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.

scholarly journals Early 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 ◽  
Compressive Test ◽  
Fe Modelling ◽  
Initiation And Propagation ◽  
Oriented Energy

Abstract The 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.

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 A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jinkang Wang ◽  
Xiaohui He ◽  
Shao Faming ◽  
Guanlin Lu ◽  
Hu Cong ◽  
...  
Keyword(s):  
Real Time ◽  
Crack Detection ◽  
Detection Method

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 SURG-03. IMMERSIVE VIRTUAL REALITY APPLICATIONS IN NEUROSURGICAL ONCOLOGY

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii461-iii461
Author(s):  
Andrea Carai ◽  
Angela Mastronuzzi ◽  
Giovanna Stefania Colafati ◽  
Paul Voicu ◽  
Nicola Onorini ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
New Technologies ◽  
Young Patients ◽  
Spatial Relationship ◽  
3D Models ◽  
Immersive Virtual Reality ◽  
Global Improvement ◽  
3D Rendering ◽  
Before And After

Abstract Tridimensional (3D) rendering of volumetric neuroimaging is increasingly been used to assist surgical management of brain tumors. New technologies allowing immersive virtual reality (VR) visualization of obtained models offer the opportunity to appreciate neuroanatomical details and spatial relationship between the tumor and normal neuroanatomical structures to a level never seen before. We present our preliminary experience with the Surgical Theatre, a commercially available 3D VR system, in 60 consecutive neurosurgical oncology cases. 3D models were developed from volumetric CT scans and MR standard and advanced sequences. The system allows the loading of 6 different layers at the same time, with the possibility to modulate opacity and threshold in real time. Use of the 3D VR was used during preoperative planning allowing a better definition of surgical strategy. A tailored craniotomy and brain dissection can be simulated in advanced and precisely performed in the OR, connecting the system to intraoperative neuronavigation. Smaller blood vessels are generally not included in the 3D rendering, however, real-time intraoperative threshold modulation of the 3D model assisted in their identification improving surgical confidence and safety during the procedure. VR was also used offline, both before and after surgery, in the setting of case discussion within the neurosurgical team and during MDT discussion. Finally, 3D VR was used during informed consent, improving communication with families and young patients. 3D VR allows to tailor surgical strategies to the single patient, contributing to procedural safety and efficacy and to the global improvement of neurosurgical oncology care.

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.

Microstructural Control of Ductile Crack Propagation in TMCP HSLA Pipeline Steels

2014 ◽  
Vol 922 ◽  
pp. 568-573
Author(s):  
Victor Carretero Olalla ◽  
N. Sanchez Mouriño ◽  
Philippe Thibaux ◽  
Leo Kestens ◽  
Roumen H. Petrov
Keyword(s):  
Grain Size ◽  
Crack Propagation ◽  
Volume Fraction ◽  
Main Role ◽  
Charpy Test ◽  
Initiation And Propagation ◽  
Average Grain Size ◽  
Main Disadvantage ◽  
Steel Grades ◽  
Increasing Demand

Control of ductile fracture propagation is one of the major concerns for pipeline industry, particularly with the increasing demand of new control rolled steel grades required to maintain integrity at high operational pressures. The objective of this research is to understand which microstructural features govern crack propagation, and to analyse the effect of two of them (average grain size, and volume fraction of pearlite). The main disadvantage during classical Charpy test was to discriminate the crack initiation and propagation energy during fracture of a notched sample. The initiation appears to be caused by the stress state in the neighbouring of Ti-containing precipitates or pearlite particles (no presence of M/A constituents or MnS inclusions was detected in the evaluated grades), propagation-arrest of the crack is assumed to play the main role concerning the control of fracture. Our approach to characterize the fracture resistance is to measure the energy absorbed during the crack propagation stage by means of load-displacement curves obtained via instrumented Charpy test. It was observed that the energy absorbed during crack propagation is not influenced by the average grain size but by the fraction and the morphological (banded-not banded) distribution of second pearlitic phase. This suggests that a different approach to characterize the heterogeneities in grain size clustering might be followed to correlate the energy measured during crack propagation and the morphological features of the steel.

Crack Propagation and Local Failure Simulation of Reinforced Concrete Subjected to Projectile Impact Using RBSM

2016 ◽  
Author(s):  
Yoshihito Yamamoto ◽  
Soichiro Okazaki ◽  
Hikaru Nakamura ◽  
Masuhiro Beppu ◽  
Taiki Shibata
Keyword(s):  
Reinforced Concrete ◽  
Crack Propagation ◽  
High Velocity ◽  
Failure Modes ◽  
Concrete Structures ◽  
Constitutive Models ◽  
Local Failure ◽  
High Velocity Impact ◽  
Velocity Impact ◽  
Projectile Impact

In this paper, numerical simulations of reinforced mortar beams subjected to projectile impact are conducted by using the proposed 3-D Rigid-Body-Spring Model (RBSM) in order to investigate mechanisms of crack propagation and scabbing mode of concrete members under high-velocity impact. The RBSM is one of the discrete-type numerical methods, which represents a continuum material as an assemblage of rigid particle interconnected by springs. The RBSM have advantages in modeling localized and oriented phenomena, such as cracking, its propagation, frictional slip and so on, in concrete structures. The authors have already developed constitutive models for the 3D RBSM with random geometry generated Voronoi diagram in order to quantitatively evaluate the mechanical responses of concrete including softening and localization fractures, and have shown that the model can simulate cracking and various failure modes of reinforced concrete structures. In the target tests, projectile velocity is set 200m/s. The reinforced mortar beams with or without the shear reinforcing steel plates were used to investigate the effects of shear reinforcement on the crack propagation and the local failure modes. By comparing the numerical results with the test results, it is confirmed that the proposed model can reproduce well the crack propagation and the local failure behaviors. In addition, effects of the reinforcing plates on the stress wave and the crack propagation behaviors are discussed from the observation of the numerical simulation results. As a result, it was found that scabbing of reinforced mortar beams subjected to high velocity impact which is in the range of the tests is caused by mainly shear deformation of a beam.

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