چکیده مقاله
The internal friction angle of soil is considered one of the most influential parameters in geostructure design and plays a core role in assessing its safety and stability In the current study, a precise prediction model for the internal friction angle of soil was established using high order machine learning techniques such as Random Forest, XGBoost, and Extra Trees Regressor The total number of 245 soil samples taken from numerous boreholes formed a dataset, and standard penetration test SPT N value, the soil type, the unit weight, the void ratio, the elevation, and the thickness of the soil cover were utilized as the input for the models The performance outcome for the models using the statistical indices such as the coefficient of determination R² , the Mean Absolute Error MAE , the Mean Squared Error MSE , and the Root Mean Squared Error RMSE verified that the Random Forest algorithm had the optimum performance on the test set with the outcome for R² = 93 75%, MAE 1 2909, MSE = 3 1341, and RMSE = 1 7703 The above approach can be deemed a swift, cost effective, and accurate methodology compared with the traditional methodology and can be utilized as a replacement for the traditional methodology for geotechnical professionals
کلیدواژهها
نویسندگان
Seyed Emad Miri
Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
Hamid Mohammadnezhad
Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
شیوه ارجاع
Miri, Seyed Emad and Mohammadnezhad, Hamid,1404,Data-Driven Estimation of Soil Internal Friction Angle Using Machine Learning Techniques,14th International Congress on Civil Engineering,Tehran
ارائهشده در
مجموعه مقالات چهاردهمین کنگره بین المللی مهندسی عمران29 مهر 1404 · تهران