Browsing by Author "T. N. Singh"

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3D Stochastic Simulation of Rockfall Mechanism and Mitigation in the Batseri Zone
(Springer Science and Business Media Deutschland GmbH, 2025) Vishnu Himanshu Ratnam Pandey; Ashutosh Kainthola; Vikas Yadav; Jagadish Kundu; Paolo Mazzanti; Ramesh P. Singh; T. N. Singh
On 25 July 2021, a deadly rockfall at Batseri (Himachal Pradesh), India, killed 9 tourists and completely destroyed a crucial Bailey Bridge. The present study primarily focuses into the geomorphological and engineering geological attributes of the Batseri Rockfall dynamics. Extreme weathering phenomenon, adversely orientated joints, and abnormally high precipitation in the valley might have evoked the doomed incident. The work combines field study, structural analysis, and 3D stochastic assessment to ascertain the triggers, and trajectory of the rockfall. Runout distance, bounce height, kinetic energy, and velocity of the falling blocks with varying geometry and sizes have also been calculated. The results have been used to test the efficiency of rockfall barriers with different configurations and combinations to safeguard the affected strategically important road. These results can aid in mitigating the rockfall damage, effectively and economically. The simulation of potential blocks destroying the Bailey Bridge forms an important section of this research, and will assist in identification of suitable locations for new bridge instalment at Baspa River. Moreover, this work is perhaps the first of its kind to undertake the rigid body stochastic analysis for understanding the rockfall mechanism in such a large scale. The results discussed in the paper will be of use to understand similar events across the Himalayan terrains and develop policy for hazard mitigation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 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.
Empirical and numerical hill slope health evaluation at Malling Nala, NH-505, Himachal Pradesh, India
(Springer, 2025) Md Alquamar Azad; Ashutosh Kainthola; Yewuhalashet Fissha; Gaurav Kumar Kushwaha; Vikas Yadav; T. N. Singh
Slope failures and rock mass movements are continuous geomorphic processes, particularly in a dynamically charged terrane like the Himalaya. Thus, failures emanating from weak geology, hydrogeology and anthropogenic disturbances are aplenty. Present research evaluates slope stability in the vicinity of Malling Nala, along NH-505 in Himachal Pradesh, India. For the two most vulnerable sections in the study area, geo-mechanical and structural attributes have initially been ascertained. Field surveys and laboratory tests identified weak and weathered mica schist and gneissic rocks in the study area. Kinematic analysis, Rock Mass Rating (RMR), Geological Strength Index (GSI), Slope Mass Rating (SMR), modified Global Slope Performance Index (mGSPI) led to determination of possible failure mechanism and rock mass behaviour. Finite element analysis provided a comprehensive understanding of slope behaviour under various conditions, highlighting significant shear strain and displacement in both sections. As noticed from the field and classification schemes, planar and localized bench failures were established. Slope section L-1 was found to collapse under saturated water condition, manifesting the influence of snow melt. The findings indicate that both natural and human factors are causing instability. Effective risk management and mitigation strategies are essential to maintain the stability and reliability of this critical frontier region. © The Author(s) 2025.
Field data driven rockfall hazard and risk assessment along Sangla-Chitkul road, Himachal Pradesh, India
(Springer Science and Business Media Deutschland GmbH, 2025) Vishnu Himanshu Ratnam Pandey; Gaurav Kumar Kushwaha; Ashutosh Kainthola; Vikas Yadav; T. N. Singh; Abhi S. Krishna
This study addresses the issue of rockfall hazard in Baspa Valley, Himachal Pradesh, India. Proven empirical rockfall hazard rating systems were interpolated in the GIS-environment to analyse the contingent risk to the population. Initially, field data were ascertained from precarious rockfall locations along the 23 km long Sangla-Chitkul road. This was followed by a kinematic analysis to identify potential structural failure modes, revealing that each studied slope section could undergo one or a combination of failures. Rockfall Hazard Rating System (RHRS) parameters were formulated and interpolated for the entire area using the inverse-distance weighting (IDW) technique. Resulting hazard map, overlaid with population data, classified rockfall risk into five categories: very high (14.7%), high (28.8%), moderate (23.2%), low (18.6%), and very low (14.7%). A similar assessment using the Missouri Rockfall Hazard Rating System (MORFH-RS) showed the following rockfall risk distribution: very high (11%), high (21%), moderate (21%), low (21%), and very low (11%). Additionally, MORFH-RS indicated that around 90% of the area lies in high-risk and high-consequence zone, with high consequences for all locations. Two-dimensional stochastic simulations were conducted to understand rockfall dynamics at all studied locations, revealing that most sites exhibit kinetic energy exceeding 500 kJ, with five locations surpassing 1000 kJ. This indicates a high potential for significant damage across a large area of the valley, based on runout-distance data. Additionally, these findings were correlated with geotechnical characterization using Global Slope Performance Index (GSPI), identifying the potential for four distinct failure types in the valley. © The Author(s) 2025.
