Prediction of fracture energy of concrete notched beam using artificial neural network

Pathak, Sudhanshu; Vesmawala, Gaurang R.; Mane, Sachin J.

doi:10.1007/s42107-023-00674-6

Cited by 4 publications

(1 citation statement)

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“…Guo et al [32] proposed a machine learning-driven approach to evaluate and optimize compression-yielded fiber-reinforced polymer (FRP)-reinforced concrete beams with T sections. Pathak et al [33] introduced an artificial neural network model for predicting the fracture energy of concrete notched beams. They utilized a database of experimental results and numerical simulations to train the ANN model.…”

Section: Introductionmentioning

confidence: 99%

Integrating Image Processing and Machine Learning for the Non-Destructive Assessment of RC Beams Damage

Naderpour,

Abbasi,

Kontoni

et al. 2024

Buildings

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Non-destructive testing (NDT) is a crucial method for detecting damages in concrete structures. Structural damage can lead to functional changes, necessitating a range of damage detection techniques. Non-destructive methods enable the pinpointing of the location of the damage without causing harm to the structure, thus saving both time and money. Damaged structures exhibit alterations in their static and dynamic properties, primarily stemming from a reduction in stiffness. Monitoring these changes allows for the determination of the failure location and severity, facilitating timely repairs and reinforcement before further deterioration occurs. A systematic approach to damage detection and assessment is pivotal for fortifying structures and preventing structural collapse, which can result in both financial and human losses. In this study, we employ image processing to categorize damaged beams based on their crack growth and propagation patterns. We also utilize support vector machine (SVM) and k-nearest neighbor (KNN) methods to detect the type, location, and extent of failures in reinforced concrete beams. To provide context and relevance for the laboratory specimens, we will compare our findings to the results from controlled experiments in a controlled laboratory setting.

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Section: Introductionmentioning

confidence: 99%