Influence of material properties on the seismic response of masonry buildings

Performance of URM structures under earthquake shakings: Validation using a template building structure by the 2019 Albanian earthquakes

This paper aims to study the seismic performance of school buildings, which have been built in accordance with template unreinforced masonry [URM] school projects in Albania. For this purpose, the most widely used two template designs which were damaged during the 2019 Durrës (Albania) Earthquakes, have been selected. Analytical models of each school were prepared following the experimental data on the quality of the masonry constitutive components of the selected school buildings. Geotechnical investigations were deployed to obtain the soil characteristics of the area where the schools’ foundation are located. Nonlinear static analyses have been performed to obtain the seismic capacity, the performance point and the damage level states. The performance-based method has been used to that purpose. The detailed examination of capacity curves and performance evaluation identified deficiencies and weak parts of the school building blocks. Results have shown that existing school buildings constructed pre-modern codes are far from satisfying the required performance criteria, suggesting that urgent response and necessary measure should be put into action.

The spatial pattern of building damage produced by an earthquake carries information that classical building-by-building vulnerability indices cannot capture. This study presents one of the first frameworks to use Topological Data Analysis (TDA), a set of methods that quantify the “shape” of data, to characterise the spatial topology of seismic damage across an urban building inventory. Using the geo-referenced centroids of buildings as a point cloud, a sequence of connectivity graphs (a Vietoris–Rips filtration) is built at increasing distance scales, and persistent homology is used to track which spatial features appear and disappear. From this we extract four interpretable descriptors: Betti numbers (the numbers of connected building clusters and of enclosed gaps), persistence entropy (a measure of how disordered the damage pattern is), total persistence (the combined lifespan of all topological features), and the Wasserstein-2 distance (how far the post-earthquake pattern has moved from the intact pre-earthquake pattern). These descriptors form a physics-informed feature vector that is used to predict the building-cluster damage state. The developed framework was trained, tested, and validated on 1490 buildings over seven post-earthquake scenarios. Lognormal fragility parameters were estimated with maximum likelihood estimation, and an Artificial Neural Network (ANN) and a Random Forest (RF) were retrained on the same 593-building training dataset for comparison. On the 847-building benchmark, the TDA framework reached 93.3% accuracy (95% CI: 91.4–94.9%), F1 = 0.921 (0.902–0.940), and AUC = 0.933, using a stratified 70/15/15 split (training = 593, validation = 127, test = 127). This is a 6.0-percentage-point gain over the retrained ANN and a 12.1-percentage-point gain over the HAZUS-MH index (McNemar p = 0.017). Damage was recorded on the six EMS-98 states DS0–DS5, with DS4 and DS5 merged into a single class to give a five-class taxonomy, and building-type-specific inter-storey drift ratio thresholds were validated against EN 1998-3 (Eurocode 8 Part 3). Exact Rips computation is practical only for clusters up to about 2000 buildings; for larger populations, a CGAL (Computational Geometry Algorithms Library)-based sparse approximation with O(N log N) cost is recommended. It seems that the topological descriptions of the damage field may provide predictive information above and beyond that given by density and ground motion intensity and offer a reproducible tool for post-earthquake screening.