A case study on the utilization of tunnel face mapping data for a back analysis based on artificial neural network

You, Kwang Ho

doi:10.1007/s12205-014-0329-1

Cited by 8 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, BP neural networks have become the most widely used artificial neural network model, and its MATLAB toolbox has a wide range of engineering applications [41][42][43]. In this paper, a BP neural network is used to learn the nonlinear relationship between indicators and the stability level of loess deposits.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Multi-Index Classification Model for Loess Deposits Based on Rough Set and BP Neural Network

Zhang¹,

Xue²,

Qiu³

et al. 2018

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Classifying loess deposits is an important process for selecting support form and construction methods for tunnels. An accurate evaluation of loess deposits is a necessary prerequisite to control deformation, save cost, and improve construction efficiency. In this paper, a neural network model with an evaluation system consisting of physical and mechanical indices of loess is proposed to realize intelligent classification of loess deposits for tunneling. The influence of water content, natural density, cohesion, internal friction angle, elastic modulus, and Poisson ratio on stability level of loess is analyzed by rough set theory based on statistical data of borehole samples. Results show that the affect of natural density is negligible. Then other indicators such as input nodes and the BP neural network model are formed after learning statistical samples and being applied to the project for testing. Finally, the output of the model is consistent with the actual. This study provides a multi-index model for evaluating loess deposits surrounding tunnels and provides a reference for future research.

show abstract

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Multi-Index Classification Model for Loess Deposits Based on Rough Set and BP Neural Network

Zhang¹,

Xue²,

Qiu³

et al. 2018

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

show abstract

“…Machine learning is also an important method in the intelligent classification of surrounding rock. The physical and mechanical parameters of rock mass have been applied to the RMR value prediction using a neural network [31,32]. These parameters include the bulk density, compressive strength, ingress of water, rock quality designation (RQD), average distance between leak, and seismic velocity.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms

Zhao

Wang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

The quality evaluation of the surrounding rock is the cornerstone of tunnel design and construction. Previous studies have confirmed the existence of a relationship between drilling parameters and the quality of surrounding rock. The application of drilling parameters to the intelligent classification of surrounding rock has the natural advantages of automatic information collection, real-time analysis, and no extra work. In this work, we attempt to establish the intelligent surrounding rock classification model and software system driven by drilling parameters. We collected 912 samples containing four drilling parameters (penetration velocity, hammer pressure, rotation pressure, and feed pressure) and three surrounding rock (grade-III, grade-IV, and grade-V). Based on the python machine learning toolkit (Scikit-learn), 10 types of supervised machine learning algorithms were used to train the intelligent surrounding rock classification model with the model parameter selection technology of grid search cross validation. The results show that the average accuracy is 0.82, which proves the feasibility of this method. Finally, the tunnel surrounding rock intelligent classification system was established based on three models with better comprehensive performance among them. The classification accuracy of the system was 0.87 in the tunnel test section, which indicates that the system has good generalization performance and practical value.

show abstract

“…In recent years, intelligent methods including Gaussian process (GP) and differential evolution (DE) algorithm have been widely used in geotechnical parameter inversion. Guan et al [18] proposed a creep parameter inversion algorithm based on BN and GA. e author in [19] used the face mapping data to predict ground properties in a tunnel back analysis by an artificial neural network. Li et al [20] used the Gaussian process model to predict tunnel water inrush.…”

Section: Introductionmentioning

confidence: 99%

Creep Parameter Inversion and Long‐Term Stability Analysis of Tunnel Based on GP‐DE Intelligent Algorithm

Zhang

Jiang

Guo

et al. 2021

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

Aiming at the creep problem of Banshi Tunnel in Jilin province, the creep laws of rock are analyzed by the creep test, and the Cvsic model describing the creep characteristics of the tunnel is established. To obtain the creep parameters accurately, considering the advantages of Gauss process and differential evolution algorithm, coupling the two methods, a Gauss process-differential evolution intelligent inversion method is proposed. According to on-site monitoring data, the the creep parameters of the tunnel are accurately inverted. On this basis, the stability analysis of the tunnel and the selection of a reasonable construction plan are carried out. The research results show that to ensure the stability of the tunnel, the construction scheme of initial lining + pipe shed + advanced grouting anchor rod should be adopted. The research results have guiding significance for the long-term stability evaluation of the tunnel.

show abstract

A case study on the utilization of tunnel face mapping data for a back analysis based on artificial neural network

Cited by 8 publications

References 3 publications

Multi-Index Classification Model for Loess Deposits Based on Rough Set and BP Neural Network

Multi-Index Classification Model for Loess Deposits Based on Rough Set and BP Neural Network

Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms

Creep Parameter Inversion and Long‐Term Stability Analysis of Tunnel Based on GP‐DE Intelligent Algorithm

Contact Info

Product

Resources

About