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

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.

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.

Environmental Fate and Effects of Foaming Agents Containing Sodium Lauryl Ether Sulphate in Soil Debris from Mechanized Tunneling

A wide use of foaming agents as lubricants is required in mechanized tunneling. Their main component, the anionic surfactant sodium lauryl ether sulphate (SLES), can remain in residual concentrations in soil debris, influencing their potential reuse as by-product. This study aimed at evaluating the environmental fate and effects of a foaming product used for conditioning soils collected from real excavation sites, in the presence/absence of an anti-clogging polymer, both containing SLES. Soil microcosm experiments were set-up and incubated for 28 days. Over time, soils and their water extracts (elutriates) were collected to perform both ecotoxicological tests (Vibrio fischeri, Lepidium sativum, Eisenia foetida, Hetereocypris incongruens, Danio rerio) and SLES analysis. The results showed that, just after conditioning, SLES did not exert any hazardous effect on the organisms tested except for the bacterium V. fischeri, which was the most sensitive to its presence. However, from day seven the toxic effect on the bacterium was never observed thanks to the SLES decrease in the elutriates (<2 mg/L). SLES degraded in soils (half-lives from 9 to 25 days) with higher disappearance rates corresponding to higher values of microbial abundances. This study highlights the importance of site-specific studies for assessing the environmental reuse of spoil materials.