Integrating Artificial Neural Networks for Early Detection and Analysis of Building Cracks
- Author Kingsley C. Ezekiel
- Co-Author Omonigho B. Otanocha, Chiamaka Jennifer Ilokwu
- DOI https://ww
- Country : United Kingdom
- Subject :
Keywords : Cracks detection, Crack measurement, Visual ground penetration, ANN Model.
Abstract
Building cracks are a common issue that can have significant consequences, such as structural damage and safety hazards. Detecting cracks at an early stage is crucial to prevent further damage and ensure the safety of building occupants. This paper aims to provide an overview of the causes and types of building cracks and discuss various techniques for their detection and monitoring. The detection techniques include visual inspection, measuring crack width and depth, ultrasonic testing, X-ray testing, infrared thermography, and ground-penetrating radar. The monitoring of cracks is also discussed, highlighting the importance of monitoring and various techniques for it. The prevention and repair of building cracks are also covered in this paper. The prevention measures include proper design and construction techniques, while repair techniques include filling and sealing of cracks. The paper concludes with a Case-Study using ANN model for the prediction of building crack, with a summary of key points and future research directions. The information provided in this paper can be useful for building owners, engineers, and construction professionals in the prevention, detection, and repair of building cracks.
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