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The development of an effective strategy for the inspection and monitoring of the nation's critical bridges has become necessary due to aging, increased traffic loads, changing environmental conditions, and advanced deterioration. This article presents the development of a probabilistic mechanistic modeling approach supported by durability monitoring to obtain improved predictions of service life of concrete bridge decks exposed to chlorides. The application and benefits of this approach are illustrated on a case study of a reinforced concrete barrier wall of a highway bridge monitored over 10 years. It is demonstrated that service life predictions using probabilistic models calibrated with selected monitored field data can provide more reliable assessments of the probabilities of reinforcement corrosion and corrosion-induced damage compared to using deterministic models based on standard data from the literature. Such calibrated probabilistic models can help decision makers optimize intervention strategies as to how and when to repair or rehabilitate a given structure, thus improving its life cycle performance, extending its service life and reducing its life cycle cost. [ABSTRACT FROM AUTHOR]

This article proposes a methodology for predicting the time to onset of corrosion of reinforcing steel in concrete bridge decks while incorporating parameter uncertainty. It is based on the integration of artificial neural network (ANN), case-based reasoning (CBR), mechanistic model, and Monte Carlo simulation (MCS). A probabilistic mechanistic model is used to generate the distribution of the time to corrosion initiation based on statistical models of the governing parameters obtained from field data. The proposed ANN and CBR models act as universal functional mapping tools to approximate the relationship between the input and output of the mechanistic model. These tools are integrated with the MCS technique to generate the distribution of the corrosion initiation time using the distributions of the governing parameters. The proposed methodology is applied to predict the time to corrosion initiation of the top reinforcing steel in the concrete deck of the Dickson Bridge in Montreal. This study demonstrates the feasibility, adequate reliability, and computational efficiency of the proposed integrated ANN-MCS and CBR-MCS approaches for preliminary project-level and also network-level analyses. [ABSTRACT FROM AUTHOR]

Building maintenance management involves decision making under multiple objectives and uncertainty, in addition to budgetary constraints. This article presents the development of a multiobjective and stochastic optimization system for maintenance management of roofing systems that integrates stochastic condition-assessment and performance-prediction models with a multiobjective optimization approach. The maintenance optimization includes determination of the optimal allocation of funds and prioritization of roofs for maintenance, repair, and replacement that simultaneously satisfy the following conflicting objectives: (1) minimization of maintenance and repair costs, (2) maximization of network performance, and (3) minimization of risk of failure. A product model of the roof system is used to provide the data framework for collecting and processing data. Compromise programming is used to solve this multiobjective optimization problem and provides building managers an effective decision support system that identifies the optimal projects for repair and replacement while it achieves a satisfactory tradeoff between the conflicting objectives. [ABSTRACT FROM AUTHOR]