Resilient maintenance of carbonation-affected concrete infrastructure via physics-informed learning and predictive strategy

Abstract

Climate change accelerates carbonation in concrete, raising risks of cracking and spalling. However, existing model formulations often oversimplify this impact, inadequately representing the intrinsically non-linear and phase-dependent behavior of the carbonation process. In this paper, we propose a novel framework that integrates Physics-Informed Neural Networks (PINNs) and a Predictive Markov Decision Process with Phase-type Approximation (PMDP-PH), enabling resilient and cost-effective infrastructure maintenance under carbonation risks. PINNs embed governing physical laws within neural architectures, enabling accurate inference of carbonation dynamics even under limited observational data. This framework accommodates non-exponential sojourn time distributions in both the initiation and propagation phases, effectively approximated using hypo-exponential models. The PMDP-PH adaptively updates inspection and maintenance strategies by continuously refining the remaining useful life (RUL) distribution in real time. This decision-making process is formulated as a tractable multi-stage model predictive control (MPC) problem over selected belief states, ensuring a non-decreasing value function throughout the robust horizon. Applied to a representative infrastructure system, our method reduces total maintenance costs by up to 61.9% compared to benchmark strategies under variable deterioration scenarios. These findings highlight the promise of combining physics-informed learning with a new form of predictive control strategy to strengthen infrastructure resilience under the growing threat of carbonation-induced deterioration.