Back-analysis of soil parameters of the Malutang II concrete face rockfill dam using parallel mutation particle swarm optimization

2015 ◽  
Vol 65 ◽  
pp. 87-96 ◽  
Author(s):  
Yufeng Jia ◽  
Shichun Chi
2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Yue Chen ◽  
Chongshi Gu ◽  
Chenfei Shao ◽  
Xiangnan Qin

The deformation behavior of rockfill is significant to the normal operation of concrete face rockfill dam. Considering both the nonlinear mechanical behavior and long-term rheological deformation, the E-ν model and modified Burgers model are coupled to describe the deformation behavior of the rockfill materials. The coupled E-ν and Burgers model contains numerous parameters with complex relationship, and an efficient and accurate inversion analysis is in demand. The sensitivity of the parameters in the coupled E-ν and modified Burgers is analyzed using the modified Morris method initially. Then, a new approach of parameter back analysis is proposed by combining back-propagation neutral network (BPNN) and Cuckoo Search (CS) algorithm. The numerical example shows that parameters K, Rf, and φ0 as well as G are more sensitive to the deformation of the rockfill body. The inversion analysis for these four parameters and η2, E2, and A as well as B in modified Burgers model is performed by the CS-BPNN algorithm. The numerical results demonstrate that the parameters obtained with the proposed method are reasonable and its feasibility is validated.


2011 ◽  
Vol 474-476 ◽  
pp. 1373-1376 ◽  
Author(s):  
An Nan Jiang ◽  
Peng Li

The geological body has uncertainty and complexity characters, therefore the predesign scheme for construction has blindness. Field measurement and back analysis can help identify rock mass parameters in actual engineering field. The paper combines an intelligent optimization arithmetic-particle swarm optimization (PSO) and 3-D fast Lagrange numerical method to construct a parameters back analysis method for underground engineering. The PSO has global robust optimization property and overcomes the conventional optimization method problem of being limited in local optimization. The FISH language is used to develop the PSO arithmetic and is embedded in the business software- FLAC-3D. The method is used in Dalian Metro construction and it proves that this method has not only fast convergence speed but also high prediction. It offers a new idea to geotechnical parameters identification.


2012 ◽  
Vol 182-183 ◽  
pp. 1647-1653
Author(s):  
Wei Hua Fang

In order to obtain geotechnical engineering material mechanical parameters correctly by using back analysis and overcome shortcoming of ordinary Particle Swarm Optimization, Improved Particle Swarm Optimization (IPSO) algorithm is developed on the aspects such as Stretching Particle, Metropolis Algorithm and adaptive weight updating .at the same time, the algorithm is compared with Catastrophe Particle Swarm Optimization Algorithm (CPSO) and Quantum Particle Swarm Optimization Algorithm(QPSO). Also result of back analysis was compared with that of Ultrasonic Testing and that of mixed-model of dam monitoring. The analysis shows that IPSO has better performance than that of PSO and CPSO, and considerable performance with QPSO.


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