Bernardino Chiaia’s research while affiliated with Politecnico di Milano and other places

Bridges serve as vital links within transportation networks, fostering societal connectivity and economic growth. Italy's diverse terrain, encompassing mountains, hillsides, and coastal regions, underscores the importance of bridge infrastructure. Following notable bridge collapses in recent years, understanding their causes and enhancing safety measures has become paramount. This study presents a comprehensive dataset spanning 2000 to 2023, cataloguing bridge collapses in Italy and analyzing their characteristics , causes, and consequences. Also, it investigates six notable case studies. In total, 246 collapses were documented, with hydraulic issues identified as the predominant cause (80.5%). Collapses primarily affect road bridges (86.2%), especially at provincial and municipal levels. Notably, the study details the human toll of collapses, reporting 70 fatalities and 51 injuries. The distinction between natural (83.7%) and human (16.3%) caused collapses highlights the need for improved approaches to bridge management and risk mitigation strategies. The evaluation of the failure rates is hampered by the current incompleteness of nationwide infrastructure registries. By examining case studies and statistical trends, this research provides valuable insights for enhancing bridge resilience and informing future infrastructure policies and practices. ARTICLE HISTORY

The conventional framework for Structural Health Monitoring (SHM) primarily focuses on individual structures. However, to effectively identify the most vulnerable elements, preliminary studies are required at a wide area scale. This becomes particularly challenging in urban settings, where numerous buildings of varied shapes, ages, and structural conditions are closely spaced from one another. A twofold task is therefore required: the automated identification and differentiation of various structures, coupled with a ranking system based on perceived structural risk, here assumed to be linked to their deformation patterns. It integrates displacement measurements acquired through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, specifically employing the full-resolution Small Baseline Subset (SBAS) approach, with Hierarchical Clustering. The effectiveness of this method is successfully demonstrated and validated in two selected areas of Rome, Italy, serving as case studies. The results achieved on this wide area scale monitoring can be used to select the constructions that need a more in-depth assessment.

The conventional Structural Health Monitoring (SHM) framework focuses on individual structures. However, preliminary studies are required at a large territorial scale to effectively identify the most vulnerable elements. This becomes particularly challenging in urban settings, where numerous buildings of varied shapes, ages, and structural conditions are closely spaced from one another. A twofold task is therefore required: the automated identification and differentiation of various structures, coupled with a ranking system based on perceived structural risk, here assumed to be linked to their deformation patterns. It integrates displacement measurements acquired through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, specifically employing the full-resolution Small Baseline Subset (SBAS) approach coupled with Hierarchical Clustering. The effectiveness of this method is successfully demonstrated and validated in two selected areas of Rome, Italy, serving as case studies. The results of this vast-area scale monitoring can be used to select the constructions that need a more in-depth assessment.

Estimating the resilience of civil infrastructures is crucial for disaster prevention (i.e. earthquakes), encompassing both above-and underground constructions. However, while below-ground infrastructures are generally acknowledged as less vulnerable than their over-ground counterparts, this aspect has not yet garnered widespread attention. Thus, noting the limited number of seismic response comparisons for underground structures and the virtual absence of comparative analysis between above-and below-ground infrastructures in the scientific literature, this work aims to address this research gap. Nevertheless, data scarcity strongly hampers this endeavour. Not only do very few tunnels have permanent dynamic monitoring systems installed, but even fewer recorded major earthquakes are in proximity to similarly instrumented bridges and viaducts. This study focuses on three infrastructures of the San Francisco Bay Area: the Bay Bridge, the Caldecott Tunnel and the Transbay Tube. The chosen infrastructures represent a unique combination of nearby, continuously monitored case studies in a seismic zone. Yet, even for these selected infrastructures, few comparable data are available-e.g., only one earthquake was recorded for all three. Hence, a Conditional Generative Adversarial Network (CGAN) technique is put forward as a strategy to build a hybrid dataset, thereby incrementing the available data and overcoming the data scarcity issue. The CGAN can generate new data that resemble the real ones while simultaneously comparing different datasets via binary classification. With this dual objective in mind, the CGAN algorithm has been applied to various cases, varying the input given in terms of selected acquisition channels, infrastructure pairs, and selected strong motions. In conclusion, each pair underwent a postprocessing phase to analyse the results. This research's outcomes show that the classifications performed with the Support Vector Machine reached excellent results, with an average of 91.6% accuracy, 93.1% precision, 93.3% recall, and 92.9% F1 score. The comparison in the time and frequency domain confirms the resemblance.

