Pile foundation section. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Shearing force in the pressure zone. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Strain-stress maps of vertical pile foundation. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Displacement-inclination variation graph. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Soil modeling and mechanical parameters. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Location of monitored piles. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Axial force in the pressure zone. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Pile-soil interaction. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Bending moment in the tension zone. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Sketch of forces on vertical and inclined piles. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Displacement cloud maps. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Morphing mesh. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Bending moment in the pressure zone. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Axial forces in the tension zone. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

VPF and VIPF. by Maogang Tian (21485116)

“…Furthermore, parametric studies reveal that the pile base displacement exhibits a non-linear trend of initially decreasing and then increasing with larger inclination angles of the inclined piles. …”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”