Transverse Connectivity and Durability Evaluation of Hollow Slab Bridges Using Surface Damage and Neural Networks: Field Test Investigation

Abstract

Prefabricated concrete hollow slab bridges are widely used in short- and medium-span highway bridges in China due to the advantages of high production quality, installation convenience, and low construction cost. Field investigation shows that severe hinge joint damage occurred during the service life, and mechanical performance of the bridges also deteriorated with the weakened joints. It is important to accurately evaluate the performance of hollow slab bridges to ensure the safety of the highway system. In this paper, transverse connectivity and durability of the concrete hollow slab bridges are investigated in a field test using the surface damage and neural networks. Hollow slab bridges in the Wu-He highway system were taken as the background bridge. Surface damage was visually checked and statistically analyzed. Static load test was conducted to evaluate the transverse connectivity of the hinge joints based on the girder responses. The hollow slab bridges were then demolished, and a total of 75 concrete girder segments were cut off. Durability of the girders was evaluated based on the conditions of concrete and rebars, and the analytic hierarchy process along with the fuzzy comprehensive evaluation method was employed. Results showed that there were two main types of the defects in the hollow slab bridges, i.e., the transverse cracks on the bottom plates of the girders and the longitudinal cracks in the hinge joints. The distribution of the deflection of each girder was non-uniform due to the weakening of the transverse connectivity, and the girders in the background bridges were within the moderate deterioration condition after 25 years’ service life. An evaluation method of the hollow slab girders using the neural networks and surface damage was verified by the field test data. The maximum crack width at different locations of the bridges was used in the input layer of the neural network, and the hinge joint damage or the durability was considered as the output results. The prediction error of the method in the test set was within 15.0% for the hinge joint damage and within 40% for the durability result of the girder, indicating the feasibility of the evaluation method.