ABSTRACT

Civil engineering encompasses many of the most fundamental aspects of everyday life in society. While usually regarded as the most conventional engineering sector, it has undergone a radical transformation in recent years, particularly over the last decade. Indeed, the demand for increasingly sophisticated solutions for designing and managing complex civil engineering systems continues to grow, making advanced computer-aided tools essential throughout their entire lifecycle. These applications can play a pivotal role in optimizing design, supporting decision-making, planning economic resources efficiently, implementing effective maintenance programs, and ensuring high performance standards. Within this framework, artificial intelligence (AI) and machine learning (ML) are now permeating all facets of civil engineering, driving emerging trends in risk mitigation, health monitoring, design optimization, automation, and computer vision. These advancements are steering the field toward a more sustainable, environmentally friendly, and cost-effective future. However, challenges remain, particularly regarding the interpretability of complex models compared to traditional analytical, experimental, and empirical approaches. To address these concerns, researchers must enhance knowledge representation and reasoning, improve information fusion techniques, refine computer vision interpretability, and test AI models against real-world civil engineering applications. In this perspective, the current Special Session aims to foster the exchange of the latest advancements and applications in AI and ML for civil engineering, with a particular focus on validation through case studies.

The main topics, but not limited to, are:

• AI and ML in Civil Engineering case studies
• AI and ML in Civil Engineering methodologies
• AI and ML interpretability studies with Civil Engineering-related applications
• AI and ML in structural health monitoring and indirect testing
• AI and ML in structural optimization and computational intelligence

ABOUT THE ORGANIZERS

Marco Martino Rosso, Ph.D. in Civil and Environmental Engineering (2024). He is currently a post-doctoral research fellow at Politecnico di Torino, ITALY. His research interests initially involved structural optimization using soft computing and computational intelligence with meta-heuristic optimization algorithms. Then, he specialized in the Structural Health Monitoring (SHM) field for Civil Engineering structures and built environments. Special focus has been dedicated to operational modal analysis (OMA) output-only vibration-based methods, and effectively integrating artificial intelligence (AI) and machine learning (ML) data-driven solutions. His research studies aim to provide methodological improvements into the current conventional paradigms of SHM, from the monitoring level, to damage detection, damage diagnosis, and even its prognosis.

Jonathan Melchiorre , Ph.D. student in the national Ph.D. programme on artificial intelligence. His research focus on the application of artificial intelligence on structural engineering problems. The main topics are structural optimization, form-finding methodologies and structural geometries in general.

Giuseppe Carlo Marano, PhD in Structural Engineering at the University of Florence (2000). Post-doctoral scholarship in “Civil Engineering Science” at Technical University of Bari in 2001 and Lecturer in structural engineering in the same university in 2001. Visiting assistant professor in Cambridge (2002), associate professor in 2011 at Politecnico di Bari and visiting Professor in Loughborough (2012) and at Hunan University, Changsha, Hunan Province (China) (2014), is research fellow at the SIBERC (Sustainable and Innovative Bridge Engineering Research Center), Fuzhou University, Fuzhou, Fujian Province, China and (2016/2018) full Professor in Structural Design, Faculty of Civil Engineering, Fuzhou University, Fuzhou, Fujian Province, China. From 2018 is full professor in structural Design at Politecnico di Torino, where he also covered vice director of the Department of Structural, Environmental and Geotechnical Engineering until 2023. His research interests deal with structural optimization, form finding and structural health monitoring. He is author of four European patents and more than 300 papers published in international journals or presented at conferences.

Giansalvo Cirrincione, (Senior Member, IEEE) received the M.S. degree in electrical engineering from the Politecnico di Torino, Italy, in 1991, and the Ph.D. degree from the Laboratoire d’Informatique et Signaux de l’Institut National Polytechnique de Grenoble, Grenoble, France, in 1998. He was a Postdoctoral Scholar at the Department of Signals, Identification, System Theory and Automation (SISTA), Leuven University, Leuven, Belgium, in 1999. Since 2000, he has been an Assistant Professor with the Department of Electrical Engineering, University of Picardie Jules Verne, Amiens, France. He is currently an Adjunct Associate Professor with The University of the South Pacific. His current research interests include neural networks, data analysis, computer vision, brain models, and system identification.

Giuseppe Quaranta earned his Ph.D. in Structural Engineering from Sapienza University of Rome in 2011. Since 2019, he has been an Associate Professor in Structural Engineering at the same university, within the Department of Structural and Geotechnical Engineering. His research focuses on structural monitoring, including the design of conventional and smart sensing systems, system identification, and diagnostics for civil structures and infrastructures. He also specializes in structural control, developing, designing, and testing passive devices for vibration control. Additionally, his work in structural concrete encompasses the assessment and design of reinforced concrete structures, concrete-filled steel tubes, and truss-reinforced composite steel-concrete beams. In the field of artificial intelligence, his interests lie in applying computational intelligence and machine learning techniques for optimal design, dynamic identification, and health monitoring. He has authored over 70 papers published in international journals.

Amedeo Manuello Bertetto is Associate Professor at the Politecnico di Torino, where he currently teaches courses on Design and Optimization of Shells and Spatial Structures, Structural Instability Phenomena and Structural Mechanics in Civil Engineering and Architecture degree courses. Amedeo Manuello has been a member of several prestigious societies, including the Seismological Society of America (since 2006), the American Society for Experimental Mechanics (since 2008), IASS (since 2017), and SISCo (since 2017). He has been Scientific Director of the PoliTo research unit for the Monfron project since 2018 and Director of the Monfron site Laboratory. Since 2019, he has been an international expert for the Notre-Dame de Paris restoration. He co-founded the Italian Workshop on Shells and Spatial Structures (2020) and the Italian Association for the Studies on Shell and Spatial Structures (2023). He also serves on the Review Committee of the Israel Science Foundation (since 2022) and the Council of the School of Masters and Continuing Education at PoliTo (since 2022). His research activity (Scopus H index: 27) is devoted to innovative experimental investigations and non-destructive techniques for the study of the integrity and the durability of infrastructures and historical buildings. Other specific studies were conducted on the instability phenomena of slender elements, the instability phenomena due to snap-through of shells, spatial structures and lowered arches. Many studies are also devoted to the proposition of an original form finding tool for grid shell structures.