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

Abstract

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.

Safety-Risk Assessment for TBM Construction of Hydraulic Tunnel Based on Fuzzy Evidence Reasoning

“…Choosing the right model for operating and maintaining the equipment not only determines the tunnel construction schedule, but also directly affects the safety of personnel at the construction site. Equipment risks include failure of the pipe assembly, failure of the jacking system, failure of the grouting system, mismatch of the operation model selection, consumption of disc cutters, and failure of the shield tail seal [1,7,13].…”

“…The total deviation of first-level indicators is shown in Table 6. Step 3: calculate the objective weight of TBM tunnel construction risk based on maximum deviation according to Formula (13). The first-level index weight λ 1i is shown in Table 7.…”

“…Gu et al [12] established a two-level fuzzy comprehensive evaluation model to Processes 2022, 10, 2597 2 of 17 evaluate the construction risk level of the right line of the West Qinling Railway Tunnel. Afradi et al [13,14] conducted a study on TBM construction models. Results show that linear regression, nonlinear regression, and the gene expression programming method can effectively predict the number of TBM consumed disc cutters considering machine and ground conditions, while the support vector machine method has superiority; the ant colony optimization, bee colony optimization, and the particle swarm optimization can realistically predict the TBM penetration rate, while the particle swarm optimization yields more precise and realistic findings.…”

Multi-step real-time prediction of hard-rock TBM penetration rate combining temporal convolutional network and squeeze-and-excitation block

“…This process is not only time-consuming but also demands significant attention and expertise from operators, particularly when constructing long and large tunnels, where maintaining optimal operation becomes increasingly complex. Furthermore, the complexity of TBM excavation poses significant risks, including working in environments without natural light, the possibility of falling tunnel walls, exposure to various air pollutants, and the risks of explosion and fire, which can lead to irreparable accidents if not properly managed 8 , 9 . The high uncertainty in ground conditions and limited space further exacerbate these risks.…”