Optimisation of pavement maintenance scheduling using transition matrices

Highway networks play a vital role in the nation's economy. With the continuing increase in traffic and road users' expectations of comfort and safety, road authorities are increasingly interested in the upgrading and strengthening of pavements to enhance their service life. However, maintenance of road infrastructures is restricted by a limited budget. In such a situation, effective pavement maintenance scheduling is essential to maintain pavements at the desired level of quality. The performance of pavements is expressed in terms of matrices called transition probability matrices (TPMs). Likewise, pavement condition is expressed as a condition vector, which represents a particular distress (roughness and deflection). In the present study, TPMs for pavement distress and treatment effectiveness are derived from regression models. Pavement condition vectors are obtained from the condition probability distribution as a proportion of each state to which the distress level belongs. After each duty cycle (1 year), the mean distress level is calculated by the expectation of the condition probability distribution. This methodology is implemented in a Microsoft Excel spreadsheet, and optimisation is done using Excel Solver. By adopting the aforementioned procedure, optimisation is performed for scheduling maintenance treatment to a road network.