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Article

An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

1
College of Computer, Mathematical and Natural Sciences, University of Maryland, College Park, MD 20742, USA
2
Department of Civil Engineering, University of Colorado, Boulder, CO 80309, USA
3
Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan 98167-45845, Iran
4
Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 159163-4311, Iran
*
Author to whom correspondence should be addressed.
Water 2021, 13(3), 302; https://doi.org/10.3390/w13030302
Received: 28 December 2020 / Revised: 16 January 2021 / Accepted: 23 January 2021 / Published: 26 January 2021
(This article belongs to the Special Issue Soft Computing and Machine Learning in Dam Engineering)
Quantification of structural vibration characteristics is an essential task prior to perform any dynamic health monitoring and system identification. Anatomy of vibration in concrete arch dams (especially tall dams with un-symmetry shape) is very complicated and requires special techniques to solve the eigenvalue problem. The situation becomes even more complicated if the material distribution is assumed to be heterogeneous within the dam body (as opposed to conventional isotropic homogeneous relationship). This paper proposes a hybrid Random Field (RF)–Polynomial Chaos Expansion (PCE) surrogate model for uncertainty quantification and sensitivity assessment of dams. For different vibration modes, the most sensitive spatial locations within dam body are identified using both Sobol’s indices and correlation rank methods. Results of the proposed hybrid model is further validated using the classical random forest regression method. The outcome of this study can improve the results of system identification and dynamic analysis by properly determining the vibration characteristics. View Full-Text
Keywords: dams; Polynomial Chaos Expansion; random fields; random forest; vibration analysis dams; Polynomial Chaos Expansion; random fields; random forest; vibration analysis
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MDPI and ACS Style

Hariri-Ardebili, M.A.; Mahdavi, G.; Abdollahi, A.; Amini, A. An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams. Water 2021, 13, 302. https://doi.org/10.3390/w13030302

AMA Style

Hariri-Ardebili MA, Mahdavi G, Abdollahi A, Amini A. An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams. Water. 2021; 13(3):302. https://doi.org/10.3390/w13030302

Chicago/Turabian Style

Hariri-Ardebili, Mohammad A., Golsa Mahdavi, Azam Abdollahi, and Ali Amini. 2021. "An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams" Water 13, no. 3: 302. https://doi.org/10.3390/w13030302

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