An efficient hybrid method of uncertainty estimation on defective pipe diagnosis in urban drainage system

Urban drainage system (UDS) plays an import role in city urbanization. Defective pipes in UDS can lead to unanticipated damages such as blockage or seepage. Previous studies have identified the locations of defects in UDS using inverse optimization models. However, these studies overlook the uncertainty introduced by errors in monitoring and simulation. In addition, the multi-point defect problem is computationally slow for Markov chain Monte Carlo methods due to high dimensional parameters space. To address these issues, the paper employs a hybrid approach on USEPA Stormwater Management Model, leveraging the efficiency of a genetic algorithm (GA) to identify an optimal solution space and the precision of an adaptive Metropolis (AM) algorithm to yield a dependable estimation of the posterior probability distribution (PPD). Firstly, a modified multi-population GA is applied to maximize the exploration of the model space, generating an initial PPD. Then, AM algorithm is used to explore the final PPD of each pipe health status variable.

Two UDS cases are used to validate the method. The first case generates 400 sets of randomized multi-point seepage scenarios with monitoring flow sequences. The metrics accuracy and Matthews correlation coefficient are used to evaluate the binary diagnosis performance. The statistical results of metrics suggest that the method is effective in diagnosing the location and seepage extent of defective pipes, including in complex scenarios of multi-point seepage. The effects of seepage location, monitoring error, and data richness on the results are also analysed. In addition, comparison reveals that appropriate GA can maximize the exploration of the model space and attenuate the “genetic drift” effect. The second case considers a UDS with multi-point blockage. The application results suggest that the proposed method offers a comprehensive representation of the PPD on each pipe blockage status. The proposed method is easy to implement and can present uncertainty in the form of probability, which will help to narrow down the scope of defect monitoring and reduce the cost of detection.