Pavement maintenance and rehabilitation decisions derived by genetic programming

The application of genetic programming (GP) to pavement performance evaluation is relatively new. GP was first proposed by John R. Koza as an evolutionary computation technique: a stochastic search method based on the Darwinian principle of `survival of the fittest', whereby intelligible relationships in a system are automatically extracted and used to generate mathematical expressions or `programs'. Nowadays, GP has been used as an important problem-solving method for function fitting and classification. In this paper, an empirical study is performed to develop a pavement maintenance and rehabilitation (M&R) decision model by using GP. As part of the research, experienced pavement engineers from the Taiwan Highway Bureau (THB) conducted pavement distress surveys on seven county roads. For each road section, the severity and coverage of existing distresses that required M&R treatments were separately identified and collated into an analytical database containing 2,340 records. These records were then used to train, validate, and apply the M&R decision model. The finding shows that the total accuracy of the evolved M&R decision model was 0.903, 0.877, and 0.878 for the training, validation, and application data set, respectively. It proves that the GP-based M&R decision model process makes the pavement knowledge extraction process more systematic, easier to use and solvable with a higher probability of success - even for complex M&R decision problems.