Vision Based Crack Detection in Concrete Structures Using Cutting-Edge Deep Learning Techniques

Paramanandham, Nirmala; Koppad, Deepali; Anbalagan, Sasithradevi

doi:10.18280/ts.390210

Cited by 7 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the accuracy of the outcomes was predicted to be 99% where the error rate is at 1%. Contrarily, several authors [1][2][3]29,30] focused and examined exclusively upon the vision based crack detection models. They claimed that though the traditional models are better in accuracy than the vision transformer models that averagely produce outcomes that are of 95% accurate, the transformer models are rapid, robust, incurs lesser costs and lesser time for computing and processing.…”

Section: Traditional Methods Versus Contemporary Methodsmentioning

confidence: 99%

“…The machine learning based models in the concrete structural health analysis has been focused by researchers to study the variations in the expansions, vibrations, features (frequency and spatial), contractions and dampness [2]. Automatic detection of the concrete structures' cracks and how it impacts the health of the concrete structures have been recently focused with the "vision transformers" (ViT) as the core focus.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of machine learning for crack detection on concrete structures using CNN architecture

Padmapoorani¹,

Senthilkumar²

2023

Matéria (Rio J.)

View full text Add to dashboard Cite

Cracks in concrete structures are caused due to contraction and expansion irregularities, from potential damages caused in the buildings. These irregularities and damages are assessed by the engineers manually or through identification and prediction models with machine learning techniques to evaluate the impact and significance of the structural health in buildings. This research aims at applying machine learning based on VGG16-Net model for the detection of cracks in concrete structures. The proposed model is of CNN (convolutional neural network) + VGG neural network based architecture. The study uses the gradient boosting algorithm for image segmentation. The datasets are obtained from "Kaggle" resource and the library used is 'Hugging Face Transformers'. To evaluate the developed models' performance metrics such as "accuracy, precision, recall and f1-score" are used. The 'accuracy' score obtained is compared against the 'ViT' (Google transformer) accuracy rate, for comparison. The proposed model achieved 98% validation accuracy rate with 0.3% loss. Thus the developed research contributes an innovative and a novel ML model that predicts and identifies the cracks in concrete structures with less loss and higher accuracy with CNN architecture than ViT (vision transformer) models. Current study also provides more input upon CNN being more accurate than ViT models for future researchers for comparative analyses.

show abstract

Section: Traditional Methods Versus Contemporary Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of machine learning for crack detection on concrete structures using CNN architecture

Padmapoorani¹,

Senthilkumar²

2023

Matéria (Rio J.)

View full text Add to dashboard Cite

show abstract

“…The first stage is the elastic stage (section 0A), no crack develops during this stage, when the bending moment increases to the crack bending moment, cracks appear at the bottom of the beam [26], reducing the bending stiffness of the beam. The second stage is the crack development stage (section AB), during this stage, as the bending moment increases, the crack width gradually increases and the cracks develop upward, at a certain distance, new cracks would appear [27]. The third stage is the failure stage (section BC), during this stage, the tensile rebar yields and the bending moment reaches the yield bending moment.…”

Section: Figure 3 Relationship Between Moment and Mid-span Deflection...mentioning

confidence: 99%

Flexural Performance and Thermomechanical Coupling Analysis of Full-Iron Tailings Reinforced Concrete Beams

Ma¹,

Zhang²,

Sun³

2022

IJHT

View full text Add to dashboard Cite

Problems such as carbonization, corrosion, and durability deterioration would occur to full-iron tailings reinforced concrete (FIT-RC) subjected to long-term use at high temperatures, to find solutions for these problems, it’s of important value to research the flexural performance of FIT-RC beam and its thermomechanical coupling mechanism. This paper proposed a calculation method for designing the flexural capacity and deflection of the normal section of FIT-RC beam, and summarized the high temperature relationship among thermal parameters, thermal stress, and thermal strain of FIT-RC material. In our experiment, four-point bending test was performed on the RC beams and the results showed that the bearing capacity of FIT-RC beam is equivalent to that of conventional reinforced concrete (CRC) beam, the failure modes of the two kinds of RC beams are basically the same. At high temperatures, the similarity of mechanical performance of concrete components with different reduction ratios is related to the similarity of temperature field. With the increase of heating time, the difference in the fire resistance grade of concrete components with different reduction ratios decreases to the minimum value. This study has clarified the high temperature relationship between thermal stress and thermal strain, and verified the feasibility of applying FIT to RC structures.

show abstract

“…The deep learning models such as VGG-16, ResNet50 and Inception ResNet-V2 are discussed in [ 25 , 26 , 27 ]. Paramanandham et al [ 28 ] discussed about concrete crack detection using various deep learnbing models. Qi Chen et al [ 29 ] used the guided filter approach for the removal of noise and analyzed the characterization of the crack structure using Hessian structures followed by refinement process.…”

Section: Introductionmentioning

confidence: 99%

Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis

Paramanandham

Rajendiran²,

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

This research article is aimed at improving the efficiency of a computer vision system that uses image processing for detecting cracks. Images are prone to noise when captured using drones or under various lighting conditions. To analyze this, the images were gathered under various conditions. To address the noise issue and to classify the cracks based on the severity level, a novel technique is proposed using a pixel-intensity resemblance measurement (PIRM) rule. Using PIRM, the noisy images and noiseless images were classified. Then, the noise was filtered using a median filter. The cracks were detected using VGG-16, ResNet-50 and InceptionResNet-V2 models. Once the crack was detected, the images were then segregated using a crack risk-analysis algorithm. Based on the severity level of the crack, an alert can be given to the authorized person to take the necessary action to avoid major accidents. The proposed technique achieved a 6% improvement without PIRM and a 10% improvement with the PIRM rule for the VGG-16 model. Similarly, it showed 3 and 10% for ResNet-50, 2 and 3% for Inception ResNet and a 9 and 10% increment for the Xception model. When the images were corrupted from a single noise alone, 95.6% accuracy was achieved using the ResNet-50 model for Gaussian noise, 99.65% accuracy was achieved through Inception ResNet-v2 for Poisson noise, and 99.95% accuracy was achieved by the Xception model for speckle noise.

show abstract

Vision Based Crack Detection in Concrete Structures Using Cutting-Edge Deep Learning Techniques

Cited by 7 publications

References 0 publications

Application of machine learning for crack detection on concrete structures using CNN architecture

Application of machine learning for crack detection on concrete structures using CNN architecture

Flexural Performance and Thermomechanical Coupling Analysis of Full-Iron Tailings Reinforced Concrete Beams

Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis

Contact Info

Product

Resources

About