Height-Dependent Seismic Vulnerability of Masonry Buildings: Validation of Fragility Screening Equations Using Post-Earthquake Data
DOI:
https://doi.org/10.15157/IJITIS.2026.9.2.1135-1185Keywords:
Seismic Vulnerability, Fragility Curves, Masonry Buildings, Height Effects, Incremental Dynamic AnalysisAbstract
The 2019 Mw 6.4 Durrës earthquake in Albania caused 51 fatalities due to the collapse of unreinforced masonry (URM) structures and revealed a pronounced height-dependent vulnerability pattern not captured by conventional single-curve fragility models. This study presents a statistically validated, height-stratified fragility framework for URM buildings, demonstrating the need to explicitly account for building height as a primary fragility parameter and quantifying the prediction errors incurred when it is neglected. The framework is developed using 307 laboratory material characterization tests and a post-earthquake dataset of 187 structures. Its validity is assessed through post-earthquake field observations, equivalent frame modeling in TREMURI, and incremental dynamic analysis (IDA) of five height-based archetypes subjected to a suite of 22 ground motion records. Results show that increasing building height significantly reduces normalized lateral strength (Vmax/W = 0.584 − 0.072N; R² = 0.89, RMSE = 0.023, p < 0.001), while displacement demand increases nonlinearly with height (δ ∝H¹.⁸²). A five-story building exhibits approximately 56% lower strength and up to 16 times higher displacement demand compared to a single-story structure. Drift concentration at the ground floor (CFdrift = 1.32 + 0.46N) promotes soft-story collapse mechanisms, with drift amplification increasing from 1.68 to 2.50 between one- and five-story buildings, highlighting the limitations of pushover analysis for N ≥ 4. The probability of collapse under the design earthquake ranges from less than 1% for single-story buildings to 45% for five-story structures. The proposed height-dependent screening criteria reduce damage-grade prediction error (RMSE) by 42–65% compared to three state-of-the-art methods, demonstrating improved predictive performance.
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Copyright (c) 2026 Enio Deneko, Altin Bidaj, Marjo Hysenlliu, Kleandro Koka

This work is licensed under a Creative Commons Attribution 4.0 International License.


