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A novel intelligent displacement prediction model of karst tunnels.
Fu, Hai-Ying; Zhao, Yan-Yan; Ding, Hao-Jiang; Rao, Yun-Kang; Yang, Tao; Zhou, Ming-Zhe.
Afiliación
  • Fu HY; School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
  • Zhao YY; School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China. yanazhao123@163.com.
  • Ding HJ; China Railway Eryuan Engineering Group Co. Ltd, Chengdu, 610031, China.
  • Rao YK; School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
  • Yang T; School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
  • Zhou MZ; School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
Sci Rep ; 12(1): 16983, 2022 Oct 10.
Article en En | MEDLINE | ID: mdl-36216860
Karst is a common engineering environment in the process of tunnel construction, which poses a serious threat to the construction and operation, and the theory on calculating the settlement without the assumption of semi-infinite half-space is lack. Meanwhile, due to the limitation of test conditions or field measurement, the settlement of high-speed railway tunnel in Karst region is difficult to control and predict effectively. In this study, a novel intelligent displacement prediction model, following the machine learning (ML) incorporated with the finite difference method, is developed to evaluate the settlement of the tunnel floor. A back propagation neural network (BPNN) algorithm and a random forest (RF) algorithm are used herein, while the Bayesian regularization is applied to improve the BPNN and the Bayesian optimization is adopted for tuning the hyperparameters of RF. The newly proposed model is employed to predict the settlement of Changqingpo tunnel floor, located in the southeast of Yunnan Guizhou Plateau, China. Numerical simulations have been performed on the Changqingpo tunnel in terms of variety of karst size, and locations. Validations of the numerical simulations have been validated by the field data. A data set of 456 samples based on the numerical results is constructed to evaluate the accuracy of models' predictions. The correlation coefficients of the optimum BPNN and BR model in testing set are 0.987 and 0.925, respectively, indicating that the proposed BPNN model has more great potential to predict the settlement of tunnels located in karst areas. The case study of Changqingpo tunnel in karst region has demonstrated capability of the intelligent displacement prediction model to well predict the settlement of tunnel floor in Karst region.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido