The Abruzzo earthquake hit the city of L'Aquila and its surroundings on the April 6, 2009. The aim of this study is to analyze the technical features of unreinforced masonry (URM) buildings and assess their seismic behavior during the Abruzzo 2009 seismic event. The damage induced in the URM constructions of L'Aquila and the suburbs was severe and several such buildings collapsed. This study includes an overview of the dynamic characteristics of the earthquake and the seismic history of the region. The seismic performance of URM buildings is discussed on the basis of both the experience in L'Aquila and the village of Castelvecchio Subequo, during the post-emergency support to the Italian Department of Civil Protection (Dipartimento della Protezione Civile [DPC]), and the field investigation carried out with the patronage of the EU COST Action C26 and the cooperation of the PLINIVS Centre of Naples in three areas of the old city of L'Aquila. The main characteristics of URM buildings, the building behavior and damage are described and reviewed with due respect to the characteristics of the earthquake, as well as with reference to the structural and non-structural characteristics of buildings, using the Italian MEDEA procedure.

Concrete has been one of the most essential building materials for decades, valued for its durability, cost efficiency, and wide availability of required components. Over time, the number of concrete bridges has been drastically increasing, highlighting the need for timely structural health monitoring (SHM) to ensure their safety and long-term durability. Therefore, a narrative review was conducted to examine the use of Artificial Intelligence (AI)-integrated techniques in the SHM of concrete bridges for more effective monitoring. Moreover, this review also examined significant damage observed in various types of concrete bridges, with particular emphasis on concrete cracking, detection methods, and identification accuracy. Evidence points to the fact that the conventional SHM of concrete bridges relies on manual inspections that are time-consuming, error-prone, and require frequent checks, while AI-driven SHM methods have emerged as promising alternatives, especially through Machine Learning- and Deep Learning-based solutions. In addition, it was noticeable that integrating multimodal AI approaches improved the accuracy and reliability of concrete bridge assessments. Furthermore, this review is essential as it also addresses critical gaps in SHM approaches and suggests developing more accurate detection techniques, providing enhanced spatial resolution for monitoring concrete bridges.

This paper presents the results of a preliminary study aimed at assessing the crack sealing capacity in chloride environments of different concrete mixtures, incorporating supplementary cementitious materials as well as self-healing enhancing crystalline admixtures. For each addition, also including pulverized fuel ash and silica fume, different contents were taken into consideration. Cylinder specimens were pre-cracked in splitting up to three different crack-opening ranges, simulating different service conditions, and then exposed to different conditioning environments, also containing different concentrations of sodium chloride and including both permanent immersion and wet/dry cycles. Healing conditioning was performed up to three months and crack sealing was visually inspected and quantified via image analysis procedures, monthly. Optimum dosages of each cement substitute/addition were quantified, also considering, besides the healing capacity, also the fresh state performance and compressive strength development. The good performance of mixes with crystalline admixture even under open-air exposure, as well as of other investigated mixes with reference to crack openings and exposure conditions, paves the way to revise the significance of a serviceability design parameter such as the maximum allowable crack width as a function of the exposure with the concept of a sealable crack width.