Pore water pressure

Geology

Lateral earth pressure

Mathematics

Multivariate adaptive regression splines

Petrology

Structural engineering

Statistics

World Wide Web

Shield

Engineering

Payment

Multivariate statistics

Geotechnical engineering

Settlement (finance)

Computer science

Linear regression

Bayesian multivariate linear regression

Underground Space

Estimating surface settlement induced by excavation construction is an indispensable task in tunneling, particularly for earth pressure balance (EPB) shield machines. In this study, predictive models for assessing surface settlement caused by EPB tunneling were established based on extreme gradient boosting (XGBoost), artificial neural network, support vector machine, and multivariate adaptive regression spline. Datasets from three tunnel construction projects in Singapore were used, with main input parameters of cover depth, advance rate, earth pressure, mean standard penetration test (SPT) value above crown level, mean tunnel SPT value, mean moisture content, mean soil elastic modulus, and grout pressure. The performances of these soft computing models were evaluated by comparing predicted deformation with measured values. Results demonstrate the acceptable accuracy of the model in predicting ground settlement, while XGBoost demonstrates a slightly higher accuracy. In addition, the ensemble method of XGBoost is more computationally efficient and can be used as a reliable alternative in solving multivariate nonlinear geo-engineering problems.

Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling