An ontology-based probabilistic framework for comprehensive seismic risk evaluation of subway stations by combining Monte Carlo simulation

The development of subways is currently at its peak and underground structures including subway stations are being constructed on a large scale. However, previous research on seismic risk evaluation of underground structures mainly focuses on the analysis of influencing factors and seismic fragility. There is a lack of research on comprehensive automated seismic risk evaluation from a macro perspective based on economic losses, casualties, disaster relief costs and post-earthquake recovery time etc. The emergence of semantic web technologies pro-vides an effective way for multi-objective automated seismic risk evaluation of subway stations. This paper aims to develop a framework for comprehensive seismic risk evaluation of subway stations based on Monte Carlo simulation and ontology theory. In the developed framework, the seismic risk probabilities of subway stations are determined using Monte Carlo simulation. Taking economic losses, casualties, disaster relief costs and post-earthquake recovery time as indicators, an ontology-based model for seismic risk evaluation of subway sta-tions is then developed by combining the knowledge base and the semantic web rules to achieve automated evaluations. A case study is also conducted to demonstrate the practicability of the proposed framework and further validate the semantic web rule language and the semantic query-enhanced web rule language

Automated summary

The development of subways is booming, but there is a lack of comprehensive automated seismic risk evaluation research for underground structures. This paper aims to develop a framework using Monte Carlo simulation and ontology theory to evaluate the seismic risk of subway stations based on economic losses, casualties, disaster relief costs, and post-earthquake recovery time. The study demonstrates the feasibility of the proposed framework and validates the semantic web rule language and the semantic query-enhanced web rule language.