The Development of a Diagnostic Approach to Predicting the Probability of Road Pavement Failure

Abstract

Road maintenance planning, an essential component of road asset management, preserves the integrity of road networks. Current state of the art pavement management systems exercise optimisation tools, pavement deterioration models, and intervention criteria to forecast the future maintenance requirements of a road network. These tools have been utilised to forecast future maintenance requirements of road networks; however, with this current approach to pavement management, uncertainties associated with the failure of individual sections of road may not always be accounted for explicitly, and therefore the susceptibility of a road network to failure is unknown. Predicting the probability of the end of life of a road pavement involves wholly understanding possible modes of failure and utilising suitable computational techniques, so that engineering knowledge can be well represented in data driven models. To this end, this thesis describes the development of a diagnostic approach that infers engineering knowledge into computational models, to quantify the probability of failure of road pavements and identify the most likely causes of failure. To do so, this research developed a number of failure charts that capture engineering knowledge, such as citing influential failure factors of road pavements including the influence from external environments and internal pavement attributes. Engineering knowledge on road pavement failure was obtained from three sources: literature describing the fundamentals of pavement design and common causes of road failure, expert knowledge from the industry identifying relationships between failure mechanisms and causes, and a data analysis to obtain site-specific causes such as road environments and material properties. Each chart presents a possible failure path, detailing a set of factors contributing to failure. A comparative study evaluated the performance of five classification modelling approaches in order to determine the most suitable technique for this research. Based on performance and user interpretability criteria, the study identified one based on support vector machines as the most suitable. The developed prototype system, consisting of a failure system for rutting, fatigue cracking, and shear, performed well in both the development phase and network testing of the system utilising data from the New Zealand Long-term Pavement Performance Programme. A case study focussing on rural New Zealand roads was carried out, which demonstrated the use of this tool in network and project level applications.