André D. Orcesi

Automated finite element updating using strain data for the lifetime reliability assessment of bridges

The importance of improving the understanding of the performance of structures over their lifetime under uncertainty with information obtained from structural health monitoring (SHM) has been widely recognized. However, frameworks that efficiently integrate monitoring data into the life-cycle management of structures are yet to be developed. The objective of this paper is to propose and illustrate an approach for updating the lifetime reliability of aging bridges using monitored strain data obtained from crawl tests. It is proposed to use automated finite element model updating techniques as a tool for updating the resistance parameters of the structure. In this paper, the results from crawl tests are used to update the finite element model and, in turn, update the lifetime reliability. The original and updated lifetime reliabilities are computed using advanced computational tools. The approach is illustrated on an existing bridge.

A bridge network maintenance framework for Pareto optimization of stakeholders/users costs

For managing highway bridges, stakeholders require efficient and practical decision making techniques. In a context of limited bridge management budget, it is crucial to determine the most effective breakdown of financial resources over the different structures of a bridge network. Bridge management systems (BMSs) have been developed for such a purpose. However, they generally rely on an individual approach. The influence of the position of bridges in the transportation network, the consequences of inadequate service for the network users, due to maintenance actions or bridge failure, are not taken into consideration. Therefore, maintenance strategies obtained with current BMSs do not necessarily lead to an optimal level of service (LOS) of the bridge network for the users of the transportation network. Besides, the assessment of the structural performance of highway bridges usually requires the access to the geometrical and mechanical properties of its components. Such information might not be available for all structures in a bridge network for which managers try to schedule and prioritize maintenance strategies. On the contrary, visual inspections are performed regularly and information is generally available for all structures of the bridge network. The objective of this paper is threefold (i) propose an advanced network-level bridge management system considering the position of each bridge in the transportation network, (ii) use information obtained at visual inspections to assess the performance of bridges, and (iii) compare optimal maintenance strategies, obtained with a genetic algorithm, when considering interests of users and bridge owner either separately as conflicting criteria, or simultaneously as a common interest for the whole community. In each case, safety and serviceability aspects are taken into account in the model when determining optimal strategies. The theoretical and numerical developments are applied on a French bridge network.

Use of Lifetime Functions in the Optimization of Nondestructive Inspection Strategies for Bridges

A model using lifetime functions is used to evaluate the probability of survival of bridge components. The possible outcomes associated with nondestructive inspections (NDIs) are incorporated in an event-tree model. Each time a bridge component is inspected, different decisions can be made. The use of a lifetime function for each component of the structural system enables one to express the probability that the component survives. In theory (i.e., perfect inspection), each NDI should be associated with two possible outcomes: survival or failure. In the first case, no damage is detected and the probability density function of time to failure is updated knowing that the component has survived until the inspection. In the second case, damage is detected and maintenance action is planned. In practice, NDIs are subjected to uncertainties (i.e., imperfect inspections) and detecting or not detecting damage depends on the inspection quality (i.e., probability of detection). For poor-quality inspections, there is a significant risk to overestimate the probability of safe performance. The aim of this paper is to provide a practical methodology for determining optimal NDI strategies for different components of steel bridges. The different types of inspections considered in this paper are visual, magnetic particle, and ultrasonic. An economic analysis is performed and NDI strategies are optimized by simultaneously minimizing both the expected inspection/maintenance cost (i.e., the sum of inspection and maintenance costs) and the expected failure cost. The proposed approach is applied to an existing steel bridge.

Inclusion of Crawl Tests and Long-Term Health Monitoring in Bridge Serviceability Analysis

Due to limited resources, structural health monitoring (SHM) of highway bridges has to be integrated in structural performance assessment in a cost-effective manner. The instrumentation and the long-term SHM procedures are generally chosen with emphasis on most critical bridge components for a particular failure mode. However, global structural analysis is necessary to obtain useful structural performance information. It is then a major challenge to use monitoring data at some locations to perform a structural reliability analysis at other locations. In this paper, a methodology for lifetime serviceability analysis of existing steel girder bridges including crawl tests and long-term monitoring information is presented. The case where the initial goal of monitoring is to provide data for a fatigue analysis of some bridge components is considered. The monitoring results are used to perform a structural reliability analysis of different sections that are critical considering serviceability of the bridge. Limit state equations are used firstly by adhering to the load and strength formulas and requirements set forth in AASHTO specifications, and secondly by integrating monitoring information. Serviceability with respect to permanent deformation under overload is estimated for the girders with these two different methods and a time-dependent performance analysis is conducted by considering corrosion penetration. The proposed approach is applied to the I-39 Northbound Bridge over the Wisconsin River in Wisconsin. A monitoring program of that bridge was performed by the Advanced Technology for Large Structural Systems Center at Lehigh University.

