Browsing by Author "Vijay Dangwal"

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Boundary element coupled structural analysis of Lesser Himalayan railway tunnels: A case study of the Shivpuri–Byasi section, Rishikesh–Karnaprayag BG rail link, Uttarakhand, India
(Springer, 2024) Abhishek Srivastav; Vikas Yadav; Ashutosh Kainthola; Vishnu H R Pandey; Vijay Dangwal; T.N. Singh
A tunnel provides reliable, low-maintenance, and all-weather connectivity in hilly terrains. Rishikesh–Karnaprayag Broad Gauge project is a 125 km long rail link, that includes 35 bridges and 17 tunnels, to connect the tough route of Uttarakhand Chardham sites. This tunnel route passes through the Lower Himalayas of Uttarakhand, India. The present research emphasizes the structural discontinuities’ influence on tunnel stability at different chainages (18570 to 32000) with a 13.4 km long span, having variable overburden and rock mass conditions. The factor of safety is determined using kinematic analysis and numerical simulation based on the boundary element method. The boundary element method examines the excavation problems and captures the interaction of the tunnel structure and surrounding rock mass. Different rock mass classification schemes are also utilized to evaluate the rock mass conditions. Schemes mainly include rock mass rating (RMR), Q-system and Ö-NORM B2203 (NATM class), suggesting that the rock mass quality ranges from poor to fair. The factor of safety for critically unstable wedges without support varies between 0.49 and 0.98, and after applying shotcrete and rock bolting, FOS varies between 0.63 and 3.23. In the present study, the overburden varies between 33 and 590 m. The influence of applied computational support (shotcrete and rock bolting) has been studied with an average value of factor of safety at each 100 m interval. The study outcomes may be significant in supporting estimation during similar rock mass conditions. © Indian Academy of Sciences 2024.
Efficiency of Classification Algorithms for Prediction of Rock Mass ÖNORM B Class in Himalayan Tunnelling
(Springer, 2025) Ashutosh Kainthola; Md Alquamar Azad; Abhishek Srivastav; Vikas Yadav; T. N. Singh; Vijay Dangwal
For underground excavation, accurate assessment of rock mass behaviour is imperative for a robust design of support system. For the ambitious, broad gauge rail link project in Uttarakhand Himalaya, India, apart from Q, and RMR, ÖNORM B system is being used for tunnel support recommendation. However, ÖNORM B system is qualitative in nature, and thus measurement of surrounding rock mass deformation is measured to designate the rock class. This approach is expensive and not often feasible. Therefore, present study attempts, perhaps for the first time, to quantify the prediction of ÖNORM B class of rock mass, using five easy to assess parameters. Two parameters from RMR, two from Q-system, and one common in both were used as inputs. Nine standard machine learning classifiers have been trained on 873 rows of data, and validated on 218 data points. Accuracy, precision, and ROC were evaluated for each classification algorithm. Results are quite promising with highest accuracy and precision in predicting the ÖNORM B class, delivered by Extra Tree, Random Forest, and Decision Tree classifiers. However, the authors recommend Extra Tree classifier since they are the least prone to overfitting and can be generalized. © The Author(s), under exclusive licence to Indian Geotechnical Society 2025.