Development of Fuel Cell Hybrid Electric Vehicle Simulator and HCU

Xuan, Dong Ji; Ning, Qian; Li, Zhen Zhe; Cheng, Tai Hong; Shen, Yonghang

doi:10.4028/www.scientific.net/amm.26-28.1110

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…According to the measured data in document [6], the initial proposal value for the settlement trough width parameter caused by underground excavation in parts of China was summarized and proposed.…”

Section: The Establishment Of Settlement Trough Width Parameter Evalu...mentioning

confidence: 99%

Research and Development of System of Shield Tunnelling to Adjacent Buildings

Ding

Wei

et al. 2011

AMR

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Considering different types of building structure and foundation, the system refers to acceptable ground settlement and acceptable soil loss ratio was established by establishing the control of building’s bending and deformation. The system which adopted Delphi dynamic data binding technology and used Access as database was developed by Delphi7.0 visual tools. The system which named "Analysis system about shield tunnel construction to adjacent buildings" is developed to judge the extent of damage of buildings influenced by adjacent shield tunnel construction.

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Section: The Establishment Of Settlement Trough Width Parameter Evalu...mentioning

confidence: 99%

Research and Development of System of Shield Tunnelling to Adjacent Buildings

Ding

Wei

et al. 2011

AMR

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“…The empirical formula method [1][2][3][4][5] is easy to use and often used to predict the maximum ground subsidence caused by tunnel engineering. However, the variability of the parameters is large, and the prediction accuracy of the ground settlement based on the empirical method cannot be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Maximum Surface Settlements of Bai∼Hua Tunnel Section based on Machine Learning

Jin-miao

Shen

Chen

2022

J. Phys.: Conf. Ser.

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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 predict the maximum settlement. Random forest (RF) and artificial neural network (ANN) are often used to predict settlement. However, applying the extreme gradient boosting algorithm (XGB) in predicting the settlement is rarely seen. This article compares these three machines learning algorithms, using tunnel geometric parameters, shield construction parameters, and geological parameters as input parameters to predict the maximum ground settlement caused during tunnel construction. Compared with linear regression, the result shows these three machine learning algorithms can achieve higher quality results, and the stability of the RF and the XGB model is better than the neural network model. The XGB method can obtain the best results.

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“…Shield tunneling can cause ground movements and damage to adjacent buildings and overlying facilities. At present, the methods of analysis and prediction of surface settlement induced by model investigations [1][2], shield tunnel construction include empirical methods [3][4][5] ， quasi-analytical solutions [6][7][8], numerical simulation [9,10] and etc., of which numerical simulation method is extensively used by the domestic and foreign scholars.…”

Section: Introductionmentioning

confidence: 99%

Finite Element Analysis of the Surface Settlement Induced by the Shield Tunnel Construction

Ling

Xiang²,

2014

AMM

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Three-dimensional non-linearity finite element model of shield tunnel was established on basis of the Z1 line of Tianjin subway. And then it was applied to simulate construction process of shield tunnel. Surface settlement of the tunnel during the construction was obtained. The settlement data of transverse and longitudinal direction from numerical simulation were fitted through a polynomial expression. Then a contrastive analysis of curves from numerical simulation and matching formulae were made. The result shows that it is feasible to utilize the empirical formulas like Pecks to predict surface settlement in Tianjin caused by shield construction.

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Development of Fuel Cell Hybrid Electric Vehicle Simulator and HCU

Cited by 3 publications

References 8 publications

Research and Development of System of Shield Tunnelling to Adjacent Buildings

Research and Development of System of Shield Tunnelling to Adjacent Buildings

Prediction of Maximum Surface Settlements of Bai∼Hua Tunnel Section based on Machine Learning

Finite Element Analysis of the Surface Settlement Induced by the Shield Tunnel Construction

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