| 基于量子遗传算法与多输出混合核相关向量机的堆石坝材料参数自适应反演研究 |
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| 引用本文： | 马春辉,杨杰,程琳,李婷,李雅琦.基于量子遗传算法与多输出混合核相关向量机的堆石坝材料参数自适应反演研究[J].岩土力学,2019,40(6):2397-2406. |
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| 作者姓名： | 马春辉 杨杰 程琳 李婷 李雅琦 |
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| 作者单位： | 1. 西安理工大学 水利水电学院，陕西 西安 710048；2. 西安理工大学 省部共建西北旱区生态水利国家重点实验室，陕西 西安 710048；
3. 南京水利科学研究院 生态环境研究中心，江苏 南京 210029 |
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| 基金项目： | 国家自然科学基金（No.41301597）；陕西省自然科学基础研究计划重点项目（No.2018JZ5010）；陕西省水利科技计划项目（No.2018SLKJ-5） |
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| 摘 要： | 为进一步提高堆石坝材料参数反演模型的计算精度与适用性，建立了基于量子遗传算法（QGA）与多输出混合核相关向量机（MMRVM）的自适应反演模型。通过引入混合核函数，使所构建的MMRVM能够高精度地模拟材料参数与大坝沉降间的复杂非线性关系，从而代替耗时较长的有限元（FEM）计算。通过利用参数较固化的QGA优化确定MMRVM核参数，使反演模型具有自适应性。以实测沉降数据为依据，充分发挥QGA的全局搜索能力反演筑坝材料本构模型参数。在分析模型所需测点个数与信噪比对计算结果影响的基础上，通过公伯峡堆石坝应用实例证明：QGA-MMRVM可快速、精确地反演堆石坝筑坝材料本构模型参数，模型凭借其自适应性在实际工程中具有良好的应用前景和推广价值。
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| 关 键 词： | 堆石坝 参数反演 多输出混合核相关向量机 量子遗传算法 自适应 |
| 收稿时间： | 2018-03-07 |
Adaptive inversion analysis of material parameters of rock-fill dam based on QGA-MMRVM |
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| MA Chun-hui,YANG Jie,CHENG Lin,LI Ting,LI Ya-qi.Adaptive inversion analysis of material parameters of rock-fill dam based on QGA-MMRVM[J].Rock and Soil Mechanics,2019,40(6):2397-2406. |
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| Authors: | MA Chun-hui YANG Jie CHENG Lin LI Ting LI Ya-qi |
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| Institution: | 1. Institute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, China;
2. State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an, Shaanxi 710048, China;
3. Center for Eco-Environmental, Nanjing Hydraulic Research Institute, Nanjing, Jiangsu 210029, China |
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| Abstract: | In order to improve the accuracy and applicability of inversion analysis model of material parameters for rockfill dam, an adaptive model based on quantum genetic algorithm (QGA) and multi-output mixed kernel relevance vector machine (MMRVM) is established. By introducing mixed kernel function, the MMRVM can accurately simulate the nonlinear relationship between the material parameters and the settlement of rockfill dam. Therefore, the finite element method (FEM) can be replaced by the MMRVM to reduce time consumption. Then, the kernel parameters of the MMRVM is optimized by the QGA, thus the QGA-MMRVM is adaptable to different inversion analysis problems. The parameters of constitutive model of dam materials can be inverted by fully utilizing QGA's global searching ability. Finally, the influences of the signal-noise ratio and the number of measured points on the calculation result are analyzed. The examples of Gongboxia dam show that the parameters of constitutive model of material can quickly and accurately calculated by the QGA-MMRVM. With its adaptability, the QGA-MMRVM has good application prospect and popularization value in practical engineering. |
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| Keywords: | rock-fill dam parameter inversion analysis multi-output mixed kernel relevance vector machine quantum genetic algorithm adaptivity |
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