Bayesian Model Updating and Its Limitations for Detecting Local Damage of an Existing Truss Bridge

Mustafa, Samim; Matsumoto, Yasunao

doi:10.1061/(asce)be.1943-5592.0001044

Cited by 26 publications

(23 citation statements)

References 28 publications

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“…Pedroza Torres et al proposed a hybrid methodology method based on the results of a comparative study between self‐organizing maps and Bayesian networks in order to reduce computational cost and improve performance in fault condition detection of structures; the proposed method can detect damage efficiently based on numerical results of a truss. Mustafa and Matsumoto presented a Bayesian model‐updating method for detecting local damage by introducing a new objective function and a realistic parameterization of the mass and stiffness matrices; the FE model of a real steel truss bridge can be updated effectively using four identified stiffness parameters from the measured vibration data; numerical and experimental results of a real truss bridge show that the method could only detect local damage when the global modal parameters changed significantly.…”

Section: Recent Progress On Damage Identification Methods For Truss Bmentioning

confidence: 99%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

231

112

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Summary Damage identification forms a key objective in structural health monitoring. Several state‐of‐the‐art review papers regarding progress in this field up to 2011 have been published. This paper summarizes the recent progress between 2011 and 2017 in the area of damage identification methods for bridge structures. This paper is organized based on the classification of bridge infrastructure in terms of fundamental structural systems, namely, beam bridges, truss bridges, arch bridges, cable‐stayed bridges, and suspension bridges. The overview includes theoretical developments, enhanced simulation attempts, laboratory‐scale implementations, full‐scale validation, and the summary for each type of bridges. Based on the offered review, some challenges, suggestions, and future trends in damage identification are proposed. The work can be served as a basis for both academics and practitioners, who seek to implement damage identification methods in next‐generation structural health monitoring systems.

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Section: Recent Progress On Damage Identification Methods For Truss Bmentioning

confidence: 99%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

231

112

View full text Add to dashboard Cite

show abstract

“…(2) is study uses some optimization algorithms to improve the performance of the ANN model, and the accuracy, rationality, and speed are better than the traditional back-propagation neural network [40]. (3) is study shows that an estimation of the damage states of many bridges in a few minutes can be achieved, while previous studies usually evaluate the performance of one bridge [21,22] or some components [19]. (4) is method can be used under both preearthquake and postearthquake conditions.…”

Section: Main Contributions and Significancementioning

confidence: 99%

“…Different methodologies have been proposed to assess the fragility [18] or the damage of a bridge. Some of the previous studies have evaluated individual bridges in detail by using different methods, such as multipoint acceleration measurement and ANNs [19], assessing the relative risks of the failure modes of a bridge and the limitations of risk priority numbers (RPNs) associated with individual failure modes [20], as well as the Bayesian method [21]. Some studies present the assessment methods of bridge components [22].…”

Section: Introductionmentioning

confidence: 99%

Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm

Jia

Jun

Liu

2020

Advances in Civil Engineering

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Earthquakes cause significant damage to bridges, which have a very strategic location in transportation services. The destruction of a bridge will seriously hinder emergency rescue. Rapid assessment of bridge seismic damage can help relevant departments to make judgments quickly after earthquakes and save rescue time. This paper proposed a rapid assessment method for bridge seismic damage based on the random forest algorithm (RF) and artificial neural networks (ANN). This method evaluated the relative importance of each uncertain influencing factor of the seismic damage to the girder bridges and arch bridges, respectively. The input variables of the ANN model were the factors with higher importance value, and the output variables were damage states. The data of the Wenchuan earthquake were used as a testing set and a training set, and the data of the Tangshan earthquake were used as a validation set. The bridges under serious and complete damage states are not accessible after earthquakes and should be overhauled and reinforced before earthquakes. The results demonstrate that the proposed approach has good performance for assessing the damage states of the two bridges. It is robust enough to extend and improve emergency decisions, to save time for rescue work, and to help with bridge construction.

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“…16–18 In addition, Bayesian approaches have been used to assess the health state of bridges. 19–21 However, these approaches mainly focus on updating the estimation of the bridge materials property (e.g. the values of the bridge stiffness) and behaviour (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Belief Network method for bridge deterioration detection

Vagnoli

Remenyte‐Prescott

Andrews

2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Bridges are one of the most important assets of transportation networks. A closure of a bridge can increase the vulnerability of the geographic area served by such networks, as it reduces the number of available routes. Condition monitoring and deterioration detection methods can be used to monitor the health state of a bridge and enable detection of early signs of deterioration. In this paper, a novel Bayesian Belief Network (BBN) methodology for bridge deterioration detection is proposed. A method to build a BBN structure and to define the Conditional Probability Tables (CPTs) is presented first. Then evidence of the bridge behaviour (such as bridge displacement or acceleration due to traffic) is used as an input to the BBN model, the probability of the health state of whole bridge and its elements is updated and the levels of deterioration are detected. The methodology is illustrated using a Finite Element Model (FEM) of a steel truss bridge, and for an in-field post-tensioned concrete bridge.

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Bayesian Model Updating and Its Limitations for Detecting Local Damage of an Existing Truss Bridge

Cited by 26 publications

References 28 publications

Recent progress and future trends on damage identification methods for bridge structures

Recent progress and future trends on damage identification methods for bridge structures

Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm

A Bayesian Belief Network method for bridge deterioration detection

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