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Chao Ma,Dechun Lu,Chengzhi Qi,Xiuli Du 국제구조공학회 2021 Structural Engineering and Mechanics, An Int'l Jou Vol.78 No.5
Great efforts have been conducted to investigate the seismic performances of the arch and rectangular underground structures, however, the differences between seismic responses of these two types of underground structures, especially the vault radian influencing the seismic responses of arch structures are not clarified. This paper presents a detailed numerical investigation on the seismic responses of arch underground structures with different vault radians, and aims to illustrate the rule that vault radian affects the seismic responses of underground structures. Five arch underground structures are built for nonlinear soil-structure interaction analysis. The internal forces of the structural components of the underground structures only under gravity are discussed detailedly, and an optimum vault radian for perfect load-carrying functionality of arch underground structures is suggested. Then the structures are analyzed under seven scaled ground motions, amounting to a total of 35 dynamic calculations. The numerical results show that the vault radian can have beneficial effects on the seismic response of the arch structure, compared to the rectangular underground structures, causing the central columns to suffer smaller axial force and horizontal deformation. The conclusions provide some directive suggestions for the seismic design of the arch underground structures.
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Fanchao Kong,Tao Tian,Dechun Lu,Bing Xu,Weipeng Lin,Xiuli Du 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.11
Four hybrid intelligent methods are developed to predict the maximum ground surface settlement (Smax) induced by shallow underground excavation method (SUEM). Particle swarm optimization (PSO) algorithm with k-fold cross validation is used to determine the optimal hyperparameters or random parameters in the four machine learning (ML) methods, namely that, back-propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR) and random forest (RF). 100 field engineering samples are collected from published papers. In each data sample, the effect of stratum mechanical conditions, tunnel geometric parameters and construction parameters on Smax is considered. Correlation laws among parameters are investigated through Pearson correlation coefficient, data distribution histogram and correlation confidence ellipse. The performance of four PSO-based ML methods is comprehensively compared by fitness function, time cost and prediction accuracy in the training and test processes. PSO-RF outperforms PSO-SVR, PSO-ELM and PSO-BPNN in the prediction accuracy for Smax owing to larger R2, smaller MAE and RMSE. Calculation time that the optimal hyperparameters are determined is the fastest for PSO-RF, and PSO-ELM has the smallest fitness function. The prediction performance of PSO-RF method for construction parameters is also discussed. This work can provide theoretical guidance for design and construction of SUEM.
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