Rapid population growth over the years has prompted the need to build critical infrastructures in hilly and mountainous areas. At the same time, the increment in the magnitude and frequency of extreme weather conditions due to climate change poses challenges to engineers to develop and monitor the safety of slopes. Consequently, researchers have devoted significant effort to slope stability assessment and prediction. However, slope stability assessment remains a complicated task. The task of slope stability assessment is further complicated when engineers need access to information and tools required to perform quick and accurate slope stability assessment. Due to the critical nature of slope stability assessment, this thesis aims to investigate challenges associated with the safety assessment of slopes and develop AI-based techniques and tools that provide quick solutions to challenging slope safety assessment problems.
This study first investigates the stability of three-layered soil slopes using gradient boosting machine (GBM) algorithm. To accomplish this task, factors such as slope angle (?o), friction angle (?′), depth factor (?⁄?), height ratio (?1⁄??), and undrained shear strength ratio (??1⁄??2), are used as inputs to predict the stability number of filled slopes. The results indicate that the slope stability estimation using the proposed GBM tool achieved good accuracy, speed, and convenience compared to other widely used ML methods such as SVM, BP-ANN, and ELM. The proposed GBM tool can provide a quick first assessment of three-layered soil slope stability.
Secondly, this study develops integrated evolutionary machine learning algorithms (GA-ANN, GA-DT, GA-RF, and GA-XGBoost), trained and evaluated on two different datasets aimed at predicting the stability number of rock slopes. The first dataset contains slope cases subjected to earthquake effect, while the second dataset contains slope cases influenced by water level elevation. Performance comparisons indicate that the GA-XGBoost models achieved the best performance scores and can accurately predict the stability of rock slopes for both datasets. Furthermore, the implementation of SHAP enables the physical and quantitative interpretations of dependencies between the input and output variables at a global and local level, making it possible to quantify the impact of influential slope parameters on the predicted stability number. Thirdly, this work developed two LSTM-based iOS mobile applications to predict slope stability. The first app named Slope_EQ predicts the stability of slopes subjected to earthquake influence. The second app name Slope_WL predicts the stability of slopes influenced by slope water level elevation. Both applications demonstrate impressive speed, accuracy, and convenience in assessing the stability of rock slopes.
Finally, this thesis explores the use of aerial imagery data combined with deep learning techniques for automated building extraction. The study develops an efficient system for building extraction in a dense urban area using aerial imagery data and deep learning. Herein, the widely used Mask R-CNN framework is adopted for building detection and instance segmentation fused with transfer learning and further integrated with PointRend. In order to create a proper dataset for the building image training set, effective data annotation and augmentation strategies were adopted. The implementation of various data augmentation methods allows the creation of a large enough dataset for training. The proposed automated building extraction model can be used to help automatically extract buildings from aerial imagery and further implemented to assess damages caused by landslides triggered by slope failures. Overall, the AI methods and tools implemented in this study offer promising, quick, and cost-effective alternative solutions to complex slope stability problems.

Rapid population growth over the years has prompted the need to build critical infrastructures in hilly and mountainous areas. At the same time, the increment in the magnitude and frequency of extreme weather conditions due to climate change poses challenges to engineers to develop and monitor the safety of slopes. Consequently, researchers have devoted significant effort to slope stability assessment and prediction. However, slope stability assessment remains a complicated task. The task of slope stability assessment is further complicated when engineers need access to information and tools required to perform quick and accurate slope stability assessment. Due to the critical nature of slope stability assessment, this thesis aims to investigate challenges associated with the safety assessment of slopes and develop AI-based techniques and tools that provide quick solutions to challenging slope safety assessment problems.
This study first investigates the stability of three-layered soil slopes using gradient boosting machine (GBM) algorithm. To accomplish this task, factors such as slope angle (?o), friction angle (?′), depth factor (?⁄?), height ratio (?1⁄??), and undrained shear strength ratio (??1⁄??2), are used as inputs to predict the stability number of filled slopes. The results indicate that the slope stability estimation using the proposed GBM tool achieved good accuracy, speed, and convenience compared to other widely used ML methods such as SVM, BP-ANN, and ELM. The proposed GBM tool can provide a quick first assessment of three-layered soil slope stability.
Secondly, this study develops integrated evolutionary machine learning algorithms (GA-ANN, GA-DT, GA-RF, and GA-XGBoost), trained and evaluated on two different datasets aimed at predicting the stability number of rock slopes. The first dataset contains slope cases subjected to earthquake effect, while the second dataset contains slope cases influenced by water level elevation. Performance comparisons indicate that the GA-XGBoost models achieved the best performance scores and can accurately predict the stability of rock slopes for both datasets. Furthermore, the implementation of SHAP enables the physical and quantitative interpretations of dependencies between the input and output variables at a global and local level, making it possible to quantify the impact of influential slope parameters on the predicted stability number. Thirdly, this work developed two LSTM-based iOS mobile applications to predict slope stability. The first app named Slope_EQ predicts the stability of slopes subjected to earthquake influence. The second app name Slope_WL predicts the stability of slopes influenced by slope water level elevation. Both applications demonstrate impressive speed, accuracy, and convenience in assessing the stability of rock slopes.
Finally, this thesis explores the use of aerial imagery data combined with deep learning techniques for automated building extraction. The study develops an efficient system for building extraction in a dense urban area using aerial imagery data and deep learning. Herein, the widely used Mask R-CNN framework is adopted for building detection and instance segmentation fused with transfer learning and further integrated with PointRend. In order to create a proper dataset for the building image training set, effective data annotation and augmentation strategies were adopted. The implementation of various data augmentation methods allows the creation of a large enough dataset for training. The proposed automated building extraction model can be used to help automatically extract buildings from aerial imagery and further implemented to assess damages caused by landslides triggered by slope failures. Overall, the AI methods and tools implemented in this study offer promising, quick, and cost-effective alternative solutions to complex slope stability problems.

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