Structural Crack Detection Using DPP-BOTDA and Crack-Induced Features of the Brillouin Gain Spectrum

Structural damage generally initiates in the form of structural cracks. Thus, developing efficient crack detection techniques is of great importance for the structural health monitoring. In this paper, a new crack identification method is proposed, which is based on the differential pulse-width pair Brillouin optical time domain analysis (DPP-BOTDA) technology and the irregular features of Brillouin gain spectrum (BGS) in the fiber due to structural cracks. The proposed method provides a new way to detect and quantify structural cracks without knowing the strain in the structure. First, the working mechanism of DPP-BOTDA is introduced to illustrate the reason that the DPP-BOTDA, compared to traditional BOTDA technique, can significantly improve the spatial resolution of distributed strain sensing, which is critical for structural crack detection. Then, the BGSs in the fiber with the presence of structural cracks, measured by the DPP-BOTDA, are numerically simulated, from which the crack-induced irregular features of the BGS are summarized. Based these irregular features, new structural crack detection and quantification methods are proposed, which are found to be independent of structural stain. Finally, an experiment is conducted on a simple supported reinforced concrete (RC) beam. The results demonstrate that by using the BGS measured by the DPP-BOTDA, the proposed structural crack identification method successfully detects the occurrence of structural cracks and relatively accurately predicts the crack widths.

Download Full-text

Structural crack detection using deep learning–based fully convolutional networks

Advances in Structural Engineering ◽

10.1177/1369433219836292 ◽

2019 ◽

Vol 22 (16) ◽

pp. 3412-3419 ◽

Cited By ~ 5

Author(s):

Xiao-Wei Ye ◽

Tao Jin ◽

Peng-Yu Chen

Keyword(s):

Edge Detection ◽

Structural Damage ◽

Crack Detection ◽

Training Data ◽

Detection Methods ◽

Crack Identification ◽

Potential Threat ◽

Online Data ◽

Convolutional Network ◽

Data Set

Cracks are a potential threat to the safety and endurance of civil infrastructures, and therefore, careful and regular structural crack inspection is needed during their long-term service periods. Many image-processing approaches have been developed for structural crack detection. However, like traditional edge detection algorithms, these methods are easily disturbed by the environmental effect. Convolutional neural networks are newly developed methods and have excellent performances in the image-classification tasks. This study proposes a fully convolutional network called Ci-Net for structural crack identification. Pixel-level labeled image training data are obtained from the online data set. Four indices are adopted to evaluate the performance of the trained Ci-Net. Crack images from an indoor concrete beam test are adopted for validation of its structural crack recognition capacity. The recognition results are also compared with those obtained by the edge detection methods. It indicates that Ci-Net exhibits a better performance over the edge detection methods in structural damage detection.

Download Full-text

Brillouin gain spectrum manipulation using multifrequency pump and probe for slope-assisted BOTDA with wider dynamic range

Applied Physics Express ◽

10.35848/1882-0786/ac4a0c ◽

2022 ◽

Author(s):

Kazuki Hoshino ◽

Daiki Saito ◽

Yuma Endo ◽

Takahiro Hasegawa ◽

Yosuke Tanaka

Keyword(s):

Dynamic Range ◽

Time Domain Analysis ◽

Gain Spectrum ◽

Optical Pulses ◽

Shaping Technique ◽

Pump And Probe ◽

Spectral Components ◽

Brillouin Gain ◽

Linear Slope ◽

Optical Time

Abstract We propose slope assisted Brillouin optical time domain analysis (SA-BOTDA) with virtual Brillouin gain spectrum (BGS) generated by multifrequency pump and probe. The virtual BGS having a wide linear slope region of 100 MHz is easily generated by employing time-to-space spectral shaping technique that has been originally developed for generating short optical pulses. We demonstrate the distribution of virtual BGS realized by using five spectral components of pump and probe.

Download Full-text

Effects of Pump-Pulse Width and Temperature on Experimental Brillouin Gain Spectrum Obtained from Brillouin Optical Time-Domain Analysis Sensors

Bangladesh Journal of Physics ◽

10.3329/bjphy.v27i1.49727 ◽

2020 ◽

Vol 27 (1) ◽

pp. 69-80

Author(s):

Abul Kalam Azad

Keyword(s):

