Predictive analytics of bridge safety for intelligent transportation system using ensemble model

Abstract

Bridges are the main links for many transportation systems. Many of the bridges built are subjected to deterioration but are still in use. Factors like heavy weights imposed by vehicles, pressure of water and depreciation effect the lifetime of the bridges. This may lead to various disasters endangering the lives of people. Hence continuous monitoring of bridges using real time bridge data is required to predict bridge safety and alert user passing by the bridge. Existing bridge safety monitoring systems are either Sensor based or uses specialized equipment to monitor safety using frequencies of vibration. Such systems are expensive and have low performance. Traditional prediction algorithms from data mining and regression modeling can help in correlation between input parameters and bridge condition using historical data. They have difficulty either in scaling of data or may not adapt to dynamic changes in input data. Mobile app based system that uses ensemble of both Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) is proposed in this paper to predict bridge safety. This paper signifies on the structural health monitoring of the bridge and the potential advantage of integrating Artificial Intelligence based predictive analytics into IoT sensors to assess the bridge safety. Three different experiments based on ANN, GPR and ensemble of ANN,GPR are made. In the first step, ANN is trained and validated on two datasets with different scenario (Indian and U.S.A). Error reported from this best trained network is used by GPR for statistical analysis of the error distributions. This combined system based mobile app is evaluated using NRMSE, COD and cross entropy loss measures. Test results proved that, the third hyper-parameter values of ensemble model yielded a good result with less error rate than ANN and GPR predictive models.