A Gene Expression Programming Model for Predicting Tunnel Convergence

Abstract

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.

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

“…In recent years, a widespread development in the use of information technology in civil engineering has paved the way for many promising applications, especially the use of machine learning (ML) approaches to solve practical engineering problems [12][13][14][15][16][17][18][19][20][21]. Moreover, different ML techniques have been used, for instance, decision tree [22], hybrid artificial intelligence approaches [23][24][25], artificial neural network (ANN) [26][27][28][29][30][31], adaptive neuro-fuzzy inference system (ANFIS) [32,33], and support vector machine (SVM) [34] in solving many real-world problems, including the prediction of behavior of piles.…”

A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

“…The present study used five performance indices to assess the performance of the models developed. These included coefficient of determination (R 2 ), mean absolute error (MAE), root-mean-square error (RMSE), variance accounted for (VAF), and a20-index [32,[40][41][42]44,[94][95][96][97]. These indices were widely used in previous studies for the performance assessment of ML models.…”

“…ANFIS-adaptive neuro-fuzzy inference system; ANN-artificial neural network; FIS-fuzzy inference system; SVM-support vector machine; PSO-particle swarm optimization; ICA-imperialism competitive algorithm; CART-classification and regression trees; GEP-gene expression programming. [31] ANN, FIS 2 162 Fisne et al [69] FIS 2 33 Li et al [72] SVM 2 32 Mohamadnejad et al [71] SVM, ANN 2 37 Ghasemi et al [73] FIS 6 120 Monjezi et al [74] ANN 3 20 Jahed Armaghani et al [4] PSO-ANN 9 44 Hajihassani et al [29] ICA-ANN 7 95 100 Hajihassani et al [77] PSO-ANN 8 88 Jahed Armaghani et al [12] ANFIS 2 109 Hasanipanah et al [78] CART 2 86 Jahed Armaghani et al [79] ICA 2 73 Faradonbeh et al [80] GEP 6 102 Shahnazar et al [81] PSO-ANFIS 2 81 Ghoraba et al [82] ANN, ANFIS 2 115 Despite the vast use of soft computing and ML techniques to predict PPV, a very limited number of studies are available that investigated the use of decision trees to predict PPV [68,73]. To this end, this study develops three decision tree-based models to find the best modeling approach.…”

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

“…Moreover, mechanical simulations (e.g., the finite element method [17]) or laboratory experiments can only be applied in limited cases. Since laboratory experiments are usually costly and time-consuming, predicting such behaviors remains challenging and requires a robust numerical model for quicker and better results, such as machine learning (ML) approaches [10,18].In the last two decades, artificial intelligence-based machine learning approaches have been widely used in civil engineering applications [19][20][21][22][23][24][25][26][27][28][29][30][31]. As an example of structural engineering, Kiani et al [32] applied ML techniques, including support vector machines and neural networks, to derive seismic fragility curves.…”

“…In the last two decades, artificial intelligence-based machine learning approaches have been widely used in civil engineering applications [19][20][21][22][23][24][25][26][27][28][29][30][31]. As an example of structural engineering, Kiani et al [32] applied ML techniques, including support vector machines and neural networks, to derive seismic fragility curves.…”