Efficient crack detection and quantification in concrete structures using IoT

2021 ◽  
Vol 18 (1) ◽  
pp. 43-57
Author(s):  
Ajay Nair ◽  
Hemalatha R ◽  
P Sangeetha ◽  
Harish Kumar K ◽  
Dinesh Kumar P ◽  
...  
Keyword(s):  
Crack Detection ◽  
Concrete Structures ◽  
Detection And Quantification

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

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).

scholarly journals Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

2021 ◽  
Vol 11 (2) ◽  
pp. 813
Author(s):  
Shuai Teng ◽  
Zongchao Liu ◽  
Gongfa Chen ◽  
Li Cheng
Keyword(s):  
Feature Extraction ◽  
Crack Detection ◽  
Computational Cost ◽  
Concrete Structures ◽  
Detection Algorithm ◽  
Computational Time ◽  
Image Size ◽  
Important Indicator ◽  
Feature Extractor ◽  
Model Training

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

scholarly journals Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1688
Author(s):  
Luqman Ali ◽  
Fady Alnajjar ◽  
Hamad Al Jassmi ◽  
Munkhjargal Gochoo ◽  
Wasif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Concrete Structures ◽  
Model Performance ◽  
Training Data ◽  
Computational Time ◽  
Data Heterogeneity ◽  
Public Datasets ◽  
Detection And Localization

This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.

Numerical study towards the use of a SH wave ultrasonic-based strategy for crack detection in concrete structures

2013 ◽  
Vol 49 ◽  
pp. 782-791 ◽  
Author(s):  
L. Godinho ◽  
D. Dias-da-Costa ◽  
P. Areias ◽  
E. Júlio ◽  
D. Soares
Keyword(s):  
Crack Detection ◽  
Numerical Study ◽  
Concrete Structures ◽  
Sh Wave

scholarly journals Crack detection using modified spectral clustering method assisted with FE analysis for distress anticipation in cement-based composites

2021 ◽  
Author(s):  
Ajitanshu Vedrtnam ◽  
Santosh Kumar ◽  
Gonzalo Barluenga ◽  
Shashikant Chaturvedi
Keyword(s):  
Crack Propagation ◽  
Real Time ◽  
Crack Detection ◽  
Compressive Loading ◽  
Concrete Structures ◽  
Spatial Relationship ◽  
Fracture Pattern ◽  
Wood Dust ◽  
Compression Tests ◽  
Fe Modelling

Abstract The present work aimed to develop an efficient way of capturing real-time crack propagation in concrete structures. The image processing was utilized for crack detection, while finite element modeling (FEM) and scanning electron microscopy (SEM) were used for quantitative and qualitative analysis of crack propagation. A green cement-based composite (CBC) containing saw dust was compared to a reference M20 grade concrete under compressive loading. Crack propagation during compression tests was captured using an 8-megapixel mobile phone camera. The randomly selected images showing crack initiation and propagation in CBCs were used to assess the crack capturing capability of a spectral analysis based algorithm. A measure of oriented energy was provided at crack edges to develop a similarity spatial relationship among the pairwise pixels. FE modelling was used for distress anticipation, by analyzing stresses during the compressive test in constituents of CBCs. SEM analyses were also done to evaluate cracked samples. It was found that FE modeling could predict the crack prone regions that can be used jointly with the image analysis algorithm, providing real-time inputs from the crack-prone areas. Green CBC were compared to reference concrete samples, showing reliable results. The replacement of OPC with wood dust reduced compression strength and produced a different fracture pattern regarding reference concrete. The results of the study can be used for distress anticipation and early crack detection of concrete structures for preventive support and management.

scholarly journals Early crack detection using modified spectral clustering method assisted with FE analysis for distress anticipation in cement-based composites

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajitanshu Vedrtnam ◽  
Santosh Kumar ◽  
Gonzalo Barluenga ◽  
Shashikant Chaturvedi
Keyword(s):  
Crack Propagation ◽  
Real Time ◽  
Crack Detection ◽  
Compressive Loading ◽  
Concrete Structures ◽  
Spatial Relationship ◽  
Compressive Test ◽  
Fe Modelling ◽  
Initiation And Propagation ◽  
Oriented Energy

AbstractThe present work reports an efficient way of capturing real-time crack propagation in concrete structures. The modified spectral analysis based algorithm and finite element modeling (FEM) were utilised for crack detection and quantitative analysis of crack propagation. Crack propagation was captured in cement-based composite (CBC) containing saw dust and M20 grade concrete under compressive loading using a simple and inexpensive 8-megapixel mobile phone camera. The randomly selected images showing crack initiation and propagation in CBCs demonstrated the crack capturing capability of developed algorithm. A measure of oriented energy was provided at crack edges to develop a similarity spatial relationship among the pairwise pixels. FE modelling was used for distress anticipation, by analysing stresses during the compressive test in constituents of CBCs. FE modeling jointly with the developed algorithm, can provide real-time inputs from the crack-prone areas and useful in early crack detection of concrete structures for preventive support and management.

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