Random forest-based evaluation technique for internal damage in reinforced concrete featuring multiple nondestructive testing results

Chun, Pang; Ujike, Isao; Mishima, Kazuo; Kusumoto, Masahiro; Okazaki, Shinichiro

doi:10.1016/j.conbuildmat.2020.119238

Cited by 72 publications

(29 citation statements)

References 13 publications

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“…In addition, to demonstrate the JSO-SVC predictive performance, the random forest classification (RFC) model [79] and convolutional neural network (CNN) models [80] have been employed as benchmark approaches. e RFC and CNN are selected for result comparison in this study because these two machine learning approaches have been successfully applied in various works related to computer vision-based or nondestructive testing-based structural health monitoring/diagnosis [14,26,[81][82][83][84][85][86][87][88].…”

Section: Resultsmentioning

confidence: 99%

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

Hoang

Huynh

Tran

2021

Advances in Civil Engineering

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During the phase of building survey, spalling and its severity should be detected as earlier as possible to provide timely information on structural heath to building maintenance agency. Correct detection of spall severity can significantly help decision makers develop effective maintenance schedule and prioritize their financial resources better. This study aims at developing a computer vision-based method for automatic classification of concrete spalling severity. Based on input image of concrete surface, the method is capable of distinguishing between a minor spalling in which the depth of the broken-off material is less than the concrete cover layer and a deep spalling in which the reinforcing steel bars have been revealed. To characterize concrete surface condition, image texture descriptors of statistical measurement of color channels, gray-level run length, and center-symmetric local binary pattern are used. Based on these texture-based features, the support vector machine classifier optimized by the jellyfish search metaheuristic is put forward to construct a decision boundary that partitions the input data into two classes of shallow spalling and deep spalling. A dataset consisting of 300 image samples has been collected to train and verify the proposed computer vision method. Experimental results supported by the Wilcoxon signed-rank test point out that the newly developed method is highly suitable for concrete spall severity classification with accuracy rate = 93.33%, F1 score = 0.93, and area under the receiver operating characteristic curve = 0.97.

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Section: Resultsmentioning

confidence: 99%

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

Hoang

Huynh

Tran

2021

Advances in Civil Engineering

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“…The authors have already published crack detection [ 28 ] and nondestructive inspection methods [ 29 ] using decision tree-based algorithms such as Random Forest. In this section, however, the authors will also summarize the theory of Random Forest for the convenience of the reader.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results

Chun

Yamane

Izumi

et al. 2020

Sensors

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It is necessary to assess damage properly for the safe use of a structure and for the development of an appropriate maintenance strategy. Although many efforts have been made to measure the vibration of a structure to determine the degree of damage, the accuracy of evaluation is not high enough, so it is difficult to say that a damage evaluation based on vibrations in a structure has not been put to practical use. In this study, we propose a method to evaluate damage by measuring the acceleration of a structure at multiple points and interpreting the results with a Random Forest, which is a kind of supervised machine learning. The proposed method uses the maximum response acceleration, standard deviation, logarithmic decay rate, and natural frequency to improve the accuracy of damage assessment. We propose a three-step Random Forest method to evaluate various damage types based on the results of these many measurements. Then, the accuracy of the proposed method is verified based on the results of a cross-validation and a vibration test of an actual damaged specimen.

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“…The present method is positioned as one that can obtain the crack ratio correctly unlike object detection methods such as SSD, and the effort of annotation is much smaller compared to semantic segmentation techniques such as Mask R-CNN. Based on our experience in [21][22][23][24][25][26] with research on damage detection using machine learning and deep learning, we believe that to improve the accuracy of deep learning, it is necessary not only to increase the amount of data but also to improve the quality of the training data. Therefore, in this study, we develop a framework to focus on training of types of images that are difficult to detect, from the viewpoint of improving the training performance.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Cracks in Asphalt Pavement Using Deep Learning to Overcome Weaknesses in Images and GIS Visualization

2021

Self Cite

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The crack ratio is one of the indices used to quantitatively evaluate the soundness of asphalt pavement. However, since the inspection of pavement requires much labor and cost, automatic inspection of pavement damage by image analysis is required in order to reduce the burden of such work. In this study, a system was constructed that automatically detects and evaluates cracks from images of pavement using a convolutional neural network, a kind of deep learning. The most novel aspect of this study is that the accuracy was recursively improved through retraining the convolutional neural network (CNN) by collecting images which had previously been incorrectly analyzed. Then, study and implementation were conducted of a system for plotting the results in a GIS. In addition, an experiment was carried out applying this system to images actually taken from an MMS (mobile mapping system), and this confirmed that the system had high crack evaluation performance.

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Random forest-based evaluation technique for internal damage in reinforced concrete featuring multiple nondestructive testing results

Cited by 72 publications

References 13 publications

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach

Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results

Automatic Detection of Cracks in Asphalt Pavement Using Deep Learning to Overcome Weaknesses in Images and GIS Visualization

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