A statistical data-based approach to instability detection and wear prediction in radial turning processes

Pavement maintenance management poses a significant challenge for highway agencies in terms of pavement deterioration over time and limited financial resources to keep the road condition at an acceptable level. In this paper two probabilistic maintenance models are proposed and compared for pavement deterioration and maintenance processes to evaluate different maintenance strategies. Firstly, the states of pavement condition are defined using the features of different pavement maintenance works, instead of using the traditional method of cumulative service index rating. Secondly, a Markovian model is presented to describe the pavement deterioration and maintenance process with some constraints on the number of interventions, the effect of interventions and etc. But for the complex scenarios, such as non-Markovian deterioration, dependencies between the different types of interventions and the usage of emergency maintenance for roads when the required budget for maintenance is unavailable, a simulation-based Petri-net model is built up to investigate the whole life-cycle evolution. Two examples are used to illustrate and compare the proposed models to demonstrate the merits and disadvantages of each model and its applicable conditions.