Optimal maintenance strategies for bridge networks using the supply and demand approach

This paper presents a probability-based approach for optimising the management of bridge networks. Most of the Bridge Management Systems are focused on condition features to ensure a minimum safety level for each individual bridge. Their location on the road network, the consequences of inadequate service due to maintenance actions are therefore not taken into consideration. These multiple criteria should be considered when scheduling maintenance activities. To overcome these limitations, a probabilistic supply and demand strategy is proposed for determining the optimal maintenance planning for each interconnected bridge. The problem is solved with genetic algorithms. One objective function is first introduced, corresponding to the summation of all the maintenance, failure, and user costs. Then, two conflicting objective functions are considered, the total user costs and the maintenance and rehabilitation costs. Safety and serviceability aspects are taken into account in the methodology and the theoretical and numerical developments are applied on a part of the French national network.

Bridge Performance Monitoring Based on Traffic Data

There is a huge interest in developing new performance metrics, based on monitoring information and considering uncertainties in the resistance and loading effects, to be used by stakeholders when determining maintenance and rehabilitation strategies for existing bridges. The cost of structural health monitoring is generally a criterion in the duration of the monitoring program, and determining when, and how long, monitoring can be interrupted is still a challenge. Providing an approach that quantifies the benefits of monitoring is crucial for stakeholders. The objective of this paper is threefold: build a performance indicator based on monitoring information that considers uncertainties and correlation in recorded data; use this indicator to check and predict if serviceability and safety thresholds are reached; and analyze the impact of short monitoring interruptions on the performance assessment accuracy by balancing cost of monitoring with the efficiency of the results. The originality of the proposed approach consists in the introduction of a performance indicator in the structural reliability analysis, based on monitoring programs. The regression methodology enables one to apply the reliability analysis when monitoring is interrupted during short-term periods and to assess the impact of such interruptions on the structural analysis accuracy. The proposed approach is applied on an existing bridge.

Baseline-free real-time assessment of structural changes

This article addresses the subject of data-driven structural health monitoring and proposes a real-time strategy to conduct structural assessment without the need to define a baseline period, in which the monitored structure is assumed healthy and unchanged. Independence from baseline references is achieved using unsupervised discrimination machine-learning methods, widely known as clustering algorithms, which are able to find groups in data relying only on their intrinsic features and without requiring prior knowledge as input. Real-time capability is based on the definition of symbolic data, which allows describing large amounts of information without loss of generality or structural-related information. The efficiency of the proposed methodology is illustrated using an experimental case study in which structural changes were imposed to a suspended bridge during an extensive rehabilitation programme. A single-value novelty index capable of describing multi-sensor data is proposed, and its effectiveness in identifying structural changes in real time, using outlier analysis, is discussed.

Optimization of Bridge Management under Budget Constraints: Role of Structural Health Monitoring

Bridge management is a complex engineering issue with public safety and financial implications. It is a challenge to provide efficient bridge management systems, taking into account uncertainties in the structural degradation process, the loads and resistance of the structure, the decisions that are made concerning assessment strategies—such as inspections or structural health monitoring (SHM)—and maintenance and rehabilitation strategies. It is important to determine optimal management strategies that satisfy budget constraints, because efficient use of available financial funds is a primary objective of stakeholders. The purpose of this paper is to provide a probabilistic framework based on SHM results for optimization of management strategies under multiple criteria, including budget availability. To take into account uncertainties in the decision process, an event tree–based approach is used. This enables quantification of the expected inspection, SHM, maintenance and failure costs, and accuracy of the decision process according to the occurrence and duration of future SHM programs. To include budget constraints, the probability distribution of management cost—defined as the sum of inspection, SHM, and maintenance costs—is determined at each decision time and compared with an ideal budget distribution. The proposed approach is applied to an existing bridge.

Calibration of Partial Safety Factors for the Assessment of Existing Bridges

The design codes use safety margins which, in general, exceed those that are reasonable to accept for the assessment of existing bridges. Knowledge about a structure can be improved by further investigations and this can justify modifying partial safety factors. In this context, the Technical Centre for Bridge Engineering (CTOA) of the Technical Department for Roads, Road Safety and Bridges (SETRA) has initiated in 2009 a large study for the calibration of partial factors adapted to the assessment of existing bridges. Based on the reliability theory, its objective is to provide modified partial factors based on tabulated experimental in-situ results. The study is performed on different families of standard bridges but the present paper will only highlight the preliminary results obtained for reinforced concrete slab bridges.

The use of nested sampling for prediction of infrastructure degradation under uncertainty

Abstract Because of the competing demands for scarce resources (funds, manpower, etc) national road owners are required to monitor the condition and performance of infrastructure elements through an effective inspection and assessment regime as part of an overall asset management strategy, the primary aim being to keep the asset in service at minimum cost. A considerable amount of information is then already available through existing databases and other information sources. Various analyses have been carried out to identify the different forms of deterioration affecting infrastructures, to investigate the parameters controlling their susceptibility to, and rate of, deterioration. This paper proposes such an approach by building a transition matrix directly from the condition scores. The Markov assumption is used stating that the condition of a facility at one inspection only depends on the condition at the previous inspection. With this assumption, the present score is the only one which is taken into account to determine the future of the facility. The objective is then to combine nested sampling with a Markov-based estimation of the condition rating of infrastructure elements to put some confidence bounds on Markov transition matrices, and ultimately on corresponding maintenance costs.