Performance-based reliability analysis of bridge pier subjected to vehicular collision: Extremity and failure

Hosseini, Parisa; Ghasemi, Seyed Hooman; Jalayer, Mohammad; Nowak, Andrzej S.

doi:10.1016/j.engfailanal.2019.104176

Cited by 26 publications

(8 citation statements)

References 21 publications

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“…This fourth condition aims to discard most of the cases of multiple vehicles on the bridge, which is not the focus of this study. Studies that utilize a filtering system to identify and eliminate trucks that are too light or questionable, and address the presence of multiple trucks on the bridge, can be found in Ghasemi et al ( 27 ) and Hosseini et al ( 28 ).…”

Section: Proof Of Conceptmentioning

confidence: 99%

Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

Mete

Corr

Wilbur

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Collecting information on heavy trucks and monitoring the bridges which they regularly cross is important for many facets of infrastructure management. In this paper, a two-step algorithm is developed using bridge and truck data, by deploying sequentially unsupervised and supervised machine learning techniques. Longitudinal clustering of bridge data, concerning strain waveforms, is adopted to perform the first step of the algorithm, while image visual inspection and classification tree methods are applied to truck data concurrently in the second step. Both bridge and truck traffic must be monitored for a limited, yet significant, amount of time to calibrate the algorithm, which is then used to build a classification framework. The framework provides the same benefits of two data collection systems while only one needs to be operative. Depending on which monitoring system remains available, the framework enables the use of bridge data to identify the truck’s profile which generated it, or to estimate bridge response given the truck’s information. As a result, the present study aims to provide decision-makers with an effective way to monitor the whole bridge-traffic system, bridge managers to plan effective maintenance, and policymakers to develop ad hoc regulations.

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Section: Proof Of Conceptmentioning

confidence: 99%

Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

Mete

Corr

Wilbur

et al. 2021

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…The representative results of this numerical study is depicted in Figs. 24,25,26,27,28,29,30,31,32,33, 34, and 35. Fig.…”

Section: Appendixmentioning

confidence: 99%

“…It is worth to be noted that the safety measures of buildings in terms of structural reliability and probabilistic point of view were studied in different researches. However, this perspective of analysis was not considered in the current study [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection of reinforced concrete shear walls subject to consequent earthquakes

2019

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Structural health monitoring is a procedure to provide accurate and immediate information on the condition and efficiency of structures. There is a variety of damage factors and the unpredictability of future damage, is essential for the application of structural health monitoring. Structural health monitoring and damage detection in early stages is one of the most fascinating topics that had been paid attention because the majority of damages can be repaired and reformed by initial evaluation, consequently the spread of damage to the structures, building collapse and rising of costs can be prevented. Detection of concrete shear wall damages are designed to withstand the lateral load on the structure is critical. Since failures and malfunctions of shear walls can lead to serious damage or even progressive dilapidation of concrete structures, any change in stiffness and frequency can clearly demonstrate the occurrence of the damage. In this article, with non-linear time history analysis, the finite element model of structures with concrete shear walls subject to four earthquakes have been extracted and applying Fourier and wavelet transform, the damage of shear walls is detected. The results of displacement, base shear and hysteresis curves for experimental and numerical programs indicate a good agreement which guarantee the precision of the damage detection procedure.

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“…Clearly, the probabilistic analysis has received more attention in current codes. Furthermore, some researchers have tried to establish a performance-based design method for RC piers under vehicle collision (Auyeung et al, 2019; Cao and Agrawal et al, 2019a; Hosseini et al, 2019; Sharma et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Vehicle collision with bridge piers: A state-of-the-art review

Chen

Liu

2020

Advances in Structural Engineering

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Although the probability of vehicle collision with bridge piers is relatively low, it may cause the pier to fracture and even the entire bridge to collapse, resulting in casualties and huge economic losses. Therefore, bridge piers should be properly designed against vehicle collision. This paper aims to make a state-of-the-art review of the research on vehicle collision with bridge piers, to summarize the achievements and current limitations in this field, and to give some suggestions for future research. It is organized within a framework of performance-based design, which is divided into four types of problems, that is, hazard analysis, structural analysis, damage analysis, and loss analysis. Studies show that reinforced concrete (RC) piers under vehicle impact generally exhibit three damage modes, that is, local damage, shear damage, and flexural damage. When a large truck hits a pier, the engine and container (cargo) may contribute to a two-stage impact characteristic, which has a great influence on the response and damage of the pier. The vehicular impact (force) models and nonlinear response models of RC members under impact loads have been developed respectively, which can be combined to quickly analyze the nonlinear response of RC piers under vehicle impact. Deformation-based methods should be developed to quantify the damage level of RC piers under different damage modes. Current codes still mainly adopt the equivalent static force design method, but some provisions regarding probabilistic (reliability) analysis have appeared. More attention should be paid to the statistical analysis of roadside crashes and vehicle-related parameters in order to obtain a more realistic probabilistic analysis model, and further establish a performance-based design method for RC piers subjected to vehicle collision.

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Performance-based reliability analysis of bridge pier subjected to vehicular collision: Extremity and failure

Cited by 26 publications

References 21 publications

Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

Structural damage detection of reinforced concrete shear walls subject to consequent earthquakes

Vehicle collision with bridge piers: A state-of-the-art review

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