What is it about?
It is about using artificial intelligence (AI) methods, specifically a hyper parameterized artificial neural network approach, to predict the factor of safety against liquefaction in soil. It explores the potential of machine learning techniques to improve the accuracy of predicting soil liquefaction and mitigate associated risks.
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Why is it important?
Soil liquefaction is a significant seismic hazard during earthquakes, and predicting its occurrence accurately is crucial for mitigating associated risks. The use of AI techniques, such as machine learning, can improve the accuracy of predicting soil liquefaction and help in assessing liquefaction risk. This study highlights the potential of AI methods for predicting soil liquefaction and emphasizes the importance of hyperparameter optimization for improving model performance.
Perspectives
The study suggests that the proposed hyper parameterized artificial neural network approach outperformed other machine learning algorithms in predicting soil liquefaction with a high degree of accuracy. The findings of this study have practical implications for improving liquefaction risk assessment and mitigating the associated hazards. The use of AI techniques in predicting soil liquefaction can be further explored and developed to improve the accuracy of predictions and reduce the risks associated with soil liquefaction during earthquakes.
Dr. Caner Erden
Sakarya University of Applied Sciences
Read the Original
This page is a summary of: A hyper parameterized artificial neural network approach for prediction of the factor of safety against liquefaction, Engineering Geology, June 2023, Elsevier,
DOI: 10.1016/j.enggeo.2023.107109.
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