Machine learning models for prediction of blasting induced ground vibrations in basaltic rocks: a case study from Navi Mumbai Airport quarry, India
(Springer Science and Business Media Deutschland GmbH, 2025) Ramesh Murlidhar Bhatawdekar; T. N. Singh; Prakash Y. Dhekne; Ashutosh Kainthola; Edy Tonnizam Mohamad; Sanjay Purohit; Vishnu Himanshu Ratnam Pandey; Vikas Yadav
Ground vibrations are a deleterious consequence of blasting. Peak particle velocity is used to assess the strength of the ground vibrations, and it usually estimated before the blasting operations. Traditional ground vibration predictors simplify the estimation by considering the maximum charge per delay and the distance from the blast site. Thus, the combined effect of the blast design parameters on the peak particle velocity is disregarded. Artificial intelligence algorithms can be used for the prediction of peak particle velocity considering the collective effects of the blast design parameters. The present research evaluates the different regression algorithms for prediction of the peak particle velocity of blast-induced ground vibrations. Feature used are the ratios of: spacing and burden, stemming and burden, blast hole depth and burden, burden and diameter, charge factor, maximum charge per delay, and the distance of the monitoring station and the target variable is peak particle velocity. A dataset consisting of 418 blasts carried out at the site in Western India. Thereafter, the supervised viz. Extra Tree, Extra Gradient Boost, Random Forest, Light Gradient Boosting Machine, Decision Tree, Support Vector have been adopted to predict the target variable. The results indicate that all the above methods have predicted the peak particle velocity with reasonable accuracy. It further shows that the Extra Tree regressor exhibits highest R2 score of 0.88, for the test set, with a RMSE of.434. The study concludes that tree-based ensemble techniques can be used for reliable prediction when the data set is limited. © Springer Nature Switzerland AG 2025.
Slime Mould Algorithm Enhanced Gradient Boosting Regressor for Prediction of Slope Stability
(Springer Science and Business Media Deutschland GmbH, 2025) Ashutosh Kainthola; Vishnu Himanshu Ratnam Pandey; T. N. Singh
Present research details the comparison of predictive efficiency of linear regression, gradient boosting regression, and slime mould algorithm optimised gradient boosting regression. These algorithms have been used to predict the factor of safety of cut slopes in lower Tons valley, Uttarakhand, India. Initially, 103 soil slopes were examined for their stability factor in finite difference code. Four parameters like cohesion, angle of internal friction, slope height, and slope angle were selected to develop the stability (factor of safety) prediction models. Based on the statistical accuracy indices like R2 and mean absolute error, the hyper-parameter optimized model based on slime mould algorithm (i.e., SMA-GBR) had the best prediction capability. And, the R2 for SMA-GBR model is 0.92 and mean absolute error is 0.063. The next better performer is linear regression model with value of 0.84 and 0.094 for R2 and mean absolute error respectively. And, the gradient boosting algorithm model has the least predictive capacity as compared to other two prediction models developed in the present work. However, its capability is significantly increased as its hyper-parameter are fine-tuned through slime mould algorithms (SMA). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Towards sustainable sand resource management - Best governance practices and alternatives to river sand in India
(Springer, 2025) Ramesh Murlidhar Bhatawdekar; Edy Tonnizam Mohamad; T. N. Singh; Dato Hock Soon Chengong; Ashutosh Kainthola; Manoj Khandelwal; Feten Chihi; Vynotdni Rathinasamy; Clement Kweku Arthur; Anand Ravi Deshpande; Sanjay Nigam; Sangki Kwon; Rahul V. Ralegaonkar; Md Alquamar Azad
[No abstract available]