Peer-reviewed journal articles covering structural engineering, computational mechanics, optimization, artificial intelligence, and related fields.
Editorial
J84. Editorial: Digital transformation in construction: integrating metaverse, digital twin, and BIM
Authors:
I. Takewaki, Z. Lu, S. Azhar*, V. Plevris, Z. Chen, S. Kaewunruen and U. Berardi
Journal:
Frontiers in Built Environment
Publisher:
Frontiers
Volume: 12
Article ID:
1905914
Year: 2026
(First online: 22 July 2026)
This editorial introduces the Research Topic “Digital Transformation in Construction: Integrating Metaverse, Digital Twin, and BIM”, published in Frontiers in Built Environment. Edited by Izuru Takewaki, Zheng Lu, Salman Azhar, Vagelis Plevris, Zhen Chen, Sakdirat Kaewunruen, and Umberto Berardi, the collection brings together 16 research contributions exploring the transformative role of digital technologies in construction and the built environment. The Research Topic highlights the integration of Building Information Modeling (BIM), digital twins, metaverse technologies, artificial intelligence, and immersive visualization to improve the design, construction, operation, and management of buildings and infrastructure. The contributions address a broad range of applications, including construction safety, structural monitoring, digital documentation, infrastructure management, sustainability, carbon reduction, and circular construction practices. The editorial emphasizes the transition from isolated digital tools toward interconnected, data-driven environments that support more informed decision-making, greater efficiency, and improved resilience throughout the infrastructure lifecycle, while identifying important challenges and future research directions for digital transformation in construction.
Review Article
J83. A systematic review of computer vision-based assessment of concrete properties
Authors:
M. Ullah and V. Plevris*
Journal:
Neural Computing and Applications
Publisher:
Springer Nature
Volume: 38
Article ID:
249
Year: 2026
(First online: 10 April 2026)
Recent advancements in image processing and convolutional neural networks have revolutionized the assessment of concrete properties. These techniques provide non-destructive and automated solutions to analyze aggregates, air voids, cement phases, and surface defects in concrete and offer significant improvements over traditional manual methods. CNNs have been developed for tasks like segmentation, classification, and quantification of concrete properties under varied conditions. This paper provides a comprehensive review of the latest developments in computer vision applications for evaluating concrete properties. It explores state-of-the-art image processing and CNN architectures being utilized in concrete properties assessment. Then, the application of these techniques to enhance the precision and reliability of concrete analysis is discussed. The review is organized into various categories based on the specific applications and advancements of CNNs in analyzing concrete properties. Challenges such as data variability, feature overlap, and imaging inconsistencies are critically discussed. The potential directions for future research are proposed, integrating advanced multimodal imaging and deep learning frameworks. This review aims to guide researchers and practitioners in advancing the field of concrete property assessment and structural health monitoring of concrete structures.
Research Article
J82. Image-based prediction of concrete mechanical properties through CNN segmentation and ANN modeling
Authors:
M. Ullah, V. Plevris, J. Mir and A. Ahmad*
Journal:
Results in Engineering
Publisher:
Elsevier
Volume: 29
Article ID:
108637
Year: 2026
(First online: 21 December 2025)
Accurate prediction of the mechanical properties of concrete is essential for assessing structural performance and ensuring safety. Traditional destructive and non-destructive testing methods are labor-intensive, costly, and often impractical for evaluating in-service structures due to specimen extraction requirements, calibration challenges, and limited field accuracy. This study presents an automated image-based framework that integrates Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) models to predict the compressive and tensile strengths of concrete from image-derived features. High-resolution images of concrete cylinders with different mix ratios and water-cement ratios were used to train a DeepLabV3+ segmentation model with EfficientNet-B5 as the encoder. The model achieved a mean Intersection over Union of 0.92 and an F1-score of 0.95 for coarse aggregate segmentation. Image analysis algorithms were then applied to extract aggregate characteristics, including proportion, size, roundness, and nearest-neighbor distance. Meanwhile, statistical descriptors of mortar pixels were derived to represent the matrix properties. These features served as inputs to multiple ANN models. The hybrid AggMortarNet model, combining aggregate and mortar characteristics, achieved the best predictive performance with R2 = 0.9680 and RMSE = 0.0363 for compressive strength, and R2 = 0.9607 and RMSE = 0.0368 for tensile strength, outperforming non-destructive methods. The results demonstrate an automated, data-driven approach with strong potential for Structural Health Monitoring (SHM) and quality assessment of both newly cast and existing concrete structures.
