Smart film for crack monitoring of concrete bridges

Zhou, Zhixiang; Zhang, Benniu; Xia, Kaiwen; Li, Xingxing; Guo, Yan; Zhang, Kaihong

doi:10.1177/1475921710373288

Cited by 30 publications

(7 citation statements)

References 27 publications

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“…It plays a crucial role in evaluating the safety and durability of structures. One of the most challenging problems in structural health monitoring is diagnosing and monitoring cracks in structures [5].…”

Section: Introductionmentioning

confidence: 99%

Research on Performance of Smart Concrete Materials and Self-Monitoring of Cracks in Beam Members

Chen

et al. 2023

Preprint

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Section: Introductionmentioning

confidence: 99%

Research on Performance of Smart Concrete Materials and Self-Monitoring of Cracks in Beam Members

Chen

et al. 2023

Preprint

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“…1 In civil engineering, continuous monitoring of the structures is essential in order to identify defects like cracks at an early stage. 2 Non-Destructive Testing (NDT) techniques identify the state of the concrete structures without causing any permanent damage. 3 The use of technology, such as robots and cameras (visual inspection), to capture images 4 is an effective non-destructive evaluation (NDE) approach for automatic detection of cracks in concrete structures; 5 also it is faster and more accurate than the traditional manual inspection.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of an improved deep CNN-based concrete crack detection algorithm

Pennada

Perry

McAlorum

et al. 2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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This study uses a novel directional lighting approach to produce a computationally efficient five-channel Visual Geometry Group-16 (VGG-16) convolutional neural network (CNN) model for concrete crack detection and classification in low-light environments. The first convolutional layer of the proposed model copies the weights for the first three channels from the pre-trained model. In contrast, the additional two channels are set to the average of the existing weights along the channels. The model employs transfer learning and fine-tuning approaches to enhance accuracy and efficiency. It utilizes variations in patterned lighting to produce five channels. Each channel represents a grayscale version of the images captured using directed lighting in the right, below, left, above, and diffused directions, respectively. The model is evaluated on concrete crack samples with crack widths ranging from 0.07 mm to 0.3 mm. The modified five-channel VGG-16 model outperformed the traditional three-channel model, showing improvements ranging from 6.5 to 11.7 percent in true positive rate, false positive rate, precision, F1 score, accuracy, and Matthew's correlation coefficient. These performance improvements are achieved with no significant change in evaluation time. This study provides useful information for constructing custom CNN models for civil engineering problems. Furthermore, it introduces a novel technique to identify cracks in concrete buildings using directed illumination in low-light conditions.

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“…Novel surface strain sensing technologies, or sensing skins, with excellent durability, sensitivity, and cost-effectiveness for geometrically large systems, have gained popularity in the research community as an organic step beyond FOS. These include strain sensing sheets based on large area electronics and integrated circuits [14,15,16], electrical impedance tomography (EIT) [17,18], and multifunctional materials [19,20]. The authors have previously proposed sensing skin technology based on soft elastomeric capacitors (SECs) that act as large-area strain gauges.…”

Section: Introductionmentioning

confidence: 99%

Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array

Jin

Downey

Cancelli

et al. 2019

Sensors

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Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the automatic monitoring of such cracks. However, these solutions become economically difficult to deploy when the surface under investigation is very large. This paper proposes to leverage a novel sensing skin for monitoring cracks in concrete structures. This sensing skin is constituted of a flexible electronic termed soft elastomeric capacitor, which detects a change in strain through changes in measured capacitance. The SEC is a low-cost, durable, and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel components. In this study, the sensing skin is introduced and preliminary validation results on a small-scale reinforced concrete beam are presented. The technology is verified on a full-scale post-tensioned concrete beam. Results show that the sensing skin is capable of detecting, localizing, and quantifying cracks that formed in both the reinforced and post-tensioned concrete specimens.

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Smart film for crack monitoring of concrete bridges

Cited by 30 publications

References 27 publications

Research on Performance of Smart Concrete Materials and Self-Monitoring of Cracks in Beam Members

Research on Performance of Smart Concrete Materials and Self-Monitoring of Cracks in Beam Members

Performance evaluation of an improved deep CNN-based concrete crack detection algorithm

Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array

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