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Zoned elasticity modulus inversion analysis method of a high arch dam based on unconstrained Lagrange support vector regression (support vector regression arch dam)

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Abstract

An inverse modeling process is proposed to estimate the actual elasticity modulus of a high arch dam based on unconstrained Lagrange support vector regression (ULSVR) and monitoring data. The proposed ULSVR eliminates the inequality and equality constraints to improve the iteration speed, especially for large repeated calculations, and the process takes advantage of the culture genetic algorithm (CGA) by importing the belief space, which utilizes social knowledge to guide the generations to accelerate the evolution speed. Then, CGA is used to optimize the parameters of ULSVR and seek the optimal elasticity modulus combination. The inverse modeling procedure is successfully applied to optimize the zoned elasticity modulus of the Jinping high arch dam and foundation in its initial impound period. The inversion analysis shows that the actual modulus is larger than the experimental results, showing that the actual situation is safer than the design condition.

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Acknowledgments

This research has been partially supported by the National Natural Science Foundation of China (Grant Nos. 51139001, 41323001, 51479054, 51279052); the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20130094110010); the Fundamental Research Funds for the Central Universities (2014B36914); the Central University Basic Research Project (Grant No. 2015B20714); and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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  1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 210098, China

    Chungao Liu, Chongshi Gu & Bo Chen

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  1. Chungao Liu
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  2. Chongshi Gu
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  3. Bo Chen
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Correspondence to Chungao Liu.

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Liu, C., Gu, C. & Chen, B. Zoned elasticity modulus inversion analysis method of a high arch dam based on unconstrained Lagrange support vector regression (support vector regression arch dam). Engineering with Computers 33, 443–456 (2017). https://doi.org/10.1007/s00366-016-0483-9

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  • DOI: https://doi.org/10.1007/s00366-016-0483-9

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