In the past decade, advances in Machine Learning (ML) techniques have resulted in developing sophisticated models that are capable of modeling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them
Reviewer #2:Comments pertaining to Geological characteristics and Geotechnical study Comment 1: Slope dimensions are not visible in field photos. Authors may add panoramic view illustrating dimensions of slope.Reply: As per recommendation, the slope dimensions were added to the field photograph (Fig. 1).We regret to inform you that we could not add a panoramic view of the slope since no panoramic photograph was taken for the slope during field investigation and now it is not possible to get the panoramic view photograph. Comment 2: Tension cracks were observed during field surveys? If yes, they were considered in simulation studies/ in pre-assumption of slip surfaces? Reply: We would like to inform that out of the four sites (Nainital, Haridwar, Dehradun, and Solan) from where field investigations were carried out, and samples were collected for laboratory tests (Ray et al. 2019), only one site (Haridwar) had few tension cracks. Rest of the sites were free from any tension cracks. Thus, during simulation, no tension cracks were assumed. Comment 3: Kindly provide boundary conditions and model environment/initial conditions. Reply: The following boundary conditions and model environment/initial conditions were used during simulation (Note: This is only to inform the reviewer, and the details are given in Ray et al. (2019). If the reviewer recommends, it can be added in the revised manuscript)"Fixed boundary conditions (zero displacements) have been used at the base of the model, and along the lateral sides, however, the slope face and the rock-soil interface were kept free for showing strain and displacement. Two-dimensional six-noded triangular plane strain elements have been used to discretise across the selected slope profile. In this study, a uniform meshing option has been used for the soil and weathered rock layer and graded meshing for the bedrock layer. The average element size of around 0.5 m,1 m, and 5 m is kept for the residual soil layer, the weathered rock layer, and the bedrock layer, respectively. It was assumed that no tension cracks are present on the crown of the slope.All the models evaluated under dry condition."
Ground improvement will be critically important in the present and future geotechnical practice for designing the structures in weak soil. This paper presents a review of the recent development in ground improvement techniques, especially chemical stabilisers. Various available chemical stabilisers are identified and compared with other available methods. Though the use of chemicals provides an excellent alternative to the traditional methods, they still lack proper understanding regarding their use, handling, application, and long-term effect on the environment. Various chemical stabilisers and their applicability conditions are summarised in the present paper. Insight of biochemical, electrochemical, inorganic, and organic stabilisers is presented with future scope of these methods along with the potential areas where a lot of efforts is needed to industrialise these methods are also discussed briefly. A need for developing a more environmentally friendly and safe method was felt while reviewing these methods. Lack of a large amount of data is a major concern for lesser use of these methods industrially. A lot of laboratory and field experiments should be conducted in different conditions to ensure safe results from chemical stabilisers.
A sudden downward movement of the geomaterial, either composed of soil, rock, or a mixture of both, along the mountain slopes due to various natural or anthropogenic factors is known as a landslide. The Himalayan Mountain slopes are either made up of residual soil or rocks. Residual soil is formed from weathering of the bedrock and mainly occurs in gentle-to-moderate slope inclinations. In contrast, steep slopes are mostly devoid of soil cover and are primarily rocky. A stability prediction system that can analyse the slope under both the condition of the soil or rock surface is missing. In this study, artificial neural network technology has been utilised to predict the stability of jointed rock and residual soil slope of the Himalayan region. The database for the artificial neural network was obtained from numerical simulation of several residual soils and rock slope models. Nonlinear equations have been formulated by coding the artificial neural network algorithm. An android application has also been developed to predict the stability of residual soil and rock slope instantly. It was observed that the developed android app provides promising results in predicting the factor of safety and stability state of the slopes.
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