ABSTRACT
Currently, a comprehensive management system for transportation networks incorporating bridges and pavements is still missing. In this study, both bridges and asphalt pavements are considered critical components in the connection among all nodes in the network. A framework for estimating the probabilistic life-cycle connectivity of transportation networks is proposed. Specifically, flexural and shear failure modes are considered for bridges. Four failure modes, including international roughness index, rut depth, alligator cracking, and transverse cracking, are considered for asphalt pavement using a neural network-based deep learning (DL) model trained by the Long-Term Pavement Performance (LTPP) database. In the case study of an existing transportation network located in Chester, Pennsylvania, the matrix-based system reliability method is applied to investigate the life-cycle connectivity and component importance based on the obtained reliability associated with individual bridges and asphalt pavement segments. The results show that both bridges and asphalt pavements make a significant contribution to the probability of network connectivity.
