Stiffness and Strength Estimation of Damaged Unreinforced Masonry Walls Using Crack Pattern

Dolatshahi, Kiarash M.; Beyer, Katrin

doi:10.1080/13632469.2019.1693446

Cited by 27 publications

(38 citation statements)

References 30 publications

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“…So to get more accurate results as compared to these methodologies refined crack monitoring and measuring methods can be used that are used for detecting complex crack patterns. [6] Image-based measurements also comes under this category. These methods can also extract information from the surface structures using digital images.…”

Section: Introduction a Developmentmentioning

confidence: 99%

Crack Monitoring in the Welding Parts of Different Alloys of Aluminium using Digital Image Correlation

Hanief¹

2021

IJRASET

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Welding joints are directly or indirectly prone to different stress and strain damages. During the test performance of welding material the crack appears and these cracks continue to grow and then move from one surface region to another region. This load is continued to apply till the test fails. It is done to check the strength limit of the welded part. Also it is not easy to find the starting point where the cracks start to appear. There are various ways or techniques to find the cracks in the welded parts during the load tests. It includes ultrasound, thermography and use of strain gauges etc. In this dissertation work a procedure for finding cracks in the welded parts after performing certain strength tests utilizing digital image correlation (DIC) method is proposed. The main benefit of this method is that one can find the cracks of macroscopic category and also monitoring can be done during performing of tests. Here digital images are captured during the different load cycles. As load applied is more than the strength of material cracks start appear to visible. So it is possible to find and detect the location of crack on the welded part. Since the digital images are stored in the database so it becomes easier for verification and comparison. This test is performed only for the cracks present on the surface of the welded material.I.

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Section: Introduction a Developmentmentioning

confidence: 99%

Crack Monitoring in the Welding Parts of Different Alloys of Aluminium using Digital Image Correlation

Hanief¹

2021

IJRASET

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“…34,35 In another study, Dolatshahi and Beyer presented a mathematical index for quantifying stiffness degradation of unreinforced masonry walls based on crack pattern characteristics. 36 In these studies, the authors have assumed that the crack pattern is collected by an automated framework, and they have just focused on the derivation of predictive equations based on the crack pattern. To find fundamental cracks of damaged prestressed concrete box girder bridge, Guoliang et al investigated types of crack on the surfaces and attempted to classify the cracks based on crack patterns.…”

Section: Introductionmentioning

confidence: 99%

“…8 Since the manned inspection is prone to significant errors for monitoring cracks and the accuracy of this method depends on the experience and knowledge of the inspectors, many researchers have focused on computer vision methods to automate crack detection procedures. 36,38,39 The purpose of this study is to automate the crack pattern detection and feature extraction of crack surfaces. This paper introduces a detailed survey on an innovative machine learning-based procedure, which is called Arc Length method to specify the crack features.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers developed probabilistic models to estimate damage assessment of concrete shear walls using crack patterns 34,35 . In another study, Dolatshahi and Beyer presented a mathematical index for quantifying stiffness degradation of unreinforced masonry walls based on crack pattern characteristics 36 . In these studies, the authors have assumed that the crack pattern is collected by an automated framework, and they have just focused on the derivation of predictive equations based on the crack pattern.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, Codes recommend manned inspection for monitoring of cracks 8 . Since the manned inspection is prone to significant errors for monitoring cracks and the accuracy of this method depends on the experience and knowledge of the inspectors, many researchers have focused on computer vision methods to automate crack detection procedures 36,38,39 …”

Section: Introductionmentioning

confidence: 99%

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Arc Length method for extracting crack pattern characteristics

Asjodi

Daeizadeh

Hamidia

et al. 2020

Struct Control Health Monit

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Although manual crack inspection has been widely used for structural health monitoring over the last decades, the development of computer vision methods allows continuous monitoring and compensates the human judgment inaccuracy. In this study, an image-based method entitled Arc Length method is introduced for extracting crack pattern characteristics, including crack width and crack length. The method contains two major steps; in the first step, the crack zones are estimated in the whole image. Afterwards, the algorithm finds the start point, follows the crack pattern, and measures the crack features, such as crack width, crack length, and crack pattern angle. The efficiency of the method is validated using a few case studies from cracked structural concrete shear walls tested in the laboratory under quasi-static cyclic loadings. The case studies show high efficiency of the proposed method in following the crack patterns even when the crack propagates in two or more branches. The application of this approach plays a significant role in crack monitoring of infrastructures, such as concrete bridges and tunnels.

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Three‐dimensional fragility surface for reinforced concrete shear walls using image‐based damage features

Asjodi

Dolatshahi

Burton

2023

Earthq Engng Struct Dyn

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This paper proposes a new data-driven method to generate three-dimensional fragility surfaces for post-earthquake damage assessment of reinforced concrete (RC) shear walls (SWs) using image-based damage features. A research database comprised of 212 images corresponding to 66 damaged reinforced concrete shear walls tested under quasi-static cyclic loads is utilized. The walls are categorized into three damage states defined based on different load points along the backbone curve. Convolutional kernel-based filters are then employed to measure the crack patterns and crushed areas from images of the damaged walls. A set of 360,000 sub-cracks from the 212 images is analyzed using Gaussian mixture modeling to distinguish between shear and flexural cracking. These two types of cracking, along with crushing, are the prominent characteristics used to determine the damage states of the RCSWs. The extracted features of shear and flexural cracking are also compressed into a unique cracking index using the Principal Component Analysis theory for dimensionality reduction. Using the compressed feature of cracking and crushing, a new methodology for generating three-dimensional fragility surfaces, entitled the Box-counting method, is introduced, and the damage surfaces are developed based on visual damage features. The damage surfaces are finally formulated using statistical models or machine learning regression learners. The proposed fragility surface can be used for postearthquake damage state identification, risk assessment, and loss estimation of damaged RCSWs.

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Stiffness and Strength Estimation of Damaged Unreinforced Masonry Walls Using Crack Pattern

Cited by 27 publications

References 30 publications

Crack Monitoring in the Welding Parts of Different Alloys of Aluminium using Digital Image Correlation

Crack Monitoring in the Welding Parts of Different Alloys of Aluminium using Digital Image Correlation

Arc Length method for extracting crack pattern characteristics

Three‐dimensional fragility surface for reinforced concrete shear walls using image‐based damage features

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