Estimating Seismic and Structural Synthetic Risk Indices Using Fragility Curves and A.I.

This study presents a comprehensive analysis of seismic hazard assessment and building structural vulnerability estimation, focusing on Italy's seismic-prone regions. The investigation comprises four interconnected chapters: literature review, methodology and fitting, Python implementation, as well as a final case study chapter. The literature review delineates the theoretical underpinnings of seismic and structural risk assessment, mentioning various dynamic analysis concepts and risk components. The methodology and curve fitting chapter presents the analytical framework used to model seismic hazard and structural vulnerability, encompassing data pre-processing, distribution fitting, and error analysis. A first level of analysis is done to estimate seismic hazard, followed by a second level of analysis aimed at estimating the structural vulnerability by using fragility curves for various cases of reinforced concrete or unconfined masonry buildings. Fitting was done using A.I. to find a suitable distribution for the fragility curves. The Python implementation section details the practical application of the developed methodology to calculate seismic hazard scores for various assets across Italy. The code integrates fragility curves to estimate building vulnerability and incorporates advanced data visualization techniques to enhance the interpretation of seismic hazard and vulnerability assessment results. In the fourth chapter, the study conducts a pushover analysis of the CRODO school case study. Subsequently, synthetic indices derived from finite element analysis (FEA) are extracted to test the fragility curve results. The study's findings facilitate informed decision-making regarding risk estimation and strategies in seismic-prone regions.