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Horizontal Directional Drilling (HDD) is extensively used in geothechnical engineering. In a variety of conditions it is essential to predict the torque required for performing the reaming operation. Nevertheless, there is presently not a convenient method to accomplish this task. To overcome this problem, in this research, the application of computational intelligence methods for data analysis named Support Vector Regression (SVR) optimized  by  differential evolution algorithm (DE) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate of the required rotational torque to operate horizontal directional drilling is demonstrated. Three ANFIS models were implemented, ANFIS–subtractive clustering method (ANFIS-SCM), ANFIS–grid partitioning (ANFIS-GP) and ANFIS–fuzzy c–means clustering method (ANFIS-FCM). The estimation abilities offered using SVR-DE, ANFIS-FCM, ANFIS-SCM, ANFIS-GP were presented by using field data given in open source literatures. In these models,  the rotational torque (M) is used as the output parameter, while the length of drill string in the borehole (L), axial force on the cutter/bit (P), rotational speed (revolutions per minute) of the bit (N), the radius for the i^th reaming operation (D_i), the mud flow rate (W), the total angular change of the borehole (K_L), and the mud viscosity (V) are the input parameters. To compare the performance of models for rotational torque to operate horizontal directional drilling prediction, the coefficient of correlation (R²) and mean square error (MSE) of the models were calculated, indicating the good performance of the ANFIS-SCM model.

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key factor in the successful application of a tunnel boring machine (TBM) in tunneling is the ability to develop accurate penetration rate estimates for determining project schedule and costs. Thus establishing a relationship between rock properties and TBM penetration rate can be very helpful in estimation of this vital parameter. However, this parameter cannot be simply predicted since there are nonlinear and unknown relationships between rock properties and TBM penetration rate. Relevance vector regression (RVR) is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of RVR in prediction of TBM performance. The model was applied to available data given in open source literatures. In this model, uniaxial compressive strengths of the rock (UCS), the distance between planes of weakness in the rock mass (DPW) and rock quality designation (RQD) were utilized as the input parameters, while the measured TBM penetration rates was the output parameter. The performances of the proposed predictive model was examined according to two performance indices, i.e., coefficient of determination (R²) and mean square error (MSE). The obtained results of this study indicated that the RVR is a reliable method to predict penetration rate with a higher degree of accuracy.

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The evaluation of seismic slope performance during earthquakes is important, because the failure of slope (such as an earth dam, natural slope, or constructed earth embankment) can result in significant financial losses and human. It is important, therefore, to be able to forecast such displacements induced by earthquake. However, the traditional forecasting methods, such as empirical formulae, are inaccurate because most of them do not take into consideration all the relevant factors. In this paper, new intelligence method, namely relevance vector regression (RVR) optimized by dolphin echolocation (DE) and grey wolf optimizer (GWO) algorithms is introduced to forecast the earthquake induced displacements (EID) of slopes. The DE and GWO algorithms is combined with the RVR for determining the optimal value of its user-defined paramee RVR. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination (R²) and mean square error (MSE). The obtained results of this study indicated that the RVR-GWO model is a reliable method to forecast EID with a higher degree of accuracy (MSE= 0.0160 and R²= 0.9955).

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During project planning, the prediction of TBM performance is a key factor for selection of tunneling methods and preparation of project schedules. During the construction, TBM performance need to be evaluated based on the encountered rock mass conditions. In this paper, the model based on a relevance vector regression (RVR) optimized by dolphin echolocation algorithm (DEA) for prediction of specific rock mass boreability index (SRMBI) is proposed. The DEA is combined with the RVR for determining the optimal value of its user-defined parameters. The optimized RVR by DEA was employed to available data given in the open source literature. In this model, rock mass uniaxial compressive strength, brittleness index (B_i), volumetric joint account (J_v), and joint orientation (J_o) were used as the input, while the SRMBI was the output parameter. The performances of the suggested predictive model were tested according to two performance indices, i.e., mean square error and determination coefficient. The results show that the RVR- DEA model can be successfully utilized for estimation of the SRMBI in mechanical tunneling.

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Mechanical excavators are widely utilized in civil/mining engineering projects. There are several types of mechanical excavators, such as an impact hammer, tunnel boring machine (TBM) and roadheader. Among these, roadheaders have some advantages (such as, initial investment cost, elimination of blast vibration, minimal ground disturbances and reduced ventilation requirements). The poor performance estimation of the roadheaders can lead to costly contractual claims. Relevance vector regression (RVR) is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of RVR in prediction of roadheader performance. The estimation abilities offered using RVR was presented by using field data of achieved from tunnels for Istanbul’s sewerage system, Turkey. In this model, Schmidt hammer rebound values and rock quality designation (RQD) were utilized as the input parameters, while net cutting rates was the output parameter. As statistical indices, coefficient of determination (R²) and mean square error (MSE) were used to evaluate the efficiency of the RVR model. According to the obtained results, it was observed that RVR model can effectively be implemented for roadheader performance prediction.

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Predicting the bearing capability (q_rs) of geogrid-reinforced stone columns poses a significant challenge due to variations in soil and rock parameters across different locations. The behavior of soil and rock in one region cannot be generalized to other regions. Therefore, accurately predicting q_rs requires a complex and stable nonlinear equation that accounts for the complexity of rock engineering problems. This paper utilizes the Rock Engineering System (RES) method to address this issue and construct a predictive model.To develop the model, experimental data consisting of 219 data points from various locations were utilized. The input parameters considered in the model included the ratio between geogrid reinforced layers diameter and footing diameter (d/D), the ratio of stone column length to diameter (L/dsc), the q_rs of unreinforced soft clay (qu), the thickness ratio of Geosynthetic Reinforced Stone Column (GRSB) and USB to base diameter (t/D), and the settlement ratio to footing diameter (s/D). Following the implementation of the RES-based method, a comparison was made with other models, namely linear, power, exponential, polynomial, and multiple logarithmic regression methods. Statistical indicators such as root mean square error (RMSE), mean square error (MSE), and coefficient of determination (R2) were employed to assess the accuracy of the models. The results of this study demonstrated that the RES-based method outperforms other regression methods in terms of accuracy and efficiency.