A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure

Frontiers in Built Environment, Journal Year: 2024, Volume and Issue: 10

Published: Oct. 25, 2024

This paper presents a novel approach for assessing liquefaction potential by integrating Dynamic Cone Penetration Test (DCPT) data with advanced machine learning (ML) techniques. DCPT offers cost-effective, rapid, and adaptable method evaluating soil resistance, making it suitable assessment across diverse conditions. study establishes threshold criterion based on the ratio of penetration rate to dynamic resistance ( e / q d ), where values exceeding four indicate high susceptibility. ML models, including Support Vector Machine (SVM) optimized Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Firefly (FA), were employed predict using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, rate. The SVM-PSO model demonstrated superior performance, R 2 0.999 0.989 in training testing phases, respectively. proposed methodology sustainable accurate assessment, reducing environmental impact investigations, while ensuring reliable predictions. bridges gap between field computational techniques, providing powerful tool engineers assess risks design resilient infrastructures.

Language: Английский

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Reservoir temperature prediction based on characterization of water chemistry data—case study of western Anatolia, Turkey

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 6, 2024

Abstract Reservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry reservoir data applied four machine learning algorithms. These models considered various input factors underwent preprocessing steps like null value imputation, normalization, Pearson coefficient calculation. Cross-validation addressed volume issues, performance metrics were used model evaluation. The results revealed that our outperformed fluid geothermometers. All surpassed methods. XGBoost model, based on the F-3 combination, demonstrated best prediction accuracy with an R 2 0.9732, while Bayesian ridge regression using F-4 combination had lowest 0.8302. This study highlights potential accurate prediction, offering professionals a reliable tool selection advancing understanding resources.

Language: Английский

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Research on Adaptive Feature Optimization and Drilling Rate Prediction Based on Real-Time Data

Published: Jan. 1, 2024

Language: Английский

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A new approach to mechanical brittleness index modeling based on conventional well logs using hybrid algorithms

Earth Science Informatics, Journal Year: 2023, Volume and Issue: 16(4), P. 3387 - 3416

Published: Sept. 8, 2023

Language: Английский

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Wellbore stability and the establishment of a safe mud weight window

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 135 - 168

Published: Jan. 1, 2024

Language: Английский

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