Seismic vulnerability assessment using association rule learning: application to the city of Constantine, Algeria

Abstract

We performed a seismic vulnerability assessment of the city of Constantine (Algeria) using the Risk-UE and datamining-based methods [association rule learning (ARL)]. The ARL method consists in establishing relationships between building attributes (number of stories or building age) and the vulnerability classes of the European Macro-seismic Scale, EMS98. This approach avoids the costly process of drawing up an inventory of building characteristics in the field, which often discourages the assessment of seismic risk initiatives in weak to moderate seismic-prone regions. We showed that the accuracy of the assessment is independent of the subset used for the learning phase leading to development of the Constantine vulnerability proxy. Considering only two attributes, the vulnerability assignment is equal to about 75%, reaching 99% if material is added to the attributes considered. Comparison of Risk-UE and ARL results revealed a reliable assessment of vulnerability, the differences having only a slight impact on the probability of exceeding the damage level computed by EMS98 or Risk-UE in Constantine. The results of this study suggest that the ARL-based vulnerability proxy is efficient and could be applied to the rest of Algeria.