Structural Health Monitoring for Flexible Bridge Structures Based on CNN-HMM Approach

Abstract

Wireless smart sensors allow for decentralized data processing, opening up novel approaches to structural health monitoring. Modal analysis and damage detection require a large number of sensor nodes for precision. However, large deployments of most smart sensor technology have been sluggish due to a lack of crucial hardware and software components, despite the fact that most of this technology has been around for almost a decade. Preprocessing, feature extraction, and model training are all used in this suggested method. Filtering and zeroing the data is performed as part of the preparatory procedure. Each part has been fine-tuned to eliminate the effects of dynamic forces, temperature changes, and variations in actual load. To evaluate a structure's health, we use a technique called feature extraction from a system designed to track such things. After the features have been extracted, the models are trained via CNN-HMM. When compared to the two most common alternatives, CNN and HMM, the proposed technique emerges victorious