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Efficient Estimation of Shear Strength Parameters of Unsaturated Soils Through Artificial Neural Networks | ||
| Geopersia | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 30 دی 1404 اصل مقاله (3.18 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/geope.2026.404511.648848 | ||
| نویسندگان | ||
| Habib Rahimimanbar1؛ Gholamreza Shoaei* 1؛ Mohammad Fathollahi2 | ||
| 1Department of Geology, Engineering Geology Group, Faculty of Basic Sciences, Tarbiat Modares University, Iran | ||
| 2Department of Geology, Faculty of Basic Sciences, Kurdistan University, Iran | ||
| چکیده | ||
| Laboratory testing of unsaturated soil shear strength parameters is often time-consuming, expensive, and requires specialized equipment. This study explores Artificial Neural Networks (ANNs) as an alternative, systematically optimizing the predictive model through a novel, multi-stage analysis investigating activation functions and iteratively tuning hidden layer counts and neuron numbers. A comprehensive evaluation of 195 network configurations was conducted using a dataset of 490 points compiled from 14 soil types, primarily fine-grained soils. Modeling identified the Bayesian regularization (TRAIN BR) function as superior (R=0.97R=0.97R=0.97). Subsequent expansion to three, four, and five hidden layers (with neuron counts from 50 down to 10) determined the most effective architecture. The four-layer Multilayer Perceptron (MLP) network emerged as the optimal configuration, achieving exceptional performance with an overall R2R^2R2 value of 0.98. Model validation utilized rigorous approaches. Initially, reserved samples confirmed the four-layer network’s high accuracy for cohesion. Secondly, predictions were compared with established empirical methods, demonstrating significantly higher accuracy. Finally, five independently prepared samples tested via in-house Direct Shear Testing further validated the model’s reliability. This external validation confirmed close agreement, showing prediction errors ranging from 1% to 11% for friction angle and 3% to 14% for cohesion. While further validation using a wider diversity of soil types and a larger external sample size is required to confirm generalizability, these results firmly establish ANNs as a powerful, accurate, and cost-effective tool for geotechnical engineers providing reliable estimates of unsaturated soil shear strength parameters. | ||
| کلیدواژهها | ||
| ANN-based modeling of unsaturated soils؛ Shear Strength Prediction؛ Cohesion and Friction Angle Estimation؛ Soil Suction؛ MLP Architecture Optimization | ||
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آمار تعداد مشاهده مقاله: 83 تعداد دریافت فایل اصل مقاله: 38 |
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