Analysis of cracks in structures and buildings

Velumani, P.; Mukilan, K.; Varun, G. S.; Divakar, S.; Doss, R. Muhil; Ganeshkumar, P.

doi:10.1088/1742-6596/1706/1/012116

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Cracks in walls are caused by several factors, as mentioned [27] in a study of various cracks in the building. Similar studies were also carried out [28] by analyzing the causes of cracks. Research [25] mentions the causes of non-structural cracks, and this damage does not harm the building.…”

Section: Related Workmentioning

confidence: 79%

“…The non-structural cracks do not usually risk safety, but they can be unsightly, give the impression of poor work, or give a sense of instability [25,26]. This kind of crack is caused primarily by internally induced stresses in construction materials or might result in poor craftsmanship [27,28]. In comparison, structural cracks are caused by improper design, defective construction, or overloading, which can endanger a building's safety.…”

Section: Crack On the Buildingmentioning

confidence: 99%

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Diagnosis and Repair of the Cracking House Next to the River

Pramono

Mahendra

Wijaya

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Natural disasters and human error factors can cause damage to buildings. Human error causes various issues, including inadequate craftsmanship in the mixing of building materials and preliminary structural design and specification. Inadequate knowledge of the building’s structure and the type of soil on which it stands is also one of the fault designs in construction. This study utilises a case study methodology divided into three sections: soil analysis, building analysis, and techniques for avoiding and repairing the research object. A literature review is essential to ascertain the type of soil and the proper footing to utilise when examining the soil. In the building analysis, it is necessary to determine the damage of the building and its repair. These steps demanded were to prevent further damage by adding structures to reinforce existing structures in load distribution. After determining that the structure is safe from further damage, the final step is to repair the walls, floor, and ceiling. These improvements are part of an effort to ensure the building’s long term sustainability. The final phase in home restoration is to decorate the interior to make the residents feel comfortable.

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Section: Related Workmentioning

confidence: 79%

Section: Crack On the Buildingmentioning

confidence: 99%

Diagnosis and Repair of the Cracking House Next to the River

Pramono

Mahendra

Wijaya

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Infrastructures are paramount in modern civilization, and their longevity and safety are essential for timely maintenance, as they can have severe consequences for the well-being of humankind and properties. However, their structural health can deteriorate over time due to various factors, including natural calamities and external forces, leading to the emergence of cracks [9]. This review highlights the composition of crack detection and analysis techniques that can be used to assess the health of buildings, specifically the implementation of image processing and the potential application of learning-based techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification-based Method for Wall Crack Detection System

Ramli,

Rahiman,

Krishnan

2023

J. Phys.: Conf. Ser.

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It is imperative to ensure that building inspectors have adequate resources and tools to conduct their inspections efficiently and effectively. Relying solely on manual labour to check for wall cracks is inconvenient and may prove inefficient and poor use of time and financial recourses. Besides, there are concerns regarding the need for skilled inspectors due to their limited accessibility and the subjective nature of their evaluations. Previously, image processing and artificial intelligence have been independently utilized to identify wall cracks and estimate their width. However, more can be done when integrating these two approaches to produce a comprehensive solution. This study presented a technique to indicate wall cracks utilizing a pre-trained Convolutional Neural Network (CNN) model called Squeezenet. Then, the following image processing can precisely estimate the width of the cracks in pixels. Based on the total models studied, 78% were successfully detected and classified into their respective crack groups. Although 22% of the remaining models were mistakenly classified, the system still managed to detect the presence of cracks in them accurately. This study only considers analyzing projected cracks categorized as minor, moderate and major. Nevertheless, the discussion does not address the translation of pixel approximations into their respective physical measurements.

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“…It was highlighted that the errors of calculating the lengths and widths were 21.91% and 7.05%, respectively. Velumani et al (2020) proposed an image processing-based approach for the identification and examination of cracks in buildings. The developed approach adopted minimum maximum gray level differentiation algorithm to better discriminate the cracking pixels from the background.…”

Section: Deep Learning and De Algorithmsmentioning

confidence: 99%

On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

Abdelkader

2021

SASBE

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PurposeCracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the timely inspection, health diagnosis and maintenance of infrastructures. However, conventional visual inspection-based methods are criticized for being subjective, greatly affected by inspector's expertise, labor-intensive and time-consuming.Design/methodology/approachThis paper proposes a novel self-adaptive-based method for automated and semantic crack detection and recognition in various infrastructures using computer vision technologies. The developed method is envisioned on three main models that are structured to circumvent the shortcomings of visual inspection in detection of cracks in walls, pavement and deck. The first model deploys modified visual geometry group network (VGG19) for extraction of global contextual and local deep learning features in an attempt to alleviate the drawbacks of hand-crafted features. The second model is conceptualized on the integration of K-nearest neighbors (KNN) and differential evolution (DE) algorithm for the automated optimization of its structure. The third model is designated for validating the developed method through an extensive four layers of performance evaluation and statistical comparisons.FindingsIt was observed that the developed method significantly outperformed other crack and detection models. For instance, the developed wall crack detection method accomplished overall accuracy, F-measure, Kappa coefficient, area under the curve, balanced accuracy, Matthew's correlation coefficient and Youden's index of 99.62%, 99.16%, 0.998, 0.998, 99.17%, 0.989 and 0.983, respectively.Originality/valueLiterature review lacks an efficient method which can look at crack detection and recognition of an ensemble of infrastructures. Furthermore, there is absence of systematic and detailed comparisons between crack detection and recognition models.

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Analysis of cracks in structures and buildings

Cited by 5 publications

References 4 publications

Diagnosis and Repair of the Cracking House Next to the River

Diagnosis and Repair of the Cracking House Next to the River

Classification-based Method for Wall Crack Detection System

On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

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