Abstract
This paper presents a comparison study between methods of deep learning as a new category of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to calculate the factor of safety (FS) using four limit equilibrium methods: Bishop’s simplified method, the Fellenius method, Janbu’s simplified method, and Janbu’s corrected method. The code was verified against Slide2 in RocScience. Subsequently, the average FS values were used to approximate the “true” FS of the slopes for labeling the images for deep learning. Using this code, a comprehensive dataset of slope images with wide ranges of geometries and soil properties was created. The average FS values were used to label the images for implementing two deep learning models: a multiclass classification and a regression model. After training, the deep learning models were used to predict the FS of an independent set of slope images. Finally, the performance of the models was compared to that of the conventional methods. This study found that deep learning methods can reach accuracies as high as 99.71% while improving computational efficiency by more than 18 times compared with conventional methods.
Highlights
Slope stability analysis is critical to the prevention and hazard mitigation of landslides
5m.1et.hoTdr. aInintrianngsfer learning, we used a convolutional neural networks (CNNs) that was pretrained on a general large dataset and repurposed the learned features for training on a target dataset
Seventy-five test iterations were performed at an IimntearvgaleoNf 50e0titderaatitoanss.eOtthwer iatdhopt1ed00hy0pecrplaarsamseetserswinaclsuduedtailmizomedentaums otfh0e.9, sa ource osfonfratpthhsehiocstlaopsfs5irfi0ec0a0tt,riaoannidmnaoewddeeilgwmhtitdohedtchaeeysleofpa0as.r0a0tm0h5e.etFerisgs.utIraner6thtdiisenmfiggounprsetor, athitenesdtthooewtfnrawoinauirndrgtprterrnoadceiosnsf ing h and significantly improved the performance of the models
Summary
Slope stability analysis is critical to the prevention and hazard mitigation of landslides. In the present day, urbanization and population growth have been necessitating the build-up of terraces and corridors to make room for buildings and infrastructures, leading to more slope stability considerations in the built environment [1,2]. This raises the demand for the understanding, analysis, and prevention of landslides. Computers enabled LEMs to consider the internal forces, pore pressure, and multiple layers of soils. LEMs are statically indeterminate problems, and the use of these methods requires assumptions of the internal forces that compromise their accuracy [7]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call