What is the improtance of ANN over empirical realtions for bond stress?
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Artificial Neural Networks (ANNs) offer significant advantages over empirical relations for predicting bond stress in concrete structures. ANNs excel in accuracy and precision due to their ability to capture complex non-linear interactions and patterns within the data, leading to more reliable predictions. Unlike traditional empirical models, ANNs do not rely on predefined mathematical formulations or empirical parameters, making them versatile and adaptable to various scenarios. The use of ANNs in predicting bond stress between reinforcement and concrete has shown superior performance compared to conventional methods, as evidenced by the high agreement between predicted and experimental results. This highlights the importance of leveraging ANN-based models for more accurate and reliable estimations of bond stress in concrete structures.
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| Papers (4) | Insight |
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12 Citations | ANN provides more accurate results than empirical relations for bond stress calculations between UHPC and steel bars, as shown by a higher coefficient of determination (R^2) of 0.984 compared to 0.977. |
| ANNs offer advantages over empirical relations for bond stress by not requiring explicit mathematical formulations or empirical parameters, allowing indirect training using measurable experimental variables like displacements and forces. | |
| ANNs offer superior accuracy in predicting tendon stress compared to empirical models, as shown in the study. They provide more precise estimations based on a database of concrete members. | |
01 Sep 2021 6 Citations | ANN is preferred over empirical relations for bond stress prediction due to its superior performance in considering corrosion level, concrete strength, and reinforcement bar characteristics, enhancing accuracy and applicability. |
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