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Analysis of the influence of karst cave parameters on surface settlement in TBM tunnelling

Dong Bichang, Yang Tao, Ju Binbin, Qu Zhongying, Yi Chao

Bulletin of the Polish Academy of Sciences. Technical Sciences

2024

Vol. 72, nr 4

art. no. e150110

In order to study the influence of multiple karst cave factors on surface settlement during tunnel boring machine (referred to TBM hereinafter) tunnelling, a three-dimensional numerical model is built by taking a subway project as an example and combining it with MIDAS GTS NX finite element software. Secondly, the influence of the radius, height, angle, vertical net distance and horizontal distance of the karst cave on maximum surface settlement is studied and sorted under the two working conditions of treatment and lack of treatment using the gray correlation analysis method. Additionally, a multi-factor numerical model of the untreated karst cave is established. Finally, based on the preceding research, a multi-factor prediction model for maximum surface settlement is proposed and tested. The results reveal that when the karst cave is not treated, the radius and height of the karst cave have a significant effect on maximum surface settlement. Following cave treatment however, the influence of the cave parameters on maximum settlement of the surface is greatly reduced. The calculating model created in this study offers excellent prediction accuracy and good adaptability.

Prediction of Standard Time of the Sewing Process using a Support Vector Machine with Particle Swarm Optimization

Shao Yibing, Ji Xiaofen, Zheng Menglin, Chen Caiya

Autex Research Journal

2022

Vol. 22, no. 3

290--297

Standard time is a key indicator to measure the production efficiency of the sewing department, and it plays a vital role in the production forecast for the apparel industry. In this article, the grey correlation analysis was adopted to identify seven sources as the main influencing factors for determination of the standard time in the sewing process, which are sewing length, stitch density, bending stiffness, fabric weight, production quantity, drape coefficient, and length of service. A novel forecasting model based on support-vector machine (SVM) with particle swarm optimization (PSO) is then proposed to predict the standard time of the sewing process. On the ground of real data from a clothing company, the proposed forecasting model is verified by evaluating the performance with the squared correlation coefficient (R2) and mean square error (MSE). Using the PSO-SVM method, the R2 and MSE are found to be 0.917 and 0.0211, respectively. In conclusion, the high accuracy of the PSO-SVM method presented in this experiment states that the proposed model is a reliable forecasting tool for determination of standard time and can achieve good predicted results in the sewing process.