scholarly journals Construction of Accurate Crack Identification on Concrete Structure using Hybrid Deep Learning Approach

2021 ◽  
Vol 3 (2) ◽  
pp. 85-99
Author(s):  
Edriss Eisa Babikir Adam ◽  
Sathesh A
Keyword(s):  
Crack Detection ◽  
Concrete Bridges ◽  
Reinforced Concrete Beams ◽  
Bending Test ◽  
Random Fluctuation ◽  
Single Type ◽  
Crack Identification ◽  
Classification Error ◽  
Svm Classifier ◽  
Residual Variation

In general, several conservative techniques are available for detecting cracks in concrete bridges but they have significant limitations, including low accuracy and efficiency. Due to the expansion of the neural network method, the performance of digital image processing based crack identification has recently diminished. Many single classifier approaches are used to detect the cracks with high accuracy. The classifiers are not concentrating on random fluctuation in the training dataset and also it reflects in the final output as an over-fitting phenomenon. Though this model contains many parameters to justify the training data, it fails in the residual variation. These residual variations are frequent in UAV recorded photos as well as many camera images. To reduce this challenge, a noise reduction technique is utilized along with an SVM classifier to reduce classification error. The proposed technique is more resourceful by performing classification via SVM approach, and further the feature extraction and network training has been implemented by using the CNN method. The captured digital images are processed by incorporating the bending test through reinforced concrete beams. Moreover, the proposed method is determining the widths of the crack by employing binary conversion in the captured images. The proposed model outperforms conservative techniques, single type classifiers, and image segmentation type process methods in terms of accuracy. The obtained results have proved that, the proposed hybrid method is more accurate and suitable for crack detection in concrete bridges especially in the unmanned environment.

scholarly journals A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique

2020 ◽  
Vol 10 (21) ◽  
pp. 7918 ◽  
Author(s):  
Md Arafat Habib ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Reinforced Concrete ◽  
Crack Detection ◽  
Concrete Beams ◽  
Time History ◽  
Reinforced Concrete Beams ◽  
Bending Test ◽  
K Nearest Neighbor ◽  
Micro Cracks ◽  
Ae Signals

This study aims at characterizing crack types for reinforced concrete beams through the use of acoustic emission burst (AEB) features. The study includes developing a solid crack assessment indicator (CAI) accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal condition, micro-cracks, and macro-cracks (fractures) of concrete beam test specimens. Reinforced concrete (RC) beams undergo a three-point bending test, from which acoustic emission (AE) signals are recorded for further processing. From the recorded AE signals, crucial AEB features like the rise time, decay time, peak amplitude, AE energy, AE counts, etc. are extracted. The Boruta-Mahalanobis system (BMS) is utilized to fuse these features to provide us with a comprehensive and reliable CAI. The noise from the CAI is removed using the cumulative sum (CUMSUM) algorithm, and the final CAI plot is used to classify the three different conditions: normal, micro-cracks, and fractures using k-NN. The proposed method not only for the first time uses the entire time history to create a reliable CAI, but it can meticulously distinguish between micro-cracks and fractures, which previous works failed to deal with in a precise manner. Results obtained from the experiments display that the CAI built upon AEB features and BMS can detect cracks occurring in early stages, along with the gradually increasing damage in the beams. It also soundly outperforms the existing method by achieving an accuracy (classification) of 99.61%, which is 17.61% higher than the previously conducted research.

scholarly journals A Crack Identification Method for Concrete Structures Using Improved U-Net Convolutional Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Wenting Qiao ◽  
Hongwei Zhang ◽  
Fei Zhu ◽  
Qiande Wu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Concrete Beams ◽  
Concrete Structures ◽  
Reinforced Concrete Beams ◽  
Bending Test ◽  
Crack Identification ◽  
Identification Method ◽  
Proposed Model