Pump Pulse ◽

Time Domain ◽

Pulse Width ◽

Nonlinear Least Squares ◽

Time Domain Analysis ◽

Domain Analysis ◽

Gain Spectrum ◽

Least Squares Curve Fitting ◽

Brillouin Gain ◽

Optical Time

In this paper, the characteristics of Brillouin gain spectrum (BGS) obtained from a Brillouin optical time-domain analysis (BOTDA) sensor are investigated and analyzed experimentally. The measured BGSs obtained for various pump-pulse widths and temperatures are fitted with different spectrum profiles using nonlinear least-squares curve fitting technique. The fitting performances of used profiles are presented and analyzed. Based on such performances, the proper spectrum profile to be used in the fitting process is determined and used to extract key parameters of the measured BGSs accurately. The variations of such key parameters with pump-pulse widths and temperatures are also investigated and analyzed. The results reveal that pump-pulse widths and temperatures have significant effects on the extracted key parameters of the measured BGSs obtained from BOTDA sensors. Bangladesh Journal of Physics, 27(1), 69-80, June 2020

Download Full-text

Concrete Crack Identification and Image Mosaic Based on Image Processing

Applied Sciences ◽

10.3390/app9224826 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4826

Author(s):

Tian ◽

Zhao ◽

Che ◽

Zhao ◽

Xin

Keyword(s):

Crack Length ◽

Crack Detection ◽

Accurate Method ◽

Crack Identification ◽

Image Mosaic ◽

Distance Method ◽

Edge Pixel ◽

Detection Techniques ◽

Object Distance ◽

Working Distance

Crack assessment is an essential process in bridge detection. In general, most non-contact crack detection techniques are not suitable for widespread use. The reason for this is that they all need to position the ruler at the inspection site in advance or calibrate the camera unit pixel size at a certain distance in a very intricate process. However, the object distance method in this paper can complete the calculation using only the crack image and the working distance, which are provided by an acquisition system equipped with a camera and laser range finder. First, the object distance method and the scale method are compared by calculating the crack width, and the results show that the object distance method is the more accurate method. Then, a double edge pixel statistical method is proposed to calculate the crack length, which solves the problem of redundant and missing pixels. In addition, the conventional mosaic algorithm is improved to realize an image mosaic for the more efficient splicing of crack images. Finally, a series of laboratory tests were conducted to verify the proposed approach. The experiments showed that the precision of crack length extraction can reach 92%, and the improved algorithm stitching precision can reach 98%.

Download Full-text

Measurement Improvement of Distributed Optical Fiber Sensor via Lorenz Local Single Peak Fitting Algorithm

Symmetry ◽

10.3390/sym13071166 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1166

Author(s):

Bin Liu ◽

Jianping He ◽

Shihai Zhang ◽

Yinping Zhang ◽

Jianan Yu ◽

...

Keyword(s):

Optical Fiber ◽

Frequency Shift ◽

Optical Fiber Sensor ◽

Gain Spectrum ◽

Fiber Sensor ◽

Peak Fitting ◽

Fitting Algorithm ◽

Distributed Optical Fiber Sensor ◽

Brillouin Gain ◽

Distributed Optical Fiber

Brillouin frequency shift (BFS) of distributed optical fiber sensor is extracted from the Brillouin gain spectrum (BGS), which is often characterized by Lorenz type. However, in the case of complex stress and optical fiber self damage, the BGS will deviate from Lorenz type and be asymmetric, which leads to the extraction error of BFS. In order to enhance the extraction accuracy of BFS, the Lorenz local single peak fitting algorithm was developed to fit the Brillouin gain spectrum curve, which can make the BSG symmetrical with respect to the Brillouin center frequency shift. One temperature test of a fiber-reinforced polymer (FRP) packaged sensor whose BSG curve is asymmetric was conducted to verify the idea. The results show that the local region curve of BSG processed by the developed algorithm has good symmetry, and the temperature measurement accuracy obtained by the developed algorithm is higher than that directly measured by demodulation equipment. Comparison with the reference temperature, the relative measurement error measured by the developed algorithm and BOTDA are within 4% and 8%, respectively.

Download Full-text

UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Sensors ◽

10.3390/s21082650 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2650

Author(s):

Daegyun Choi ◽

William Bell ◽

Donghoon Kim ◽

Jichul Kim

Keyword(s):

Visual Information ◽

Crack Detection ◽

Image Data ◽

Image Processing Techniques ◽

Location Determination ◽

Aerial Vehicle ◽

Using Data ◽

Processing Techniques ◽

Deep Learning Model ◽

Structural Cracks

Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.

Download Full-text

An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽

10.3390/s21051581 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1581

Author(s):

Xiaolong Chen ◽

Jian Li ◽

Shuowen Huang ◽

Hao Cui ◽

Peirong Liu ◽

...

Keyword(s):

Crack Detection ◽

Detection Method ◽

Multiple Scales ◽

Point Clouds ◽

Depth Image ◽

Detection Accuracy ◽

Detection Techniques ◽

Concrete Crack ◽

3D Point Clouds ◽

2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

Download Full-text