V. Plevris and A. Ahmad, “Deriving analytical solutions using symbolic matrix structural analysis for continuous beams”, Scientific Reports, 15, Article ID 15897 (DOI: 10.1038/s41598-025-98023-x), 2025.

Abstract:
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.

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, "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.