Design and Implementation of Health Monitoring System for an Airport Terminal Building with a Large-Span Truss Steel Structure

This study investigates the structural health monitoring and stress prediction of large-span steel roof structures in airport terminals, focusing on the impact of temperature variations and the development of an advanced hybrid prediction model. A comprehensive monitoring system was designed and implemented to track key structural responses, including stress, displacement, and temperature, revealing significant correlations between thermal effects and structural behavior. To enhance predictive accuracy, a BO-CNN-LSTM hybrid model was proposed, integrating Bayesian optimization with convolutional and long short-term memory neural networks. The model demonstrated superior performance in capturing spatial–temporal stress patterns compared to traditional methods, providing a reliable tool for real-time structural assessment and early warning. The findings highlight the importance of temperature effects on structural integrity and offer practical insights for the health monitoring of large-span steel structures in complex environments. This study provides a reference for future research on structural health monitoring and performance assessment.