Acoustic Emission Sensing for Crack Monitoring in Prefabricated and Prestressed Reinforced Concrete Bridge Girders

Worley, Robert; Dewoolkar, Mandar M.; Xia, Tian; Farrell, Robert; Orfeo, Daniel; Burns, Dylan; Huston, Dryver R.

doi:10.1061/(asce)be.1943-5592.0001377

Cited by 25 publications

(10 citation statements)

References 12 publications

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“…In past decades, damage detection in PSC structures has been the concern of many researchers and practitioners [ 7 , 8 , 9 ]. Strain-based methods have been commonly used to directly estimate prestress force levels based on well-defined strain-stress relationships in steel strands [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone

Pham

Dang

et al. 2021

Sensors

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This study investigates the feasibility of smart aggregate (SA) sensors and their optimal locations for impedance-based damage monitoring in prestressed concrete (PSC) anchorage zones. Firstly, numerical stress analyses are performed on the PSC anchorage zone to determine the location of potential damage that is induced by prestressing forces. Secondly, a simplified impedance model is briefly described for the SA sensor in the anchorage. Thirdly, numerical impedance analyses are performed to explore the sensitivities of a few SA sensors in the anchorage zone under the variation of prestressing forces and under the occurrence of artificial damage events. Finally, a real-scale PSC anchorage zone is experimentally examined to evaluate the optimal localization of the SA sensor for concrete damage detection. Impedance responses measured under a series of prestressing forces are statistically quantified to estimate the performance of damage monitoring via the SA sensor in the PSC anchorage.

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Section: Introductionmentioning

confidence: 99%

Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone

Pham

Dang

et al. 2021

Sensors

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“…Fan [19] and Wang [20] applied AE technique to monitor the fractal process of prestressed concrete beams, obtaining the good correlations of AE parameters and damage evolution information. AE has been proven to be useful for analyzing the localization and identification of cracking behaviors and assessing the loss of the bearing capacities for prestressed concrete members by Worley et al [21] and ElBatanouny et al [22].…”

Section: Introductionmentioning

confidence: 99%

Fracture characterization of composite slabs with different connections based on acoustic emission parameters

Zhou

Shan

Liu

et al. 2021

Struct Control Health Monit

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Summary Damage fracture characteristics of composite slabs with different connection configurations were analyzed using acoustic emission (AE) with cast‐in‐place (CIP) reinforced concrete (RC) slabs as contrast. AE characteristic parameters were obtained during static experiments and compared with each other. The bearing capacity was directly proportional to cumulative AE energy, rise time, and duration time, of which the correlation coefficient of rise time was the highest and reached 99.99%. The whole fracture process could be divided into three stages. Compared to CIP slabs, the rates of energy release in the former two stages were slower, which caused the worse ductility. Meanwhile, many other differences among the tested slabs were captured in aspects of energy release, crack development, and severity. Finally, the quantitative analysis of damage classifications was performed under various load levels. The experimental results were beneficial to advance the application of AE in fields of damage analysis and health monitoring for composite slabs.

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“…Aldahdooh and Bunnori [9] also adopted the AE signals to classify crack types of reinforced concrete beams subjected to four-point bending. Over the past years, portable AE equipment has been applied to monitor the cracks in prefabricated prestressed concrete beams during construction [10]. As an effective method for classification of structural cracks, the AE method has high demand on equipment and is seriously affected by noise.…”

Section: Introductionmentioning

confidence: 99%

Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks

et al. 2020

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Cracks are often the most intuitive indicators for assessing the condition of in-service structures. Intelligent detection methods based on regular convolutional neural networks (CNNs) have been widely applied to the field of crack detection in recently years; however, these methods exhibit unsatisfying performance on the detection of out-of-plane cracks. To overcome this drawback, a new type of region-based CNN (R-CNN) crack detector with deformable modules is proposed in the present study. The core idea of the method is to replace the traditional regular convolution and pooling operation with a deformable convolution operation and a deformable pooling operation. The idea is implemented on three different regular detectors, namely the Faster R-CNN, region-based fully convolutional networks (R-FCN), and feature pyramid network (FPN)-based Faster R-CNN. To examine the advantages of the proposed method, the results obtained from the proposed detector and corresponding regular detectors are compared. The results show that the addition of deformable modules improves the mean average precisions (mAPs) achieved by the Faster R-CNN, R-FCN, and FPN-based Faster R-CNN for crack detection. More importantly, adding deformable modules enables these detectors to detect the out-of-plane cracks that are difficult for regular detectors to detect.

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Acoustic Emission Sensing for Crack Monitoring in Prefabricated and Prestressed Reinforced Concrete Bridge Girders

Cited by 25 publications

References 12 publications

Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone

Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone

Fracture characterization of composite slabs with different connections based on acoustic emission parameters

Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks

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