A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Analysis of factors affecting the deformation of soft rock tunnels by data envelopment analysis and a risk assessment model
Highlights Fourteen factors were collected to establish a deformation risk assessment system. DEA was used to analyze the efficiency of the factors affecting the deformation. Water content was the most efficient factor in the deformation after DEA. The deformation risk assessment model was provided using a normal cloud model. The proposed model was verified by five typical soft rock tunnels.
Abstract Deformation of the surrounding rock is an anticipated geological hazard when driving tunnels through soft rock. If deformation is not restricted, it can lead to serious engineering safety hazards. Therefore, studying the factors that affect the deformation of soft rock tunnels is highly significant. This study selected twelve main influencing factors involving subjective and objective factors and four actual indexes to establish a deformation risk assessment system for a soft rock tunnel. Data envelopment analysis (DEA) was innovatively utilized to analyze the efficiency of the twelve factors which affect the deformation of soft rock tunnels. The analytical results showed that the water content of the soft rock was the most efficient factor, followed by the tunnel depth, uniaxial compressive strength, excavation method, strength-stress ratio and groundwater seepage condition. A risk assessment model for the deformation of a soft rock tunnel was established using a normal cloud model and verified by the deformation occurred in typical soft rock tunnel cases. Risk assessment results confirmed that the proposed model was consistent with actual deformation risk degree. This model can guidie significance for the assessment of soft rock tunnel engineering.
Analysis of factors affecting the deformation of soft rock tunnels by data envelopment analysis and a risk assessment model
Highlights Fourteen factors were collected to establish a deformation risk assessment system. DEA was used to analyze the efficiency of the factors affecting the deformation. Water content was the most efficient factor in the deformation after DEA. The deformation risk assessment model was provided using a normal cloud model. The proposed model was verified by five typical soft rock tunnels.
Abstract Deformation of the surrounding rock is an anticipated geological hazard when driving tunnels through soft rock. If deformation is not restricted, it can lead to serious engineering safety hazards. Therefore, studying the factors that affect the deformation of soft rock tunnels is highly significant. This study selected twelve main influencing factors involving subjective and objective factors and four actual indexes to establish a deformation risk assessment system for a soft rock tunnel. Data envelopment analysis (DEA) was innovatively utilized to analyze the efficiency of the twelve factors which affect the deformation of soft rock tunnels. The analytical results showed that the water content of the soft rock was the most efficient factor, followed by the tunnel depth, uniaxial compressive strength, excavation method, strength-stress ratio and groundwater seepage condition. A risk assessment model for the deformation of a soft rock tunnel was established using a normal cloud model and verified by the deformation occurred in typical soft rock tunnel cases. Risk assessment results confirmed that the proposed model was consistent with actual deformation risk degree. This model can guidie significance for the assessment of soft rock tunnel engineering.
Analysis of factors affecting the deformation of soft rock tunnels by data envelopment analysis and a risk assessment model
Bai, Chenghao (author) / Xue, Yiguo (author) / Qiu, Daohong (author) / Su, Maoxin (author) / Ma, Xinmin (author) / Liu, Haiting (author)
2021-07-12
Article (Journal)
Electronic Resource
English
Evaluating Efficiency of Rock Blasting Using Data-Envelopment Analysis
| Online Contents | 1996