The traditional method for detecting cracks in concrete bridges has the disadvantages of low accuracy and weak robustness. Combined with the crack digital image data obtained from bending test of reinforced concrete beams, a crack identification method for concrete structures based on improved U-net convolutional neural networks is proposed to improve the accuracy of crack identification in this article. Firstly, a bending test of concrete beams is conducted to collect crack images. Secondly, datasets of crack images are obtained using the data augmentation technology. Selected cracks are marked. Thirdly, based on the U-net neural networks, an improved inception module and an Atrous Spatial Pyramid Pooling module are added in the improved U-net model. Finally, the widths of cracks are identified using the concrete crack binary images obtained from the improved U-net model. The average precision of the test set of the proposed model is 11.7% higher than that of the U-net neural network segmentation model. The average relative error of the crack width of the proposed model is 13.2%, which is 18.6% less than that measured by using the ACTIS system. The results indicate that the proposed method is accurate, robust, and suitable for crack identification in concrete structures.

scholarly journals Construction of Concrete Surface Crack Recognition Model Based on Digital Image Processing Technology

2021 ◽  
Vol 2074 (1) ◽  
pp. 012067
Author(s):  
Yuzhong Kang ◽  
Aimin Yu ◽  
Wenquan Zeng
Keyword(s):  
Image Processing ◽  
Prestressed Concrete ◽  
Crack Width ◽  
Crack Detection ◽  
Concrete Surface ◽  
Concrete Bridges ◽  
Crack Identification ◽  
Detection Accuracy ◽  
Depth Analysis ◽  
Class B

Abstract In this paper, the bridge crack detection method based on digital images is studied. In-depth analysis and evaluation are performed on the image processing algorithms such as image graying, resolution of checkerboard corner pixel rate, filtering denoising, and edge detection, etc. The calculation and software system for bridge crack width based on videos (or images) is implemented, and 15 bridge crack images are used to verify its crack detection accuracy. The results suggest that the proposed crack identification method in this paper can be used for the crack detection of reinforced concrete bridges and class B prestressed concrete bridges properly. When the crack width is greater than 0.3 mm, the calculated crack width value based on images is very close to the measured value.

Influence of Nano Particles in the Flexural Behavior of High-Strength Reinforced Concrete Beams

2021 ◽  
Vol 1160 ◽  
pp. 25-43
Author(s):  
Naglaa Glal-Eldin Fahmy ◽  
Rasha El-Mashery ◽  
Rabiee Ali Sadeek ◽  
L.M. Abd El-Hafaz
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
High Strength Concrete ◽  
High Strength ◽  
Reinforced Concrete Beams ◽  
Nano Particles ◽  
Flexural Behavior ◽  
Single Type ◽  
Strength Concrete ◽  
Flexural Ductility

High strength concrete (HSC) characterized by high compressive strength but lower ductility compared to normal strength concrete. This low ductility limits the benefit of using HSC in building safe structures. Nanomaterials have gained increased attention because of their improvement of mechanical properties of concrete. In this paper we present an experimental study of the flexural behavior of reinforced beams composed of high-strength concrete and nanomaterials. Eight simply supported rectangular beams were fabricated with identical geometries and reinforcements, and then tested under two third-point loads. The study investigated the concrete compressive strength (50 and 75 N/mm2) as a function of the type of nanomaterial (nanosilica, nanotitanium and nanosilica/nanotitanium hybrid) and the nanomaterial concentration (0%, 0.5% and 1.0%). The experimental results showed that nano particles can be very effective in improving compressive and tensile strength of HSC, nanotitanium is more effective than nanosilica in compressive strength. Also, binary usage of hybrid mixture (nanosilica + nanotitanium) had a remarkable improvement appearing in compressive and tensile strength than using the same percentage of single type of nanomaterials used separately. The reduction in flexural ductility due to the use of higher strength concrete can be compensated by adding nanomaterials. The percentage of concentration, concrete grade and the type of nanomaterials, could predominantly affect the flexural behavior of HSRC beams.

scholarly journals Multiple crack identification in stepped beam by measurements of natural frequencies

2014 ◽  
Vol 36 (2) ◽  
pp. 119-132
Author(s):  
Nguyen Tien Khiem ◽  
Duong The Hung ◽  
Vu Thi An Ninh
Keyword(s):  
Crack Detection ◽  
Natural Frequencies ◽  
Rayleigh Quotient ◽  
Crack Identification ◽  
Multiple Cracks ◽  
Simple Relationship ◽  
Multiple Crack ◽  
Scanning Method ◽  
New Approach ◽  
Efficient Procedure

