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V. Plevris and H. Hosamo, “Responsible AI in Structural Engineering: A Framework for Ethical Use”, Frontiers in Built Environment, 11:1612575 (DOI: 10.3389/fbuil.2025.1612575), 2025.

Abstract:
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.

V. Plevris, A.T. Al-Sayegh, J. Mir and A. Ahmad, “Nondestructive Evaluation of Hybrid Concrete Properties Using Image Processing and Machine Learning”, Structures, 79, Article ID 109423 (DOI: 10.1016/j.istruc.2025.109423), 2025.

Abstract:
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.

V. Plevris, “A Glimpse into the Future of Civil Engineering Education: The New Era of Artificial Intelligence, Machine Learning, and Large Language Models”, Journal of Civil Engineering Education, 151(2) (10.1061/JCEECD.EIENG-2193), 2025.

Introduction:
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.

V. Plevris, "Blockchain applications in the construction industry", in Digital Twin and Blockchain for Sensor Networks in Smart Cities, T.A. Nguyen (Ed.), Elsevier, pp. 265-290, 2025.

Abstract:

This chapter delves into the transformative potential of blockchain technology in the construction industry, a sector pivotal to the global economy yet plagued by inefficiencies, lack of transparency, and slow adoption of digital innovations. It presents a comprehensive analysis, starting from the fundamental concepts of blockchain technology, including its decentralized nature, security features, and the role of consensus mechanisms like Proof of Work and Proof of Stake. The exploration extends to the practical applications of blockchain across various aspects of construction management, such as supply chain management, contract management, project management and documentation, and real estate transactions. Highlighting the sector’s critical challenges, including fragmented communication, inefficient supply chain management, and the perennial issues of delays and budget overruns, the chapter positions blockchain as a potent solution capable of enhancing transparency, efficiency, and accountability within the industry. It provides detailed insights into how blockchain can streamline operations, from automating procurement and improving inventory management to securing land registry processes and facilitating real-time tracking of materials. Moreover, the study reviews significant scholarly contributions to the field, offering a bibliometric analysis that underscores the burgeoning interest and research activity surrounding blockchain in construction and engineering. It also discusses the technical hurdles, legal and regulatory considerations, and the financial implications of blockchain adoption, laying out a balanced view of the opportunities and obstacles ahead. Concluding with a forward-looking perspective, the chapter envisions a future where blockchain technology not only addresses the current limitations of the construction industry but also fosters a new era of innovation, sustainability, and collaboration. Through this comprehensive examination, the chapter contributes valuable knowledge to academics, industry professionals, and policymakers interested in the intersection of blockchain technology and construction, encouraging further exploration and adoption of this groundbreaking technology in shaping the future of the built environment.

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