Seismic vulnerability assessment of buildings using rapid visual screening for hybrid UMNN-RFO approach

Abstract

This chapter proposes a hybrid approach for seismic vulnerability assessment of buildings using rapid visual screening (RVS). The Unconstrained Monotonic Neural Network (UMNN) and Red Fox Optimisation (RFO) are combined to create the hybrid technique, thus, the term ‘UMNN-RFO approach’. The UMNN algorithm predicts the RVS score and the RFO algorithm optimizes the RVS score. The RVS methods via Google Maps are being used to examine the Andaman and Nicobar Islands because it is one of the main earthquake prone regions in the world. A score was given after a screening that looked at Reinforced Concrete moment-resisting frames, load-bearing structures, steel frames (SF) and wooden frames (WF). By then, the MATLAB platform will have the proposed model implemented. The proposed approach outperforms all current techniques, including Granular Computing Artificial Neural Network (GrC-ANN), Particle Swarm Optimization-Back Propagation Neural Network (PSO-BPNN) and Genetic Algorithm-Artificial Neural Network (GA-ANN). The proposed method’s accuracy is 90%, determination coefficient is 0.91 and the Root Mean Square error is 0.27%. From the result, the proposed method concluded that older structures are more likely to sustain greater damage shortly than more recently built ones based on the score.