3D point-cloud data corrosion model for predictive maintenance of concrete sewers

Predictive maintenance decisions can promote resilient sewers, however, interpretable and accurate corrosion predictions are challenging because of the dynamics of corrosion stages and environmental conditions. In this paper, a 3D point-cloud data-based Bayesian model updating approach is presented to predict the critical parameter evolution of concrete sewer corrosion. The proposed approach adopts a novel distribution-based updating strategy to address the multivariate and asymmetric nature of massive point-cloud data. The effectiveness of the proposed method is investigated using two publicly available sewer corrosion datasets from Perth, Australia and Texas, USA. The Perth case results show that critical parameters after Bayesian updating have the same trends as the in situ monitoring data, which provides interpretability for ultimate decision-making. The Texas case results show that the proposed framework enables more accurate service life predictions than the non-updated Pomeroy model. The proposed approach achieves interpretable and intelligent decision-making, contributing to improved sewer predictive maintenance.