Rock slope along motorways in the Higher Himalayan terrains are prone to various types of failure.In order to effectively mitigate these failures,a thorough assessment of rock mass behavior is entailed.The present res...Rock slope along motorways in the Higher Himalayan terrains are prone to various types of failure.In order to effectively mitigate these failures,a thorough assessment of rock mass behavior is entailed.The present research employs and compares widely practiced geo-mechanical classification schemes viz.,RQD,RMR,SMR,Q-slope,and GSI.A 23 km road cut section,along Sangla to Chitkul route,in Higher Himalayan region(India)has been taken up for this work.Total of 18 locations were selected,and their slope and rockmass properties were examined.Afterwards,the most influencing parameters in RMR,SMR,and Q-Slope were evaluated through a machine learning algorithm,i.e.,Random Forest.For RMRbasic,about 83%of rock-slopes were designated in good condition and rest were of Fair quality.Evaluation of slope mass rating along all 18-locations highlighted eight-sites as partially unstable,six-sites as partially stable.Remaining four locations varied between,Very Bad to Bad slope-conditions,necessitating the installation of mechanical supports and redesign of slopes.For SMR classification,feature importance analysis revealed the predominance of F3 variable,RQD and intact rock strength.Q-Slope approach was incorporated to identify the most stable steepest angle of the examined locations.For Q-Slope rating,Jn and RQD were found to have the most influence in classification of the slopes.Three zones on the basis of GSI-scores have been identified in the study area,i.e.,A(6595),B(4555),and C(2535).This study highlights the application of multiple geomechanical classification schemes,demonstrating how each approach can complement the others.展开更多
基金Anusandhan National Research Foundation(ANRF)(previously,Science and Engineering Research Board-SERB),India for the grant CRG/2022/002509.
文摘Rock slope along motorways in the Higher Himalayan terrains are prone to various types of failure.In order to effectively mitigate these failures,a thorough assessment of rock mass behavior is entailed.The present research employs and compares widely practiced geo-mechanical classification schemes viz.,RQD,RMR,SMR,Q-slope,and GSI.A 23 km road cut section,along Sangla to Chitkul route,in Higher Himalayan region(India)has been taken up for this work.Total of 18 locations were selected,and their slope and rockmass properties were examined.Afterwards,the most influencing parameters in RMR,SMR,and Q-Slope were evaluated through a machine learning algorithm,i.e.,Random Forest.For RMRbasic,about 83%of rock-slopes were designated in good condition and rest were of Fair quality.Evaluation of slope mass rating along all 18-locations highlighted eight-sites as partially unstable,six-sites as partially stable.Remaining four locations varied between,Very Bad to Bad slope-conditions,necessitating the installation of mechanical supports and redesign of slopes.For SMR classification,feature importance analysis revealed the predominance of F3 variable,RQD and intact rock strength.Q-Slope approach was incorporated to identify the most stable steepest angle of the examined locations.For Q-Slope rating,Jn and RQD were found to have the most influence in classification of the slopes.Three zones on the basis of GSI-scores have been identified in the study area,i.e.,A(6595),B(4555),and C(2535).This study highlights the application of multiple geomechanical classification schemes,demonstrating how each approach can complement the others.
