Intelligent inversion model of macro-micro parameters for rockfill using discrete-continuous coupling method--颗粒学报PARTICUOLOGY

Ma, C., Gao, M., Hou, Y., Cheng, L., Chai, J., & Khofiz, I. (2026). Intelligent inversion model of macro-micro parameters for rockfill using discrete-continuous coupling method. Particuology, 110, 286-299. https://doi.org/10.1016/j.partic.2026.01.025

Intelligent inversion model of macro-micro parameters for rockfill using discrete-continuous coupling method

Chunhui Ma^{a b *}, Mingyuan Gao^{a b}, Yuanyuan Hou^{a b}, Lin Cheng^{a b}, Junrui Chai^{a b}, Ibrokhimov Khofiz^c

a School of Water Resources and Hydropower, Xi'an University of Technology, Xi'an, 710048, China
b State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Xi'an University of Technology, Xi'an, 710048, China
c Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent, 100000, Uzbekistan

10.1016/j.partic.2026.01.025

Volume 110, March 2026, Pages 286-299

Received 17 October 2025, Revised 28 December 2025, Accepted 16 January 2026, Available online 30 January 2026, Version of Record 5 February 2026.

E-mail: machunhui@xaut.edu.cn

Highlights

• Hybrid RUN-XGBoost algorithm improves inverse analysis precision and adaptability.

• Proposed a discrete-continuous coupling method for modeling rockfill dam construction.

• RUN-XGBoost model efficiently determines macro-micro parameters of rockfill dams.

• Coupled model reproduces dam deformation behavior consistent with field monitoring.

Abstract

This study addresses the limitations of traditional feedback analysis methods for dam construction materials, which suffer from low accuracy, long computation times, and an inability to capture micromechanical properties. We propose a novel macro-micro parameter joint intelligent feedback analysis model for rockfill materials, driven by dam deformation monitoring data, that efficiently and accurately determines the macro and micro parameters of these materials. By employing an intelligent inverse analysis model, researchers can derive the macro and micro material parameters of the discrete-continuum coupling model, aiding in the optimization of design standards and guiding dam construction and operation. To enhance this process, we construct an adaptive surrogate model using a Runge-Kutta optimizer (RUN) and an extreme gradient boosting (XGBoost) algorithm. This model captures the complex nonlinear relationship between macro and micro parameters and dam settlement, reducing the need for time-consuming numerical simulations. By leveraging deformation monitoring data from panel rockfill dams, the RUN-XGBoost algorithm effectively addresses the inverse analysis problem. The results demonstrate that this intelligent inverse analysis model can rapidly and accurately determine rockfill dam parameters, improving the precision of macro-micro parameter calculations and enabling a comprehensive investigation of the mechanical evolution of rockfill materials, with implications for structural safety analysis.

Graphical abstract

Keywords

Face rockfill dam; Discrete-continuous numerical simulation; Macro-micro parameter joint inverse analysis; Intelligent inverse analysis model

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