Editorial
J81. Computational Methods in Structural Engineering: Current Advances and Future Perspectives
Authors:
V. Plevris*, M. Georgioudakis and M. Kioumarsi
Journal:
Computation
Publisher:
MDPI
Volume: 13, Issue: 9
Article ID:
224
Year: 2025
(First online: 16 September 2025)
This brief editorial introduces the Special Issue “Computational Methods in Structural Engineering”. This Special Issue brings together recent advances in computational approaches—including finite element modeling, machine learning applications, stochastic analysis, and high-precision numerical methods— highlighting their increasing influence on the analysis, design, and assessment of modern structural systems. The published contributions cover topics such as the nonlinear finite element method (FEM) for structural response under extreme loading, advanced plate and composite modeling, explainable AI for material characterization, machine learning for predictive performance modeling, data-driven signal processing for structural health monitoring, and stochastic analysis of dynamic inputs. Through this collection of studies, this Special Issue underscores both the opportunities and the challenges of applying advanced computational methods to enhance the resilience, efficiency, and understanding of structural engineering systems.
Research Article
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.
Research Article
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.
Research Article
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.
Research Article
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.
Research Article
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.
Research Article
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.
Research Article
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.
Research Article
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.
Forum Article
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)
This Forum article explores the transformative potential of artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in civil engineering education, examining how emerging technologies can enhance traditional teaching and learning practices. It identifies five key areas of application: intelligent tutoring and personalized learning systems, automated grading and feedback, interactive simulations and virtual laboratories, enhanced collaboration and communication, and resource management and curriculum development. The article discusses how AI-driven tools can support individualized learning experiences, improve teaching efficiency, and help students develop the knowledge and practical skills required in modern civil engineering. It also addresses important challenges associated with the integration of AI in education, including technical infrastructure, data privacy and security, algorithmic bias, accessibility, ethical considerations, and the need to balance technological innovation with human instruction. The article emphasizes that AI should complement rather than replace educators, advocating for its responsible integration to create more effective, inclusive, and adaptable civil engineering education.
Research Article
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.
Research Article
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.
Review Article
J69. AI in Structural Health Monitoring for Infrastructure Maintenance and Safety
Authors:
V. Plevris* and G. Papazafeiropoulos
Journal:
Infrastructures
Publisher:
MDPI
Volume: 9, Issue: 12
Article ID:
225
Page count:
25
Year: 2024
(First online: 07 December 2024)
This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect of infrastructure maintenance and safety. This study begins with a bibliometric analysis to identify current research trends, key contributing countries, and emerging topics in AI-integrated SHM. We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition and sensor networks, highlighting improvements in sensor technology and data collection; (2) data processing and signal analysis, where AI techniques enhance feature extraction and noise reduction; (3) anomaly detection and damage identification using machine learning (ML) and deep learning (DL) for precise diagnostics; (4) predictive maintenance, using AI to optimize maintenance scheduling and prevent failures; (5) reliability and risk assessment, integrating diverse datasets for real-time risk analysis; (6) visual inspection and remote monitoring, showcasing the role of AI-powered drones and imaging systems; and (7) resilient and adaptive infrastructure, where AI enables systems to respond dynamically to changing conditions. This review also addresses the ethical considerations and societal impacts of AI in SHM, such as data privacy, equity, and transparency. We conclude by discussing future research directions and challenges, emphasizing the potential of AI to enhance the efficiency, safety, and sustainability of infrastructure systems.
Research Article
J68. Towards Enhanced Built Cultural Heritage Conservation Practices: Perceptions on Industry 5.0 Principles and Enabling Technologies
Authors:
A. Jiménez Rios*, M. Nogal, V. Plevris, R. Ramirez and M.L. Petrou
Journal:
The Historic Environment: Policy & Practice
Publisher:
Taylor & Francis
Volume: 15, Issue: 4
Pages:
466–492
Year: 2024
(First online: 27 November 2024)
Despite its recent adoption, Industry 5.0 has attracted significant attention from researchers across various fields. However, the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C) industry, particularly in the context of built cultural heritage conservation, has lagged in this regard. This study aims to gain a deeper understanding of conservation professionals’ perceptions regarding the adoption of Industry 5.0 principles and enabling technologies, as well as the perceived barriers and the skills needed to address them. A survey questionnaire was designed, tested, and implemented to collect relevant data. Analysis of the collected data reveals that, although there is a clear recognition of the significance of Industry 5.0 principles and enabling technologies, their application in built cultural heritage conservation remains limited. Future initiatives should prioritise bridging knowledge gaps, enhancing training programmes, and securing necessary resources to overcome these existing barriers.
