Temperature tracer method for crack detection in underwater concrete structures

Zhu, Yuhe; Chen, Jiang; Zhang, Yuanyuan; Xiong, Feng; He, Fengfei; Fang, X.

doi:10.1002/stc.2595

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…At the same time, the seepage monitoring method based on distributed optical fiber thermal monitoring technology can be extended to other monitoring fields. For example, this technology can be applied to underwater concrete crack monitoring after integrated optimization (Chen et al, 2020;Zhu et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

A review of previous studies on dam leakage based on distributed optical fiber thermal monitoring technology

Fang

Zeng

Xiong

et al. 2021

Self Cite

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Purpose Seepage of the dam is an important safety problem, which may cause internal erosion of the structure. In the field of seepage monitoring in civil engineering, the distributed optical fiber sensing technology based on the temperature tracing method has been paid more attention due to its unique advantages of high sensitivity, good stability and high resolution. The purpose of this paper is to make a review of the existing related research, so as to facilitate the later scholars to understand and further study more systematically. Design/methodology/approach In this paper, three kinds of commonly used distributed fiber temperature measurement technologies are introduced. Based on the working principle, monitoring system, theoretical analysis, experimental research and engineering application of the fiber seepage monitoring technology, the present situation of dam seepage monitoring based on distributed fiber is reviewed in detail and their advantages and disadvantages are compared. Findings The thermal monitoring technology of seepage measurement depends on the accuracy of optical fiber temperature measurement (including the accuracy of the system and the rationality of the discrimination method), the correct installation of optical fiber and the quantitative analysis of temperature data. The accuracy of the current monitoring system can basically meet the existing measurement requirements, but the correct installation of optical fiber and the calibration of temperature data need to be further studied for different discrimination methods, and this field has great research value. Originality/value At present, there are many applications and research studies of optical fiber sensing in the field of structural health monitoring, and there are also reviews of related aspects. However, there is little or no review only in the field of seepage monitoring. This paper summarizes the research and application of optical fiber sensing in the field of seepage monitoring. The possibility of the gradient method to find its new prospect with the development of monitoring systems and the improvement of temperature resolution is discussed. The idea of extending the seepage monitoring method based on distributed optical fiber thermal monitoring technology to other monitoring fields is also given in the paper.

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Section: Discussionmentioning

confidence: 99%

A review of previous studies on dam leakage based on distributed optical fiber thermal monitoring technology

Fang

Zeng

Xiong

et al. 2021

Self Cite

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“…[48] provided a strategy for evaluating the statistical distribution of water's level slopes using flash photography. Researchers introduced a temperature tracer approach for fracture identification in sub-sea concrete structures based on heat transport theory in [49][50][51]. A method for controlling concrete cracking in underwater marine structures using basalt fiber was presented in [6].…”

Section: Other Methods: Including the Use Of Fiber-optic Sensors Ther...mentioning

confidence: 99%

Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem

et al. 2023

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This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, such as offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams, etc. Our focus is on the use of machine-learning-based approaches. First, a detailed literature review of the state of the current methods for underwater surface crack detection is presented, highlighting challenges and opportunities. An overview of the image augmentation approach for the creation of underwater optical effects is also presented. Experimental results using a standard network-based machine learning approach, which is used for surface crack detection in onshore environments, are presented. A series of test cases is presented in which existing networks’ performance is improved using augmented images for underwater conditions. The effectiveness and accuracy of the proposed approach in detecting cracks in underwater concrete structures are demonstrated. The proposed approach has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures.

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“…With the accumulation of defects, bridge collapse accidents will frequently occur in case surface defects of the bridge underwater pile-pier structure are not detected and found in time. Terefore, it is particularly urgent and important to carry out the defect detection of underwater pile-pier structures, thus providing accurate and efective data for the damage analysis and evaluation of underwater pile-pier structures [3,4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Surface Defects on Underwater Pile-Pier of Bridges Based on Image Fusion and Deep Learning

Jiang

Wang

et al. 2023

Structural Control and Health Monitoring

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As an important part of the bridge structure system, the underwater pile-pier structure usually occurs various defects on its surfaces due to its complex hydrological environment. The existing conventional defect detection approaches exist two aspects of problems: (1) insufficient definition and color distortion of the underwater images, and (2) low efficiency and error-prone. To solve these problems, this paper proposed the target defect detection model by integrating the image-fusion enhancement algorithm and the deep learning algorithm. Firstly, by analyzing the reasons for the degradation of the underwater images, the ACE (automatic color equalization) and CLAHE (contrast limited adaptive histogram equalization) algorithms are selected to enhance the image, respectively. Secondly, the two enhanced images are fused based on the point sharpness weight, and then the fusion results are further sharpened by the USM (unsharp mask) algorithm, thus obtaining the final fused images. Thirdly, 3,200 fused images are taken as the training set, by adopting the YOLOv3 algorithm to train the detection model, and then the training model is validated and tested by the other each 400 fused images, thus building up the target automatic detection model of underwater pile-pier surface defects. Finally, a series of comparison and discussion were conducted to validate the effectiveness of image-fusion and the robustness and effectiveness of the target detection model. The results found that the target detection model has excellent robustness against noise and effectiveness in the surface defect detection. This indicates that the image-fusion approach proposed in this paper can effectively enhance the image features, and the target detection model is feasible, robust, and effective in the automatic detection of surface defects on underwater pile-pier structures.

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Temperature tracer method for crack detection in underwater concrete structures

Cited by 16 publications

References 26 publications

A review of previous studies on dam leakage based on distributed optical fiber thermal monitoring technology

A review of previous studies on dam leakage based on distributed optical fiber thermal monitoring technology

Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem

Automatic Detection of Surface Defects on Underwater Pile-Pier of Bridges Based on Image Fusion and Deep Learning

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