Rockfall events are expected to rise throughout the future due to climate change and extreme meteorological events. In the perspective of climate change adaptation, an accurate quantification of the risk is needed, together with a precise assessment of the effectiveness of protective measures eventually installed. All the possible block detachment scenarios together with their occurrence probability should be considered, and a time span should be selected. A fully probabilistic framework to compute the risk in absence and in presence of a protective structure is herein proposed, and a time-integrated reliability-based method, developed by the authors, is applied to define the failure probability of the protective measure. The complete method, in absence and presence of a rockfall barrier, is applied to a study case, and the residual risk in presence of the barrier is quantified. The results show the importance of considering all the possible detachment situations to have reliable results in terms of both risk and effectiveness of the protective measure quantification.

Structural health monitoring of aerostructures often faces challenges identifying damage, especially in complex systems. Multi-input multi-output modal parameter identification methods are known to offer enhanced insight compared to single-input multi-output testing, as they allow for the identification of additional out-of-plane modes. The improved Loewner Framework presents a computationally efficient approach to extracting these modal parameters, focusing on natural frequencies and mode shapes as indicators of structural health. To address the challenges of damage detection, a numerical case study involving a cantilever beam with variable cross-sections is used to simulate various damage scenarios. Additionally, a full-scale experimental dataset from the BAE Hawk T1A trainer jet aircraft is employed for SHM for the first time. The modified total modal assurance criterion (MTMAC) is proposed as a standalone metric for assessing damage severity, while the coordinate modal assurance criterion (COMAC) is applied for localising damage. Benchmarking against methods such as least-squares complex exponential (LSCE) and stochastic subspace identification with the canonical variate analysis (SSI-CVA) demonstrates the effectiveness of the improved Loewner Framework in accurately identifying even small changes in modal parameters. The MTMAC and COMAC are shown to be valuable tools for, respectively, damage quantification and localisation.

Fully embedded and spatially diffuse sensors are central to the advancement of civil and construction engineering. Indeed, they serve as an enabling technology necessary for addressing the current challenges associated with through‐life management and structural health monitoring of existing structures and infrastructures. The need to identify structural issues early on has driven the integration of such embedded sensing capabilities into construction materials, turning passive structures into proactive, self‐aware “entities,” commonly referred to as Smart Structures. The economic rationale behind this endeavor is underscored by the vital significance of continuous monitoring, which enables prompt anomaly assessment and thus mitigates the risks of potential structural failures. This is particularly relevant for road and rail infrastructures, as they represent a substantial and enduring investment for any nation. Given that a large majority of these large infrastructures are composed of concrete and reinforced concrete, both academics and construction companies are continuously researching micro‐ and nano‐engineered self‐sensing solutions specifically tailored for this building material. This comprehensive review paper reports the latest advances in the field of self‐sensing concrete as of 2024, with an emphasis on intrinsic self‐sensing concrete, that is, electrically conductive functional fillers. A critical analysis and a discussion of the findings are provided. Based on the perceived existing gaps and demands from the industry, the field's future perspectives are also briefly outlined.

Building resilient infrastructure is at the core of sustainable development, as evidenced by the UN Sustainable Development Goal 9. In fact, the effective operation of road networks is crucial and strategic for the smooth functioning of a nation’s economy. This is also fundamental from a sustainability perspective, as efficient transportation networks reduce traffic, and thus, their environmental impact. However, road networks are constantly at risk of traffic closure and/or limitations due to a plurality of natural hazards. These environmental stressors, among other factors like aging and degradation of structural materials, negatively affect the disaster resilience of both single components and the system of road networks. However, the estimation of such resilience indices requires a broad multidisciplinary vision. In this work, a framework for application to large road networks is delineated. In the proposed methodology, seismic hazard is considered, and its corresponding impacts on road networks are evaluated. The assessment encompasses not only the road network system (including squares, roads, bridges, and viaducts) but also the buildings that are located in the urban area and interact with the network. In this context, the probability that buildings will suffer seismic-induced collapse and produce partial or total obstruction of roads is considered. This scheme is designed for implementation in different geographical contexts using geo-referenced data that include information about specific risks and alternative rerouting options. The proposed methodology is expected to support the mitigation of functionality loss in road networks after disasters, contributing to both the economic and social dimensions of sustainability. To evaluate the methodology, two case studies focusing specifically on hospital-to-hospital connections were conducted in Naples and Turin, Italy. However, the proposed approach is versatile and can be extended to other critical infrastructures, such as theatres, stadiums, and educational facilities.