QGA-MMRVM-based self-adaptive inversion method of material parameters of rockfill dam

Abstract

The invention discloses a QGA-MMRVM-based self-adaptive inversion method of material parameters of a rockfill dam. The method comprises the following steps: firstly, introducing a mixed kernel function on the basis of a multi-output relevance vector machine (M-RVM) to build MMRVM with higher precision; then using parameter-fixed QGA to optimize MMRVM kernel parameters to realize self-adaptive calculation of an MMRVM model; and then exerting global-search capability of the QGA, and using actually measured settlement data of the dam as a goal to obtain dam construction material constitutive-model parameters by inversion to realize self-adaptive calculation of an inversion model. The method solves the problems of low computational accuracy, low computational speed, insufficient small-sample pertinence, poor inversion-model adaptability and the like existing in the prior art; and can be widely applied to other engineering and inversion projects.

Description

technical field [0001] The invention belongs to the technical field of inversion of rockfill dam material parameters in water conservancy projects, and in particular relates to an adaptive inversion method of rockfill dam material parameters based on QGA-MMRVM. Background technique [0002] The inversion methods of constitutive model parameters of rockfill dams can be divided into direct algorithm and intelligent algorithm. The direct algorithm converts the parameter inversion problem into an optimization problem, but it has the defect that it is difficult to converge to the global optimal solution. In recent years, intelligent algorithms have developed rapidly, and have good applications in the inversion research of rockfill dams, including neural network methods, genetic algorithms, particle swarm algorithms, and support vector machines (SVM). [0003] Yu et al used the evolutionary algorithm to optimize the artificial neural network algorithm to invert the parameters of ...

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Problems solved by technology

[0005] The above-mentioned intelligent algorithm has achieved great results in the inversion of rockfill dam material parameters, but there are still some problems: ① the optimization algorithm is easy to fall into local minimum; Restrict the application and popularization of the inversion model; ③ In view of the limited monitoring data and small number of samples in the early stage of dam construction, the above-mentioned models are still not fully trained, and there are large errors in the inversion results

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