and results from the literature, with complete agreement. These validations confirm the reliability, scalability, and general applicability of the proposed methodology.
J70. Assessing Uncertainty in Image-Based Monitoring: Addressing False Positives, False Negatives, and Base Rate Bias in Structural Health Evaluation
Authors:
V. Plevris*
Journal:
Stochastic Environmental Research and Risk Assessment
Publisher:
Springer Nature
Volume: 39
Pages:
959–972
Year: 2025
(First online: 26 January 2025)
This study explores the limitations of image-based structural health monitoring (SHM) techniques in detecting structural damage. Leveraging machine learning and computer vision, image-based SHM offers a scalable and efficient alternative to manual inspections. However, its reliability is impacted by challenges such as false positives, false negatives, and environmental variability, particularly in low base rate damage scenarios. The Base Rate Bias plays a significant role, as low probabilities of actual damage often lead to misinterpretation of positive results. This study uses both Bayesian analysis and a frequentist approach to evaluate the precision of damage detection systems, revealing that even highly accurate models can yield misleading results when the occurrence of damage is rare. Strategies for mitigating these limitations are discussed, including hybrid systems that combine multiple data sources, human-in-the-loop approaches for critical assessments, and improving the quality of training data. These findings provide essential insights into the practical applicability of image-based SHM techniques, highlighting both their potential and their limitations for real-world infrastructure monitoring.