Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located...Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.展开更多
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical propertie...Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.展开更多
基金supported by the "Light of West China" Program of Chinese Academy of Sciences (Grant No.Y6R2250250)the National Basic Research Program of China (973 Program, Grant No.2013CB733201)+2 种基金the One-Hundred Talents Program of Chinese Academy of Sciences (LijunSu)the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant No.QYZDB-SSW-DQC010)the Youth Fund of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (Grant No. Y6K2110110)
文摘Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金supported by the National Natural Science Foundation of China(Grant Nos.51379161&51509190)China Postdoctoral Science Foundation(Grant No.2015M572195)the Fundamental Research Funds for the Central Universities
文摘Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.
