2022
DOI: 10.1088/1742-6596/2185/1/012042
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Prediction of Maximum Surface Settlements of Bai∼Hua Tunnel Section based on Machine Learning

Abstract: Research on the settlement caused by subway tunnel construction has always been an essential issue in tunnel research. However, due to the complexity of soil characteristics and construction parameters, using empirical formulas or numerical simulations to predict the maximum ground settlement is challenging to balance ease of use and accuracy. In recent years, with the rapid development of machine learning theory and computer science technology, machine learning algorithms are increasingly being used to predic… Show more

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Cited by 3 publications
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“…Moreover, the machine learning settlement prediction model is trained with fast calculation and accurate results, which meets the requirements of actual engineering to obtain surface settlement in a timely and accurate manner. Since the 1980s, some scholars have tried to use methods related to machine learning to solve practical tunnel engineering problems [20][21][22][23][24][25][26][27][28] . In recent years, artificial neural networks, support vector machines and random forest algorithms have become the main machine learning algorithms used to predict surface subsidence caused by shield tunnels.…”
mentioning
confidence: 99%
“…Moreover, the machine learning settlement prediction model is trained with fast calculation and accurate results, which meets the requirements of actual engineering to obtain surface settlement in a timely and accurate manner. Since the 1980s, some scholars have tried to use methods related to machine learning to solve practical tunnel engineering problems [20][21][22][23][24][25][26][27][28] . In recent years, artificial neural networks, support vector machines and random forest algorithms have become the main machine learning algorithms used to predict surface subsidence caused by shield tunnels.…”
mentioning
confidence: 99%