Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis

Arbaoui, Ahcene; Ouahabi, Abdeldjalil; Jacques, Sébastien; Hamiane, Madina

doi:10.3390/electronics10151772

Cited by 47 publications

(26 citation statements)

References 52 publications

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“…The value at which the structure completely loses its ability to resist the load and its destruction occurs are taken as the value of the maximum bearing capacity of the support. Multiple cracking of concrete requires the development of non-destructive methods of structural control in difficult operating conditions [81,82].…”

Section: Discussionmentioning

confidence: 99%

Strain Hardening of Polypropylene Microfiber Reinforced Composite Based on Alkali-Activated Slag Matrix

Smirnova

Pidal²,

Alekseev³

et al. 2022

Materials

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A comparative study of the fracture features, strength and deformation properties of pseudo strain-hardening composites based on alkali-activated slag and Portland cement matrices with polypropylene microfiber was carried out. Correlations between their compositions and characteristics of stress–strain diagrams under tension in bending with an additional determination of acoustic emission parameters were determined. An average strength alkali-activated slag matrix with compressive strength of 40 MPa and a high-strength Portland cement matrix with compressive strength of 70 MPa were used. The matrix compositions were selected for high filling the composites with polypropylene microfiber in the amount of 5%-vol. and 3.5%-vol. ensuring the workability at the low water-to-binder ratios of 0.22 and 0.3 for Portland cement and alkali-activated slag matrices, respectively. Deformation diagrams were obtained for all studied compositions. Peaks in the number of acoustic signals in alkali-activated slag composites were observed only in the strain-softening zone. Graphs of dependence of the rate of acoustic events occurrence in samples from the start of the test experimentally prove that this method of non-destructive testing can be used to monitor structures based on strain-hardening composites.

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Section: Discussionmentioning

confidence: 99%

Strain Hardening of Polypropylene Microfiber Reinforced Composite Based on Alkali-Activated Slag Matrix

Smirnova

Pidal²,

Alekseev³

et al. 2022

Materials

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“…Third, we only analyzed video data; however, it may be beneficial to also analyze audio data because smartphones record both audio and video, and audio data can be robust to occlusion. Finally, we extracted the rectangular bounding box of the persons, but the segmentation of images related to the regions of interest can provide relevant information on the posture of the patients [43,44].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

Lee

Park

Jung

2021

JPM

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The goal of this study was to develop a framework to classify dependence in ambulation by employing a deep model in a 3D convolutional neural network (3D-CNN) using video data recorded by a smartphone during inpatient rehabilitation therapy in stroke patients. Among 2311 video clips, 1218 walk action cases were collected from 206 stroke patients receiving inpatient rehabilitation therapy (63.24 ± 14.36 years old). As ground truth, the dependence in ambulation was assessed and labeled using the functional ambulatory categories (FACs) and Berg balance scale (BBS). The dependent ambulation was defined as a FAC score less than 4 or a BBS score less than 45. We extracted patient-centered video and patient-centered pose of the target from the tracked target’s posture keypoint location information. Then, the extracted patient-centered video was input in the 3D-CNN, and the extracted patient-centered pose was used to measure swing time asymmetry. Finally, we evaluated the classification of dependence in ambulation using video data via fivefold cross-validation. When training the 3D-CNN based on FACs and BBS, the model performed with 86.3% accuracy, 87.4% precision, 94.0% recall, and 90.5% F1 score. When the 3D-CNN based on FACs and BBS was combined with swing time asymmetry, the model exhibited improved performance (88.7% accuracy, 89.1% precision, 95.7% recall, and 92.2% F1 score). The proposed framework for dependence in ambulation can be useful, as it alerts clinicians or caregivers when stroke patients with dependent ambulatory move alone without assistance. In addition, monitoring dependence in ambulation can facilitate the design of individualized rehabilitation strategies for stroke patients with impaired mobility and balance function.

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“…This can be used to separate high quality from low quality interferograms by use of the ResNet50-DCNN technique through learning the spatial behavior of color changes within the interferogram phase. The technique has the ability to classify features and learn image priors in the training phase [37][38][39][40].…”

Section: Methodsmentioning

confidence: 99%

Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

Zhang

Kaufmann

et al. 2021

Remote Sensing

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The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore, the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring.

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Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis

Cited by 47 publications

References 52 publications

Strain Hardening of Polypropylene Microfiber Reinforced Composite Based on Alkali-Activated Slag Matrix

Strain Hardening of Polypropylene Microfiber Reinforced Composite Based on Alkali-Activated Slag Matrix

Machine Learning-Based Classification of Dependence in Ambulation in Stroke Patients Using Smartphone Video Data

Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

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