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Article

ASA-PSO-Optimized Elman Neural Network Model for Predicting Mechanical Properties of Coarse-Grained Soils

1
College of Water Conservancy & Architeclural Engineering, Shihezi University, Shihezi 832003, China
2
Bureau of Xinjiang Tarim River Basin Management, Korla 841000, China
3
China Institute of Water Resources and Hydropower Research, Beijing 100048, China
*
Authors to whom correspondence should be addressed.
Processes 2025, 13(8), 2447; https://doi.org/10.3390/pr13082447 (registering DOI)
Submission received: 7 July 2025 / Revised: 23 July 2025 / Accepted: 29 July 2025 / Published: 1 August 2025
(This article belongs to the Section Particle Processes)

Abstract

Coarse-grained soils serve as essential fill materials in earth–rock dam engineering, where their mechanical properties critically influence dam deformation and stability, directly impacting project safety. Artificial intelligence (AI) techniques are emerging as powerful tools for predicting the mechanical properties of coarse-grained soils. However, AI-based prediction models for these properties face persistent challenges, particularly in parameter tuning—a process requiring substantial computational resources, extensive time, and specialized expertise. To address these limitations, this study proposes a novel prediction model that integrates Adaptive Simulated Annealing (ASA) with an improved Particle Swarm Optimization (PSO) algorithm to optimize the Elman Neural Network (ENN). The methodology encompasses three key aspects: First, the standard PSO algorithm is enhanced by dynamically adjusting its inertial weight and learning factors. The ASA algorithm is then employed to optimize the Adaptive PSO (APSO), effectively mitigating premature convergence and local optima entrapment during training, thereby ensuring convergence to the global optimum. Second, the refined PSO algorithm optimizes the ENN, overcoming its inherent limitations of slow convergence and susceptibility to local minima. Finally, validation through real-world engineering case studies demonstrates that the ASA-PSO-optimized ENN model achieves high accuracy in predicting the mechanical properties of coarse-grained soils. This model provides reliable constitutive parameters for stress–strain analysis in earth–rock dam engineering applications.
Keywords: enhanced particle swarm optimization; dynamic recurrent neural network; coarse-grained soils; mechanical property prediction enhanced particle swarm optimization; dynamic recurrent neural network; coarse-grained soils; mechanical property prediction

Share and Cite

MDPI and ACS Style

Wang, H.; Li, J.; Zhao, Y.; Liu, B. ASA-PSO-Optimized Elman Neural Network Model for Predicting Mechanical Properties of Coarse-Grained Soils. Processes 2025, 13, 2447. https://doi.org/10.3390/pr13082447

AMA Style

Wang H, Li J, Zhao Y, Liu B. ASA-PSO-Optimized Elman Neural Network Model for Predicting Mechanical Properties of Coarse-Grained Soils. Processes. 2025; 13(8):2447. https://doi.org/10.3390/pr13082447

Chicago/Turabian Style

Wang, Haijuan, Jiang Li, Yufei Zhao, and Biao Liu. 2025. "ASA-PSO-Optimized Elman Neural Network Model for Predicting Mechanical Properties of Coarse-Grained Soils" Processes 13, no. 8: 2447. https://doi.org/10.3390/pr13082447

APA Style

Wang, H., Li, J., Zhao, Y., & Liu, B. (2025). ASA-PSO-Optimized Elman Neural Network Model for Predicting Mechanical Properties of Coarse-Grained Soils. Processes, 13(8), 2447. https://doi.org/10.3390/pr13082447

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