A new approach is proposed for calculating natural frequencies and crack detection in a stepped cantilever beam with arbitrary number of cracks. This is based an explicit expression of the natural frequencies in term of crack parameter derived in the form similar to the so-called Rayleigh quotient for vibrating beam. The obtained simple relationship between natural frequencies and crack parameters enables not only accurate calculating the natural frequencies but also to develop an efficient procedure for detecting multiple cracks from given natural frequencies. The proposed technique called crack scanning method is illustrated and validated by numerical results.

scholarly journals Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3956
Author(s):  
Shan He ◽  
Huaiyong Shao ◽  
Wei Xian ◽  
Shuhui Zhang ◽  
Jialong Zhong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Single Type ◽  
Support Vector ◽  
Svm Classifier ◽  
Cultivated Land ◽  
Remote Sensing Images ◽  
Data Set ◽  
Abandoned Land ◽  
Spatio Temporal ◽  
Sentinel 2

Hilly areas are important parts of the world’s landscape. A marginal phenomenon can be observed in some hilly areas, leading to serious land abandonment. Extracting the spatio-temporal distribution of abandoned land in such hilly areas can protect food security, improve people’s livelihoods, and serve as a tool for a rational land plan. However, mapping the distribution of abandoned land using a single type of remote sensing image is still challenging and problematic due to the fragmentation of such hilly areas and severe cloud pollution. In this study, a new approach by integrating Linear stretch (Ls), Maximum Value Composite (MVC), and Flexible Spatiotemporal DAta Fusion (FSDAF) was proposed to analyze the time-series changes and extract the spatial distribution of abandoned land. MOD09GA, MOD13Q1, and Sentinel-2 were selected as the basis of remote sensing images to fuse a monthly 10 m spatio-temporal data set. Three pieces of vegetation indices (VIs: ndvi, savi, ndwi) were utilized as the measures to identify the abandoned land. A multiple spatio-temporal scales sample database was established, and the Support Vector Machine (SVM) was used to extract abandoned land from cultivated land and woodland. The best extraction result with an overall accuracy of 88.1% was achieved by integrating Ls, MVC, and FSDAF, with the assistance of an SVM classifier. The fused VIs image set transcended the single source method (Sentinel-2) with greater accuracy by a margin of 10.8–23.6% for abandoned land extraction. On the other hand, VIs appeared to contribute positively to extract abandoned land from cultivated land and woodland. This study not only provides technical guidance for the quick acquirement of abandoned land distribution in hilly areas, but it also provides strong data support for the connection of targeted poverty alleviation to rural revitalization.

Reference-Free Breathing Crack Identification of Beam-Like Structures Using an Enhanced Spatial Fourier Power Spectrum with Exponential Weighting Functions

2019 ◽  
Vol 19 (02) ◽  
pp. 1950017 ◽  
Author(s):  
J. Prawin ◽  
A. Rama Mohan Rao
Keyword(s):  
Fatigue Crack ◽  
Power Spectrum ◽  
Crack Detection ◽  
Crack Problem ◽  
Experimental Studies ◽  
Crack Identification ◽  
Successful Implementation ◽  
Breathing Crack ◽  
Fourier Power Spectrum ◽  
Exponential Weighting

Detection of incipient damage of structures at the earliest possible stage is desirable for successful implementation of any health monitoring system. In this paper, we focus on breathing crack problem and present a new reference-free algorithm for fatigue crack detection, localization, and characterization for beam-like structures. We use the spatial curvature of the Fourier power spectrum as a damage sensitive feature for fatigue crack identification. An exponential weighting function that takes into account nonlinear dynamic signatures, such as sub- and superharmonics, is proposed in the Fourier power spectrum in order to enrich the damage-sensitive features of the structure. Both numerical and experimental studies have been carried out to test and verify the proposed algorithm.

