Giuseppina Uva

1. The structural analysis of historical masonry arch-bridges (Figure 1) is typically conditioned by multiple sources of uncertainty that can affect the reliability of the results, including the in...

Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to solving hard engineering problems, including that of structural health monitoring (SHM). SHM consists of automating the condition assessment task of civil, health, mechanical, and aerospace systems using measurements obtained from temporary or permanently installed sensors. Often, the systems of interest are geometrically large and/or technically complex, which complicates the development and application of physics-based methods. It follows that AI is seen as a key potential contributor enabling SHM in field applications for data-driven analysis. As with many research endeavors, many concepts using AI for SHM have been explored in the literature. Nevertheless, very few AI methods have been deployed in the context of SHM, which may be due to the lack of available data supporting their capabilities, limited integrated AI-SHM systems capable of providing results to users and operators with decision-making capabilities, or certification of AI methods for safety-critical applications. The objective of this Roadmap publication is to discuss the integration of AI at the system level enabling SHM, including associated challenges and opportunities such as those found in common metrics of concern (e.g. transparency, interpretability, explainability, security, certifiability, etc), with a particular focus on providing a path to research and development efforts that could yield impactful field applications. The overview of available methods and directions will provide the readers with applicability of AI for certain SHM designs (software), availability of common data sets for further AI comparisons (data), and lessons learned in implementation (hardware).

Bridge risk assessment is recognized as a fundamental part of the process for guaranteeing the structural and seismic reliability of transport networks. To achieve this goal, a variety of strategies are being used around the world, and in 2020, specific guidelines were issued in Italy that provide a homogeneous, standardized multi-level and multi-hazard approach to risk classification and monitoring to be applied to existing bridges by all road managers. The first step of the approach is the preliminary screening of bridge assets based on essential typological and geometric data and periodic inspections. This allows for the continuous and up-to-date monitoring of infrastructure works, filtering out situations that require special attention and prioritizing investigations and actions at the next level. Although the effort required is lower than that required for more advanced assessment procedures, it remains considerable, as demonstrated by the field experience gained through the collaboration between FABRE and ANAS S.p.A. and reported in this study. Based on the data systematically collected on the field and organized in a specific database, the study presents a statistical characterization of an inventory of 746 existing bridges, including descriptive statistics for the key classification parameters of the Italian Guidelines. Through this large sample, one of the largest among similar studies, the research first presents an in-depth analysis and characterization of the most common bridge typologies in Italy. In the next part of the research, statistical methods were applied to critically analyze the possible calibration of the structural-foundational and seismic risk classification as the actual boundary conditions of some parameters involved in the classification logic of the Italian Guidelines vary. In particular, the parameters: Average Daily Traffic; Average Daily Truck Traffic; presence of Road alternatives; Strategic importance of the bridge have been taken into account, evaluating a series of multiple independent scenarios on which the impact on the structural and seismic risk classification within the stock was analyzed. The results highlighted how boundary conditions could affect the risk classification and confirmed the role of other parameters (e.g., Defectiveness class) into the risk classification. Thanks to the breadth, richness of data and representativeness of the available sample of bridges, the statistical analysis and sensitivity assessment presented can be considered a useful basis for calibrating future improvements in the Italian Guidelines calculation process.

A numerical model based on the heuristic molecule (HM) concept is proposed to evaluate the in-plane and Coulomb-like shear behavior of masonry panels. The model extends the well-established Rigid-Body-Spring Model (RBSM), which demonstrated good effectiveness in the seismic analysis of masonry structures. The proposed advancement introduces two diagonal bond-springs specifically designed to improve the representation of shear damage mechanisms. The performance of this enhanced formulation was assessed through numerical simulations of small-scale shear panel tests experimentally tested in the literature under varying levels of pre-compression, for which dedicated nonlinear stress–strain laws for axial, shear, and diagonal bond-springs were implemented. The results indicate that the proposed model provides an accurate description of the observed behavior while maintaining a limited number of degrees of freedom, thus ensuring computational efficiency. These promising outcomes highlight the model’s potential for future applications, including large-scale dynamic analyses.