When every parameter is properly scaled down in accordance with some similarity coefficients, it is possible to study the physical-mechanical properties of rock mass with a scale model. To identify the key mechanisms ...When every parameter is properly scaled down in accordance with some similarity coefficients, it is possible to study the physical-mechanical properties of rock mass with a scale model. To identify the key mechanisms of soft rock in deep buried tunnels, the proper sand, binder and ratio were selected. During the process, the model manufacture technology was introduced and typical tests were done and the results were presented. The physical and meehanieal properties effects caused by each composition were discussed. It is shown that the physical and mechanical properties of chosen ratio material such as uniaxial compressive strength tests, elasticity modulus, tensile strength, internal frictional angle, and Poisson＇s ratio meet with similarity relationship well. The physical and mechanical properties of deep soft rock are simulated successfully.展开更多
A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust c...A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.展开更多
基金Supported by the New Century Excellent Talent Foundation from MOE of China（NCET-09-0844） the National Natural Science Foundation of China （50804060, 50921063）
文摘When every parameter is properly scaled down in accordance with some similarity coefficients, it is possible to study the physical-mechanical properties of rock mass with a scale model. To identify the key mechanisms of soft rock in deep buried tunnels, the proper sand, binder and ratio were selected. During the process, the model manufacture technology was introduced and typical tests were done and the results were presented. The physical and meehanieal properties effects caused by each composition were discussed. It is shown that the physical and mechanical properties of chosen ratio material such as uniaxial compressive strength tests, elasticity modulus, tensile strength, internal frictional angle, and Poisson＇s ratio meet with similarity relationship well. The physical and mechanical properties of deep soft rock are simulated successfully.
文摘A new strategy is presented to solve robust multi-physics multi-objective optimization problem known as improved multi-objective collaborative optimization （IMOCO） and its extension improved multi-objective robust collaborative （IMORCO）. In this work, the proposed IMORCO approach combined the IMOCO method, the worst possible point （WPP） constraint cuts and the Genetic algorithm NSGA-II type as an optimizer in order to solve the robust optimization problem of multi-physics of microstructures with uncertainties. The optimization problem is hierarchically decomposed into two levels： a microstructure level, and a disciplines levels, For validation purposes, two examples were selected： a numerical example, and an engineering example of capacitive micro machined ultrasonic transducers （CMUT） type. The obtained results are compared with those obtained from robust non-distributed and distributed optimization approach, non-distributed multi-objective robust optimization （NDMORO） and multi-objective collaborative robust optimization （McRO）, respectively. Results obtained from the application of the IMOCO approach to an optimization problem of a CMUT cell have reduced the CPU time by 44% ensuring a Pareto front close to the reference non-distributed multi-objective optimization （NDMO） approach （mahalanobis distance, D2M =0.9503 and overall spread, So=0.2309）. In addition, the consideration of robustness in IMORCO approach applied to a CMUT cell of optimization problem under interval uncertainty has reduced the CPU time by 23% keeping a robust Pareto front overlaps with that obtained by the robust NDMORO approach （D2M =10.3869 and So=0.0537）.
