Multiclass seismic damage detection of buildings using quantum convolutional neural network

Abstract

The traditional visual inspection technique for damage assessment of buildings immediately after an earthquake can be time-consuming, labor-intensive, and risky. Numerous studies have been carried out using deep learning techniques, particularly convolutional neural network (CNN), to evaluate the damage to building structures after an earthquake using buildings’ damage images. Quantum computing, on the other hand, is a computing environment that can exploit superposition and entanglement, which are not available in classical computing environments, to achieve higher performance using parallelism between qubits. This paper presents a novel quantum CNN (QCNN) approach to detect damage to reinforced concrete (RC) buildings from images after the earthquake. The QCNN model is developed and trained using the RC building damaged images collected from past earthquakes. The performance of this model is evaluated based on the multiclass damage detection ability of the real-world RC building damaged images collected from the recent earthquake in Turkey in February 2023. Furthermore, the seismic damage detection accuracy obtained from the QCNN model is compared with various CNN architecture results.