Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies

The shield machine attitude (SMA) is the most important parameter in the process of tunnel construction. To prevent the shield machine from deviating from the design axis (DTA) of the tunnel, it is of great significance to accurately predict the dynamic characteristics of SMA. We establish eight SMA...

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Autores principales:	Haohan Xiao, Bo Xing, Yujie Wang, Peng Yu, Lipeng Liu, Ruilang Cao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	EPB shield machine data preprocessing intelligent algorithms SMA dynamic prediction Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/60ce350bab0a444aa9a727b0bd155864
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spelling	oai:doaj.org-article:60ce350bab0a444aa9a727b0bd1558642021-11-11T15:17:58ZPrediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies10.3390/app1121102642076-3417https://doaj.org/article/60ce350bab0a444aa9a727b0bd1558642021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10264https://doaj.org/toc/2076-3417The shield machine attitude (SMA) is the most important parameter in the process of tunnel construction. To prevent the shield machine from deviating from the design axis (DTA) of the tunnel, it is of great significance to accurately predict the dynamic characteristics of SMA. We establish eight SMA prediction models based on the data of five earth pressure balance (EPB) shield machines. The algorithms adopted in the models are four machine learning (ML) algorithms (KNN, SVR, RF, AdaBoost) and four deep learning (DL) algorithms (BPNN, CNN, LSTM, GRU). This paper obtains the hyperparameters of the models by utilizing grid search and K-fold cross-validation techniques and uses EVS and RMSE to verify and evaluate the prediction performances of the models. The prediction results reveal that the two best algorithms are the LSTM and GRU with EVS > 0.98 and RMSE < 1.5. Then, integrating ML algorithms and DL algorithms, we design a warning predictor for SMA. Through the historical 5-cycle data, the predictor can give a warning in advance if the SMA deviates significantly from DTA. This study indicates that AI technologies have considerable promise in the field of SMA dynamic prediction.Haohan XiaoBo XingYujie WangPeng YuLipeng LiuRuilang CaoMDPI AGarticleEPB shield machinedata preprocessingintelligent algorithmsSMAdynamic predictionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10264, p 10264 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EPB shield machine data preprocessing intelligent algorithms SMA dynamic prediction Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	EPB shield machine data preprocessing intelligent algorithms SMA dynamic prediction Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Haohan Xiao Bo Xing Yujie Wang Peng Yu Lipeng Liu Ruilang Cao Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies
description	The shield machine attitude (SMA) is the most important parameter in the process of tunnel construction. To prevent the shield machine from deviating from the design axis (DTA) of the tunnel, it is of great significance to accurately predict the dynamic characteristics of SMA. We establish eight SMA prediction models based on the data of five earth pressure balance (EPB) shield machines. The algorithms adopted in the models are four machine learning (ML) algorithms (KNN, SVR, RF, AdaBoost) and four deep learning (DL) algorithms (BPNN, CNN, LSTM, GRU). This paper obtains the hyperparameters of the models by utilizing grid search and K-fold cross-validation techniques and uses EVS and RMSE to verify and evaluate the prediction performances of the models. The prediction results reveal that the two best algorithms are the LSTM and GRU with EVS > 0.98 and RMSE < 1.5. Then, integrating ML algorithms and DL algorithms, we design a warning predictor for SMA. Through the historical 5-cycle data, the predictor can give a warning in advance if the SMA deviates significantly from DTA. This study indicates that AI technologies have considerable promise in the field of SMA dynamic prediction.
format	article
author	Haohan Xiao Bo Xing Yujie Wang Peng Yu Lipeng Liu Ruilang Cao
author_facet	Haohan Xiao Bo Xing Yujie Wang Peng Yu Lipeng Liu Ruilang Cao
author_sort	Haohan Xiao
title	Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies
title_short	Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies
title_full	Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies
title_fullStr	Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies
title_full_unstemmed	Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies
title_sort	prediction of shield machine attitude based on various artificial intelligence technologies
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/60ce350bab0a444aa9a727b0bd155864
work_keys_str_mv	AT haohanxiao predictionofshieldmachineattitudebasedonvariousartificialintelligencetechnologies AT boxing predictionofshieldmachineattitudebasedonvariousartificialintelligencetechnologies AT yujiewang predictionofshieldmachineattitudebasedonvariousartificialintelligencetechnologies AT pengyu predictionofshieldmachineattitudebasedonvariousartificialintelligencetechnologies AT lipengliu predictionofshieldmachineattitudebasedonvariousartificialintelligencetechnologies AT ruilangcao predictionofshieldmachineattitudebasedonvariousartificialintelligencetechnologies
_version_	1718435561651306496

Prediction of Shield Machine Attitude Based on Various Artificial Intelligence Technologies

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