A comparative analysis of artificial neural network predictive and multiple linear regression models for ground settlement during tunnel construction

• Autorzy: Zou Baoping , Chibawe Musa , Hu Bo , Deng Yansheng

Identyfikatory

DOI

10.24425/ace.2023.145281

• Języki publikacji: EN

Abstrakty

EN

Ground settlement during and after tunnelling using TBM results in varying dynamic and static load action on the geo-stratum. It is an undesirable effect of tunnel construction causing damage to the surface and subsurface infrastructure, safety risk, and increased construction cost and quality issues. Ground settlement can be influenced by several factors, like method of tunnelling, tunnel geometry, location of tunnelling machine, machine operational parameters, depth & its changes, and mileage of recording point from starting point. In this study, a description and evaluation of the performance of the artifcial neural network (ANN) was undertaken and a comparison with multiple linear regression (MLR) was carried out on ground settlement prediction. The performance of these models was evaluated using the coefficient of determination R2, root mean square error (RMSE) and mean absolute percentage error (MAPE). For ANN model, the R2, RMSE and MAPE were calculated as 0.9295, 4.2563 and 3.3372, respectively, while for MLR, the R2, RMSE and MAPE, were calculated as 0.5053, 11.2708, 6.3963 respectively. For ground settlement prediction, both ANN and MLR methods were able to predict significantly accurate results. It was further noted that the ANN performance was higher than that of the MLR.

Słowa kluczowe

EN

tunnel; construction; ground settlement; multiple linear regression; artificial neural network

PL

budowa; tunel; osiadanie gruntu; regresja liniowa wielokrotna; sieć neuronowa sztuczna

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2023
• Tom: Vol. 69, nr 2
• Strony: 503--515
• Opis fizyczny: Bibliogr. 36 poz., il., tab.

Twórcy

autor

Zou Baoping

• School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou, China

• https://orcid.org/0000-0003-3729-2425

autor

Chibawe Musa

• School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou, China

• https://orcid.org/0000-0002-9451-6459

autor

Hu Bo

• School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou, China

• https://orcid.org/0000-0002-5119-869X

autor

Deng Yansheng

• School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou, China

• https://orcid.org/0000-0002-4329-1827

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