DEEP LEARNING AND RANDOM FOREST BASED CRACK DETECTION FROM AN IMAGE OF CONCRETE SURFACE

In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracy close to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access database, SDNET2018, for the automatic detection of external cracks.

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽

10.3390/en14154595 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4595

Author(s):

Parisa Asadi ◽

Lauren E. Beckingham

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ct Images ◽

Ct Imaging ◽

Learning Method ◽

Learning Methods ◽

X Ray ◽

Machine Learning Methods ◽

Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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Autonomous Crack and Bughole Detection for Concrete Surface Image Based on Deep Learning

IEEE Access ◽

10.1109/access.2021.3088292 ◽

2021 ◽

pp. 1-1

Author(s):

Yujia Sun ◽

Yang Yang ◽

Gang Yao ◽

Fujia Wei ◽

Mingpu Wong

Keyword(s):

Deep Learning ◽

Concrete Surface ◽

Surface Image

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Deep Learning-Based Automatic Safety Detection System for Crack Detection

2020 7th International Conference on Dependable Systems and Their Applications (DSA) ◽

10.1109/dsa51864.2020.00034 ◽

2020 ◽

Author(s):

Quan Chen ◽

Xi-Xi Zhang ◽

Yu Chen ◽

Wei Jiang ◽

Guan Gui ◽

...

Keyword(s):

Deep Learning ◽

Crack Detection ◽

Detection System

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Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽

10.3390/electronics10101195 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1195

Author(s):

Priya Varshini A G ◽

Anitha Kumari K ◽

Vijayakumar Varadarajan

Keyword(s):

Deep Learning ◽

Random Forest ◽

Software Development ◽

Weighted Averaging ◽

Software Project ◽

Effort Estimation ◽

Software Effort Estimation ◽

Single Model ◽

Ensemble Techniques ◽

Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

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Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

Advances in Structural Engineering ◽

10.1177/1369433220986637 ◽

2021 ◽

pp. 136943322098663

Author(s):

Diana Andrushia A ◽

Anand N ◽

Eva Lubloy ◽

Prince Arulraj G

Keyword(s):

Deep Learning ◽

Crack Detection ◽

Detection Method ◽

Concrete Structures ◽

Vital Role ◽

Subjective Knowledge ◽

Thermal Cracks ◽

Reinforced Cement ◽

Novel Method ◽

Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

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