A bridge structural health data analysis model based on semi-supervised learning

Bridge structural health monitoring is a multi-parameter monitoring for guaranteeing safe construction and service of bridges. Focused on the features of the collected data by various front end sensors, that are reflecting bridge structural health state such as strain, vibration, distortion, cable tension etc., a bridge structural health data analysis model is established in this paper, based on semi-supervised learning which classifies diversified parameter data, and using classifier under various learning patterns, to conduct classification of two types of sample set respectively, on which analysis is done so as to diagnose the bridge structural damage degree and provide evidence and guidance to bridge maintenance and management decision taking.