Quantum Deep Learning for Structural Health Monitoring of Bridges and Buildings

Abstract

Environmental impacts can cause brace damage, cracking, loss of stiffness, and other damage to buildings, bridges, and frame structures. By identifying damage early on, Structural Health Monitoring (SHM) technologies could avert catastrophic disasters. Deep Learning (DL), which has advanced quickly in recent years, has been used in SHM to efficiently extract features in order to identify, locate, and assess various damages. In order to guarantee the stability and long-term serviceability, structural health monitoring (SHM) of buildings and bridges is crucial. However, traditional deep learning approaches encounter difficulties with enormous amounts of sensing data, computational expense, and scaling in large infrastructure structures. This research introduces a quantum deep learning (QDL) paradigm that takes advantage of the representational and parallelism benefits of quantum computing for structural health monitoring. The suggested method combines quantum feature encoding, deep variational quantum circuits, and classical data from sensors preprocessing for damage detection and architectural condition evaluation. In order to improve feature learning performance while lowering model complexity, a hybrid fundamental–classical architecture is created in which quantum layers are integrated into deep neural networks. Benchmark vibration and strain datasets from viaduct and construction structures under various damage scenarios are used to test the framework. According to experimental findings, the suggested quantum deep neural network model performs better than traditional deep learning techniques in terms of computational efficiency, resistance to noise, and detection accuracy. The results show that next-generation autonomous monitoring systems for structural health in civil construction have a promising future thanks to quantum deep learning.