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During their lifetime, sewer networks face risks of infiltration or exfiltration, restriction of flow capacity, and loss of structural integrity. These factors individually or combined can impact the public health and the environment. This study is aimed at analyzing uncertainties contributing to the service life performance of sewer pipes under compounding effects of corrosion deterioration and traffic loads using a global sensitivity analysis (GSA). The specific goals are to identify significant sources of uncertainty, and to quantify and rank their time-varying significance for extended time horizons. The study considers a comprehensive set of uncertain environmental, structural, and geotechnical variables. Results indicate that among all the material properties, tensile and compressive strength of concrete are the most influential parameters over the entire service life of sewer pipes. According to the results, the significance of environmental variables varies considerably with time. Dissolved sulfide concentration and pH-dependent factor for the proportion of H 2 S in wastewater significantly affect infiltration/exfiltration for pipes that are older than 30 years. Variables contributing to truckload including the truck weight and multiple presence factor are also found to be significant. [ABSTRACT FROM AUTHOR]

Existing bridge performance metrics are limited in their ability to objectively reflect the safety and serviceability of bridges and identify effective Maintenance, Repair and Replacement (MR&R) actions for a large number of bridges in the face of budget constraints. This study presents a comprehensive optimal budget allocation framework with an integer linear programing formulation for a portfolio of bridges based on element-level inspection data. Considering budget constraints, this method determines optimal MR&R actions at element-level such that agency and user costs of repair actions required to have bridges in their like-new state at the next budget allocation year are minimized. The framework incorporates structural safety risks via the probability that the designated structural functionality of a bridge is or deemed to be compromised. The approach is applied to 484 National Highway Systems bridges in district 3 of Ohio. Results indicate that the proposed method can identify work plans comprising the optimal set of MR&R actions that maximize the network-level performance of bridge portfolios, while satisfying budgetary constraints. It is observed that this method consistently assigns higher priority to work plans that reduce structural safety risks of bridges, and to bridges with high traffic demands and long detour lengths. [ABSTRACT FROM AUTHOR]

Identifying key variables that influence crack formation in buried concrete sewer pipes and impact their structural integrity is crucial for evaluating the leakage and collapse vulnerability of these underground structures. The present study performs a comprehensive global sensitivity analysis for these critical pipe responses considering various uncertainties associated with material properties and loading. A high-fidelity three-dimensional nonlinear Finite Element (FE) model of a typical pipe is developed and used to capture pipe behavior. Considering the significantly high computational demand of the generated FE model, a surrogate model using Bayesian Additive Regression Trees (BART) is developed to accurately approximate input-output relationships in this high-dimensional problem. Significant variables are subsequently determined via a global sensitivity analysis based on Sobol's theorem through the application of Markov Chain Monte Carlo simulation to the trained BART model. Results indicate that concrete and backfill soil properties and variables associated with truckloads contribute significantly to the variation of key pipe responses, thereby affecting the likelihood of leakage and collapse. Findings of this study can benefit the design and management of sewer pipes in large wastewater networks and significantly reduce the complexity of assessing the reliability of sewer pipes. [ABSTRACT FROM AUTHOR]

Probabilistic life‐cycle cost (LCC) analysis facilitates optimal management of infrastructure systems under uncertainty. Despite recent advancements, accurate and efficient estimation of the LCC of systems facing risk of exposure to multiple stochastic occurrences of hazards has remained a significant challenge. This paper proposes a Markovian LCC framework that substantially reduces the often very high computational cost of probabilistic LCC analysis especially for large systems. The presented methodology also enhances the accuracy of LCC estimates in multiple fronts. It captures damage accumulations due to incomplete or no repair actions over multiple stochastic hazard exposures in a system's lifetime. Moreover, it tracks the state of infrastructure vulnerability during repair processes and incorporates uncertainties in downtimes associated with damage states of the system. Finally, it estimates the entirity of loss induced by a hazard and avoids the truncation error that exists in annual loss estimates for scenarios where repair downtimes are greater than one year. With these new capabilities, the proposed LCC framework is applied to a bridge in a seismic zone to estimate the LCC and analyze complex interactions of stochastic hazards and their effects with recovery efforts. Results indicate that neglecting effects of damage accumulation and partial improvements in the course of repair can lead to 20% underestimation and 14% overestimation of the expected hazard‐induced LCC, respectively. [ABSTRACT FROM AUTHOR]