Prediction of earth pressure balance for EPB-TBM using machine learning algorithms

• Published in: International journal of geo-engineering Vol. 14; no. 1; pp. 21 - 31
• Main Authors: Samadi, Hanan, Hassanpour, Jafar, Rostami, Jamal
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01-12-2023; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Artificial intelligence techniques; Boring machines; Civil Engineering; Control stability; Earth; Earth and Environmental Science; Earth pressure; Earth pressure balance; Earth Sciences; Empirical analysis; Excavation; Foundations; Fuzzy logic; Geoengineering; Geotechnical Engineering & Applied Earth Sciences; Hydraulics; Machine learning; Mathematical models; Model accuracy; Multilayer perceptrons; Neural networks; Original Research; Parameters; Screw conveyors; Soft ground tunneling; Statistical analysis; Statistical methods; Subways; Support vector machines; Thrust; Urban areas; Artificial intelligence techniques; Earth pressure balance; Machine learning; Soft ground tunneling
• ISSN: 2198-2783; 2092-9196; 2198-2783
• DOI: 10.1186/s40703-023-00198-7

Face stability control of excavation with earth pressure balance machine (EPB) approach is the best available method to reduce the ground deformation and settlement of surface structures in a tunneling project in urban areas. In the present paper, several models have proposed through a statistical method, including feed-forward stepwise regression (FSR) and machine learning techniques such as support vector machine (SVM), Takagi–Sugeno fuzzy model (TS), and multilayer perceptron neural network (ANN-MLP), to provide a predictive strategy for EPB machine during the tunnel excavation. For this purpose, a monitoring dataset of machine performance parameters including advance speed, screw conveyor speed, screw conveyor torque, thrust force, and cutterhead rotation speed from Tehran Metro Line 6 Southern Extension Sector (TML6-SE) has been compiled. Then, the relation between the performance parameters and target values were investigated to analyze the available inputs and offer a new equation using the FSR. Moreover, evaluation metrics and loss functions were utilized for the evaluation of the developed models’ efficiency. The results proved the significance of the presented methods in this paper that could be used to predict the earth pressure balance operation with high efficiency.
Highlights
A robust database was generated and analyzed through daily operating records, TBM data logger, and statistical analysis.
Novel equation was formulated to predict the earth pressure balance for EPB-TBM by considering various operating parameter of boring machine.
The predictive capabilities of machine learning, neural network, and fuzzy algorithm in estimating earth pressure balance were explored.
A comprehensive evaluation was conducted to compare and contrast the empirical and ML outcomes.
The accuracy of the models was examined using multiple loss functions and evaluation metrics.