Abstract:With the road construction from the incremental mode to the stock mode in China, the era of large-scale road management and maintenance has arrived. The application of perception devices to acquire the data and to evaluate the pavement service resilience is a research trend in the context of the strategy for building a strong transportation nation. Firstly, the internal and external factors affecting the pavement service resilience are analyzed, and the limitations of existing indicators for assessing the pavement service resilience are evaluated. On this basis, the composition of a multi-element perception system of pavement service is pointed out, mainly including the traffic load perception, pavement environment perception, subgrade condition perception and pavement service perception. And based on the types of perception equipment, the input parameters, output parameters and relevant technical approaches based on deep learning neural networks are discussed. The new idea is studied and developed for the long service life of road in order to offer the valuable guidance for achieving the overall process, intelligent and proactive management of road health, and enhancing the resilience of road infrastructure.
