
International Journal of Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 247, P. 127153 - 127153
Published: April 26, 2025
Language: Английский
International Journal of Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 247, P. 127153 - 127153
Published: April 26, 2025
Language: Английский
Published: Jan. 1, 2025
Language: Английский
Citations
0International Journal for Numerical and Analytical Methods in Geomechanics, Journal Year: 2025, Volume and Issue: unknown
Published: April 3, 2025
ABSTRACT In this study, a hybrid black‐ and white‐box machine learning (ML) framework is proposed for matric suction estimation in compacted fine‐grained soils, utilizing ML techniques, namely, particle swarm optimized support vector regression (PSO‐SVR) multi‐gene genetic programming (MGGP). This objective achieved through developing novel ML‐based method designing requisite soil parameters, including new parameter, the “effective degree of aggregation”. parameter captures influence varying structures associated with different initial water content conditions soils estimating suction. Additionally, sensitivity analyses are performed to better understand significance effective aggregation other critical properties. Explicit equations derived from MGGP models, enabling their use using spreadsheets alleviating reliance on complex tools. The models promising prediction hydro‐mechanical behavior related properties, facilitating application conventional geotechnical engineering practice.
Language: Английский
Citations
0International Journal of Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 247, P. 127153 - 127153
Published: April 26, 2025
Language: Английский
Citations
0