Ground Movements Prediction in Shield-Driven Tunnels using Gene Expression Programming

Introduction: The increase in population and traffic in metropolitan areas has led to the development of underground transportation spaces. Therefore, the estimation of the surface settlement caused by the construction of underground structures should be accurately considered. Several methods have been developed to predict tunneling-induced surface settlement. Among these methods, artificial intelligence-based methods have received much attention in recent years. This paper is aimed to develop a model based on Gene Expression Programming (GEP) algorithm to predict surface settlement induced by mechanized tunneling. Methods: For this purpose, Tehran Metro Line 6 was simulated numerically to investigate the effects of different parameters on the surface settlement, and 85 datasets were prepared from numerical simulations. Subsequently, several GEP models were implemented using the obtained datasets from numerical simulations and finally, a model with 30 chromosomes and 3 genes was selected as the optimum model. Results: A comparison was made between obtained maximum surface settlements by the proposed GEP model and numerical simulation. The results demonstrated that the proposed model could predict surface settlement induced by mechanized tunneling with a high degree of accuracy. Conclusion: Finally, a mathematical equation was derived from the proposed GEP model, which can be easily used for surface settlement prediction.

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An Intelligent Model to Predict Breaking Strength of Rotor Spun Yarns Using Gene Expression Programming

Journal of Engineered Fibers and Fabrics ◽

10.1177/155892501200700202 ◽

2012 ◽

Vol 7 (2) ◽

pp. 155892501200700 ◽

Cited By ~ 2

Author(s):

Abdolrasool Moghassem ◽

Alireza Fallahpour ◽

Mohsen Shanbeh

Keyword(s):

Gene Expression ◽

Breaking Strength ◽

Gene Expression Programming ◽

Ann Model ◽

Spun Yarn ◽

Yarn Properties ◽

Spun Yarns ◽

Artificial Neural Network Ann ◽

High Degree ◽

Prediction Approach

Exploring relationships between characteristics of a yarn and influencing factors is momentous subject to optimize the selection of the variables. Different modelling methodologies have been used to predict spun yarn properties. Developing a prediction approach with higher degree of precision is a subject that has received attention by the researchers. In the last decade, Artificial Neural Network (ANN) has been developed successfully for textile nonlinear processes. In spite of the precision, ANN is a black box and does not indicate inter-relationship between input and output parameters. Hence, Gene Expression Programming (GEP) is presented here as an intelligent algorithm to predict breaking strength of rotor spun yarns based on draw frame parameters as one of the most important stages in spinning line. Forty eight samples were produced and different models were evaluated. Prediction performance of the GEP was compared with that of ANN using Mean Square Error (MSE) and correlation coefficient (R2-Value) parameters on test data. The results showed a better capability of the GEP model in comparison to the ANN model. The R2-value and MSE were 97% and 0.071 respectively which means desirable predictive power of GEP algorithm. Finally, an equation was extracted to predict breaking strength of the yarns with a high degree of accuracy using GEP algorithm.

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Prediction of the number of consumed disc cutters of tunnel boring machine using intelligent methods

Mining of Mineral Deposits ◽

10.33271/mining15.04.068 ◽

2021 ◽

Vol 15 (4) ◽

pp. 68-74

Author(s):

Alireza Afradi ◽

Arash Ebrahimabadi ◽

Tahereh Hallajian

Keyword(s):

Gene Expression ◽

Support Vector Machine ◽

Linear Regression ◽

Nonlinear Regression ◽

Gene Expression Programming ◽

Regression Method ◽

Support Vector ◽

Tunnel Boring ◽

Mechanized Tunneling ◽

Disc Cutters

Purpose. Disc cutters are the main cutting tools for the Tunnel Boring Machines (TBMs). Prediction of the number of consumed disc cutters of TBMs is one of the most significant factors in the tunneling projects. Choosing the right model for predicting the number of consumed disc cutters in mechanized tunneling projects has been the most important mechanized tunneling topics in recent years. Methods. In this research, the prediction of the number of consumed disc cutters considering machine and ground conditions such as Power (KW), Revolutions per minute (RPM) (Cycle/Min), Thrust per Cutter (KN), Geological Strength Index (GSI) in the Sabzkooh water conveyance tunnel has been conducted by multiple linear regression analysis and multiple nonlinear regression, Gene Expression Programming (GEP) method and Support Vector Machine (SVM) approaches. Findings. Results showed that the number of consumed disc cutters for linear regression method is R2 = 0.95 and RMSE = 0.83, nonlinear regression method is – R2 = 0.95 and RMSE = 0.84, Gene Expression Programming (GEP) method is – R2 = 0.94 and RMSE = 0.95, Support Vector Machine (SVM) method is – R2 = 0.98 and RMSE = 0.45. Originality. During the analyses, in order to evaluate the accuracy and efficiency of predictive models, the coefficient of determination (R2) and root mean square error (RMSE) have been used. Practical implications. Results demonstrated that all four methods are effective and have high accuracy but the method of support vector machine has a special superiority over other methods.

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A Gene Expression Programming Model for Predicting Tunnel Convergence

Applied Sciences ◽

10.3390/app9214650 ◽

2019 ◽

Vol 9 (21) ◽

pp. 4650 ◽

Cited By ~ 31

Author(s):

Mohsen Hajihassani ◽

Shahrum Shah Abdullah ◽

Panagiotis G. Asteris ◽

Danial Jahed Armaghani

Keyword(s):

Gene Expression ◽

Programming Model ◽

Gene Expression Programming ◽

Initial Stresses ◽

Unit Weight ◽

Tunneling Method ◽

Tunnel Convergence ◽

Optimum Model ◽

And Performance ◽

Experimental Findings

Underground spaces have become increasingly important in recent decades in metropolises. In this regard, the demand for the use of underground spaces and, consequently, the excavation of these spaces has increased significantly. Excavation of an underground space is accompanied by risks and many uncertainties. Tunnel convergence, as the tendency for reduction of the excavated area due to change in the initial stresses, is frequently observed, in order to monitor the safety of construction and to evaluate the design and performance of the tunnel. This paper presents a model/equation obtained by a gene expression programming (GEP) algorithm, aiming to predict convergence of tunnels excavated in accordance to the New Austrian Tunneling Method (NATM). To obtain this goal, a database was prepared based on experimental datasets, consisting of six input and one output parameter. Namely, tunnel depth, cohesion, frictional angle, unit weight, Poisson’s ratio, and elasticity modulus were considered as model inputs, while the cumulative convergence was utilized as the model’s output. Configurations of the GEP model were determined through the trial-error technique and finally an optimum model is developed and presented. In addition, an equation has been extracted from the proposed GEP model. The comparison of the GEP-derived results with the experimental findings, which are in very good agreement, demonstrates the ability of GEP modeling to estimate the tunnel convergence in a reliable, robust, and practical manner.

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