Risk-based bridge asset management

Bridges, critical components of a nation’s infrastructure network, are vulnerable to deterioration from aging, fatigue, and external events like earthquakes or impacts. These challenges, coupled with limited maintenance resources, create the need for efficient and expeditious predictive maintenance strategies. The present thesis focuses on the development and application of Decision Support Systems (DSS) for the optimization of bridge stocks management and upkeep. The developed DSS introduces a streamlined bridge risk assessment methodology that leverages structural reliability and advanced probabilistic models to perform risk projections, accounting for the degradation in both the short and long-term. The methodology is based on the general definition of risk as the combination of Hazard, Vulnerability – jointly computed by means of structural reliability – and Exposure. Within the structural reliability evaluation, two key components are integrated into the proposed procedure: a Markov Chain-based degradation model that forecasts the progression of existing damage, and a Bayesian model that predicts the appearance of new structural defects over time. Hence, the DSS provides a projection of structural reliability over different timeframes. Within the context of future bridge maintenance interventions, such projected reliability, combined with the potential consequences of the collapse (Exposure) and intervention costs, computes a Priority Index for each possible intervention scenario. The result is a cost-efficient ranking system that enables data-driven decisions, optimizing both maintenance priorities and resource allocation. The efficiency of the DSS is demonstrated through practical examples on real life bridges, simulating the daily tasks of an infrastructure manager. In addition, contextually to the inclusion of railway bridges in the DSS, the thesis addresses the need for streamlined procedures for their swift risk assessment. A quantitative method is proposed to calculate in an expeditious and low-input manner the structural risk of railway bridges by integrating once again structural reliability and exposure. Specifically, for the evaluation of the former for aging masonry arch railway bridges, which constitute a significant portion of the Italian railway infrastructure stock, a simplified approach is introduced to estimate their load-carrying capacity based on minimal structural data: span, rise-to-span ratio and design code. This method applies the Static Theorem to determine the most conservative geometry compatible with the original design code, estimating the load rating factor with respect to modern freight loads. A parametric analysis is conducted for various spans, rise-to-span ratios, and design codes, with results presented in easy-to-use charts and practical guidelines to help railway operators rank their bridges based on capacity deficits. This research advances the field of bridge management by offering sophisticated yet practical tools that enhance decision-making, ensuring safer and more efficient management of aging infrastructure.