Crack Grid Detection and Calculation Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Liyang Xiao ◽  
Wei Li ◽  
Ju Huyan ◽  
Zhaoyun Sun ◽  
Susan Tighe
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Image Denoising ◽  
Crack Detection ◽  
Experimental Results ◽  
Crack Identification ◽  
Threshold Segmentation ◽  
Improve Image Quality

This paper aims to develop a method of crack grid detection based on convolutional neural network. First, an image denoising operation is conducted to improve image quality. Next, the processed images are divided into grids of different, and each grid is fed into a convolutional neural network for detection. The pieces of the grids with cracks are marked and then returned to the original images. Finally, on the basis of the detection results, threshold segmentation is performed only on the marked grids. Information about the crack parameters is obtained via pixel scanning and calculation, which realises complete crack detection. The experimental results show that 30×30 grids perform the best with the accuracy value of 97.33%. The advantage of automatic crack grid detection is that it can avoid fracture phenomenon in crack identification and ensure the integrity of cracks.

RETROFITTING PERFORMANCE OF REINFORCED CONCRETE ONE WAY SLAB

2016 ◽  
Vol 78 (5-3) ◽  
Author(s):  
Norliyati Mohd Amin ◽  
Nur Aqilah Aziz ◽  
Ilya Joohari ◽  
Anizahyati Alisibramulisi
Keyword(s):  
Reinforced Concrete ◽  
Concrete Slab ◽  
Load Capacity ◽  
Reinforced Concrete Beams ◽  
Fiber Reinforced Polymer ◽  
Bending Test ◽  
Strength Performance ◽  
Concrete Crack ◽  
Reinforced Polymer ◽  
Structural Capacity

Cracks in concrete structure have always been a big threat on the strength of the concrete. Crack is one of the common deterioration observed in reinforced concrete beams and slabs. Concrete cracking is a random process, highly variable and influenced by many factors. To restore the structural capacity of the concrete damages, retrofitting and strengthening are required. There are several techniques that are used for retrofitting and strengthening reported in the literature [1], [2], [3]. This paper investigates the strength performance of retrofitting and strengthening methods of reinforced concrete one-way slab. Flexural bending test are performed on three different concrete slab of size 1000 mm x 500 mm x 75 mm. The methods that are used for retrofit are epoxy injection and patching and for the strengthening is lamination of carbon fiber reinforced polymer. The slabs were loaded to a certain stage where the cracks were formed for retrofitting and strengthening procedure. The achieved failure mode and load capacity of the concrete slab were observed. The repaired techniques for restoring and improving the structural capacity of cracked concrete slabs were analyzed. The ultimate load achieved for the epoxy injection laminate was 19.60 kN followed by CFRP laminate and patching that were 17.64 kN and 17.03 kN respectively. While the deflection value for the three specimens were 14.42 mm, 4.49 mm and 7.036 mm.  

scholarly journals Pavement Crack Detection and Segmentation Method Based on Improved Deep Learning Fusion Model

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Xiaoran Feng ◽  
Liyang Xiao ◽  
Wei Li ◽  
Lili Pei ◽  
Zhaoyun Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Geometric Parameter ◽  
Crack Detection ◽  
Crack Identification ◽  
Single Shot ◽  
Road Maintenance ◽  
Fusion Model ◽  
Detection Model ◽  
Crack Classification

Pavement damage is the main factor affecting road performance. Pavement cracking, a common type of road damage, is a key challenge in road maintenance. In order to achieve an accurate crack classification, segmentation, and geometric parameter calculation, this paper proposes a method based on a deep convolutional neural network fusion model for pavement crack identification, which combines the advantages of the multitarget single-shot multibox detector (SSD) convolutional neural network model and the U-Net model. First, the crack classification and detection model is applied to classify the cracks and obtain the detection confidence. Next, the crack segmentation network is applied to accurately segment the pavement cracks. By improving the feature extraction structure and optimizing the hyperparameters of the model, pavement crack classification and segmentation accuracy were improved. Finally, the length and width (for linear cracks) and the area (for alligator cracks) are calculated according to the segmentation results. Test results show that the recognition accuracy of the pavement crack identification method for transverse, longitudinal, and alligator cracks is 86.8%, 87.6%, and 85.5%, respectively. It is demonstrated that the proposed method can provide the category information for pavement cracks as well as the accurate positioning and geometric parameter information, which can be used directly for evaluating the pavement condition.

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