A Machine Learning Based Model to Assess Flexural Strength of Corroded Reinforced Concrete Beams

Sharma, Arjun; Sharma, Somain; Kumar, Kuldeep

doi:10.1007/978-3-031-39663-2_81

Lecture Notes in Civil Engineering

2023

DOI: 10.1007/978-3-031-39663-2_81

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A Machine Learning Based Model to Assess Flexural Strength of Corroded Reinforced Concrete Beams

Arjun Sharma,

Somain Sharma,

Kuldeep Kumar

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2024

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Cited by 2 publications

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Prediction of central deflection and slenderness limit for lateral stability of simply supported concrete beam using machine learning techniques

Mustafa,

Ahmad,

Kumar

et al. 2024

Asian J Civ Eng

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Prediction of central deflection and slenderness limit for lateral stability of simply supported concrete beam using machine learning techniques

Mustafa,

Ahmad,

Kumar

et al. 2024

Asian J Civ Eng

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Role of artificial intelligence (AI) and machine learning (ML) in the corrosion monitoring processes

Saminathan,

Nashali,

Haqawi

et al. 2024

Zas Mat

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When it comes to the upkeep of engineering structures in a variety of industries, corrosion monitoring systems are an extremely important components. In particular, applications such as storage tanks for hazardous chemicals and weight-bearing structures of large engineering constructions are at the forefront of providing attention to relevance. This is due to the fact that failures experienced by these applications can potentially result in catastrophic consequences. As a result, contemporary methods make use of the application of concepts connected with machine learning and artificial intelligence in order to efficiently monitor and identify corrosion related damages. As a consequence of this, the monitoring system is able to provide the control of the industrial structures with minute-by-minute updates. Therefore, the catastrophe is prevented to a significant degree, and there is a significant possibility of lowering the costs associated with technical procedures that require maintenance. Within the scope of this paper, a comprehensive analysis is conducted on the applications of artificial intelligence and machine learning techniques that are utilized in corrosion monitoring systems across a wide range of industries. Through this assessment, the solutions and efficient corrosion monitoring methods that are specific to the domains made available. Consequently, the purpose of this work is to determine the appropriate technique of monitoring systems for each and every corrosion-related disorder.

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A Machine Learning Based Model to Assess Flexural Strength of Corroded Reinforced Concrete Beams

Cited by 2 publications

References 33 publications

Prediction of central deflection and slenderness limit for lateral stability of simply supported concrete beam using machine learning techniques

Prediction of central deflection and slenderness limit for lateral stability of simply supported concrete beam using machine learning techniques

Role of artificial intelligence (AI) and machine learning (ML) in the corrosion monitoring processes

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