???ViewItemFull_lblHideGroup???
???ViewItemFull_lblSubject???:
???lbl_noEntry???
???ViewItemFull_lblAbstract???:
Earthquakes pose a significant threat to people, communities, and infrastructure due to their devastating nature, necessitating effective seismic hazard and risk assessments. This study focuses on seismic microzonation, a widely used technique for mapping local responses by estimating ground motion amplification at small scales. Italy, which has experienced more than 60 destructive earthquakes in the last two centuries, has conducted extensive microzonation studies to improve understanding of local behavior and optimize emergency response, disaster preparedness, and land use planning. This involved numerical simulations based on geological and geotechnical knowledge of the site to predict the expected amplification effects.
The aim of this research is to systematically compare empirical site amplification derived from strong-motion records with amplification factors obtained from Italian microzonation studies. For this comparison, four statistical similarity measures are employed within a transparent and reproducible testing method to evaluate the shape similarity between the amplification data from 48 selected seismic stations.
All stations were classified into three ranks – A, B, and C – representing high, medium, and low similarity, respectively. The ranking results with nine, 24, and 15 stations assigned to these three ranks, respectively, show clear spatial paterns: stations with low similarity are clustered in the Po Valley, eastern coast, and southern mountains, while those with higher similarity are located in the Apennines and adjacent areas. The developed approach thus demonstrates the effectiveness of integrating multiple similarity measures and establishes a reliable framework for assessing station performances. Through comprehensive analyses which include the investigation of variations in measure weights, the use of four different references, and the integration of additional stations, the method’s robustness in quantifying and ranking shape similarity is further confirmed. Given these findings, additional validation in different seismic regions and with expanded data sets is recommended to improve the applicability and credibility of the method, thereby contributing to increased resilience against seismic risks.