Research Article
J67. Exploring porosity impact on the free vibration of FG plates using trigonometric shear deformation theory
Authors:
V. Plevris*, L. Hadji and R. Madan
Journal:
Structural Engineering and Mechanics
Publisher:
Techno-Press
Volume: 92, Issue: 3
Pages:
267–275
Year: 2024
(First online: 10 November 2024)
This study investigates the free vibration behavior of functionally graded (FG) plates using trigonometric shear deformation plate theory. The novelty of this work lies in the incorporation of porosities, which are inherent in FG materials due to manufacturing processes, and their detailed impact on the vibrational performance of these plates. Unlike existing studies, this research comprehensively examines multiple porosity distribution patterns, including homogeneous, "O", "X", and "V" configurations, which are seldom analyzed together. The governing equations of motion are derived using Hamilton's principle and solved analytically with the Navier method for simply supported boundary conditions. A key contribution of this study is the exploration of how porosity levels, distribution types, and geometry parameters collectively influence the natural frequencies of FG plates. The results highlight the significant effect of different porosity patterns, with "X"-shaped porosity yielding the highest natural frequency and homogeneous distribution leading to the lowest. Furthermore, the findings reveal that increased porosity levels can either enhance or diminish the vibrational characteristics depending on the distribution pattern. These insights provide valuable guidance for optimizing the design of FG plates for various engineering applications, such as aerospace and biomedical industries.
Review Article
J66. AI-Driven Innovations in Earthquake Risk Mitigation: A Future-Focused Perspective
Authors:
V. Plevris*
Journal:
Geosciences
Publisher:
MDPI
Volume: 14, Issue: 9
Article ID:
244
Page count:
29
Year: 2024
(First online: 15 September 2024)
This study explores the transformative potential of artificial intelligence (AI) in revolutionizing earthquake risk mitigation across six key areas. Unlike traditional approaches, this paper examines how AI-driven innovations can uniquely enhance early warning systems, enabling real-time structural health monitoring, and providing dynamic, multi-hazard risk assessments that seamlessly integrate seismic data with other natural hazards such as tsunamis and landslides. It introduces groundbreaking applications of AI in earthquake-resilient design, where generative design algorithms and predictive analytics create structures that optimally balance safety, cost, and sustainability. The study also presents a novel discussion on the ethical implications of AI in this domain, stressing the critical need for transparency, accountability, and bias mitigation. Looking forward, the manuscript envisions the development of advanced AI platforms capable of delivering real-time, personalized risk assessments, immersive public training programs, and collaborative design tools that adapt to evolving seismic data. These innovations promise not only to significantly enhance current earthquake preparedness but also to pave the way toward a future where the societal impact of earthquakes is drastically reduced. This work underscores the potential of AI’s role in shaping a safer, more resilient future, emphasizing the importance of continued innovation, ethical governance, and collaborative efforts.
Review Article
J65. Industry 5.0, Towards an Enhanced Built Cultural Heritage Conservation Practice
Authors:
A. Jiménez Rios*, M.L. Petrou, R. Ramirez, V. Plevris and M. Nogal
Journal:
Journal of Building Engineering
Publisher:
Elsevier
Volume: 96
Article ID:
110542
Year: 2024
(First online: 22 August 2024)
The rise of Industry 4.0 has led to a rapid increase in digitalization and industrial operations. However, it has recently been deemed insufficient in fulfilling European objectives for 2030. In response, and to counteract the unintended negative consequences triggered by Industry 4.0, Industry 5.0 has been introduced. The purpose of this article is to shed light on how the architecture, engineering, construction, management, operation, and conservation industry can adapt and better prepare to embrace novel Industry 5.0 principles and enabling technologies, ultimately resulting in enhanced conservation practices for the built cultural heritage environment. To achieve this, a systematic literature review was conducted following the PRISMA methodology. The principal results of this article highlight the work of different conservation professionals and our views on the potential of Industry 5.0 for enhancing conservation practices. Major conclusions indicate that artificial intelligence and digital twins are the two most studied technologies in the field. Sustainability is broadly discussed throughout the analyzed literature, whereas resilience and human centrism require further research and implementation efforts to achieve a holistic Industry 5.0 adoption. The significant scientific novelty of this work lies in the comprehensive scope of the review in terms of principles and enabling technologies, with a particular emphasis on heritage buildings. Thus, it is valuable for conservation practitioners seeking best practices, for policymakers as it suggests ways to encourage the adoption of novel technologies and principles in conservation, and for researchers as it highlights gaps and stimulates further paths of research and innovation.