LIU, Xiaohui ;DONG, Siyu ;XUE, Kaidong ;WANG, Penghui ;REN, Yongyi .
Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method.
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 72, n.1-2, p. 29-39, november 2025.
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/optimization-of-simulation-parameters-for-wet-concrete-particles-based-on-response-surface-methodology-and-pso-bp-ga-method/>. Date accessed: 06 apr. 2026.
doi:http://dx.doi.org/10.5545/sv-jme.2025.1440.
Liu, X., Dong, S., Xue, K., Wang, P., & Ren, Y.
(2026).
Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method.
Strojniški vestnik - Journal of Mechanical Engineering, 72(1-2), 29-39.
doi:http://dx.doi.org/10.5545/sv-jme.2025.1440
@article{sv-jmesv-jme.2025.1440,
author = {Xiaohui Liu and Siyu Dong and Kaidong Xue and Penghui Wang and Yongyi Ren},
title = {Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {72},
number = {1-2},
year = {2026},
keywords = {wet concrete particles; particle simulation parameter optimization; response surface analysis; PSO-BP-GA; },
abstract = {To address the challenges of low calibration efficiency and limited accuracy in the discrete element modeling of wet concrete within high-dimensional parameter spaces, this study developed a parameter calibration scheme that integrates experimental design and intelligent algorithms. It achieved efficient and high-precision inverse function optimization for determing contact parameters, thereby providing a robust foundation for related engineering simulations. Specifically, the repose angle of wet concrete was determined to be 32.07° based on the heap experiment. Through the Plackett-Burman (PB) experiment and steepest ascent experiment, the three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were identified. These parameters are static friction (X1), the coefficient of rolling friction (X2), and surface energy (X3). Subsequently, using the Box-Behnken (BB) test, the optimal 17 sets of combined data for these three significant factors were determined. To establish the objective function between the repose angle of wet concrete and its influencing parameters, and to obtain optimal parameter values, the particle swarm optimization (PSO) - back propagation (BP) - genetic algorithm (GA) method (PSO-BP-GA) is adopted. First, 80 % of the 17 sets obtained from the BB test were used as the training samples for the BP neural network (BPNN), while the remaining 20 % served as test samples. Then, the PSO is used to optimize the weights and thresholds within the BPNN. After deriving the objective function, GA was utilized to perform inverse function optimization, targeting repose angle of 32.07°. Finally, the static friction coefficient (X1) between wet concrete particles was determined to be 0.158, the rolling friction coefficient (X2) 0.187, and the surface energy (X3) 1.580 J/m2. With these parameters, five simulations were conducted, yielding an average repose angle of 32.31°. Compared with the actual repose angle, the relative error was 0.748 %.},
issn = {0039-2480}, pages = {29-39}, doi = {10.5545/sv-jme.2025.1440},
url = {https://www.sv-jme.eu/article/optimization-of-simulation-parameters-for-wet-concrete-particles-based-on-response-surface-methodology-and-pso-bp-ga-method/}
}
Liu, X.,Dong, S.,Xue, K.,Wang, P.,Ren, Y.
2026 November 72. Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 72:1-2
%A Liu, Xiaohui
%A Dong, Siyu
%A Xue, Kaidong
%A Wang, Penghui
%A Ren, Yongyi
%D 2026
%T Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method
%B 2026
%9 wet concrete particles; particle simulation parameter optimization; response surface analysis; PSO-BP-GA;
%! Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method
%K wet concrete particles; particle simulation parameter optimization; response surface analysis; PSO-BP-GA;
%X To address the challenges of low calibration efficiency and limited accuracy in the discrete element modeling of wet concrete within high-dimensional parameter spaces, this study developed a parameter calibration scheme that integrates experimental design and intelligent algorithms. It achieved efficient and high-precision inverse function optimization for determing contact parameters, thereby providing a robust foundation for related engineering simulations. Specifically, the repose angle of wet concrete was determined to be 32.07° based on the heap experiment. Through the Plackett-Burman (PB) experiment and steepest ascent experiment, the three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were identified. These parameters are static friction (X1), the coefficient of rolling friction (X2), and surface energy (X3). Subsequently, using the Box-Behnken (BB) test, the optimal 17 sets of combined data for these three significant factors were determined. To establish the objective function between the repose angle of wet concrete and its influencing parameters, and to obtain optimal parameter values, the particle swarm optimization (PSO) - back propagation (BP) - genetic algorithm (GA) method (PSO-BP-GA) is adopted. First, 80 % of the 17 sets obtained from the BB test were used as the training samples for the BP neural network (BPNN), while the remaining 20 % served as test samples. Then, the PSO is used to optimize the weights and thresholds within the BPNN. After deriving the objective function, GA was utilized to perform inverse function optimization, targeting repose angle of 32.07°. Finally, the static friction coefficient (X1) between wet concrete particles was determined to be 0.158, the rolling friction coefficient (X2) 0.187, and the surface energy (X3) 1.580 J/m2. With these parameters, five simulations were conducted, yielding an average repose angle of 32.31°. Compared with the actual repose angle, the relative error was 0.748 %.
%U https://www.sv-jme.eu/article/optimization-of-simulation-parameters-for-wet-concrete-particles-based-on-response-surface-methodology-and-pso-bp-ga-method/
%0 Journal Article
%R 10.5545/sv-jme.2025.1440
%& 29
%P 11
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 72
%N 1-2
%@ 0039-2480
%8 2025-11-28
%7 2025-11-28
Liu, Xiaohui, Siyu Dong, Kaidong Xue, Penghui Wang, & Yongyi Ren.
"Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method." Strojniški vestnik - Journal of Mechanical Engineering [Online], 72.1-2 (2026): 29-39. Web. 06 Apr. 2026
TY - JOUR
AU - Liu, Xiaohui
AU - Dong, Siyu
AU - Xue, Kaidong
AU - Wang, Penghui
AU - Ren, Yongyi
PY - 2026
TI - Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method
JF - Strojniški vestnik - Journal of Mechanical Engineering
DO - 10.5545/sv-jme.2025.1440
KW - wet concrete particles; particle simulation parameter optimization; response surface analysis; PSO-BP-GA;
N2 - To address the challenges of low calibration efficiency and limited accuracy in the discrete element modeling of wet concrete within high-dimensional parameter spaces, this study developed a parameter calibration scheme that integrates experimental design and intelligent algorithms. It achieved efficient and high-precision inverse function optimization for determing contact parameters, thereby providing a robust foundation for related engineering simulations. Specifically, the repose angle of wet concrete was determined to be 32.07° based on the heap experiment. Through the Plackett-Burman (PB) experiment and steepest ascent experiment, the three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were identified. These parameters are static friction (X1), the coefficient of rolling friction (X2), and surface energy (X3). Subsequently, using the Box-Behnken (BB) test, the optimal 17 sets of combined data for these three significant factors were determined. To establish the objective function between the repose angle of wet concrete and its influencing parameters, and to obtain optimal parameter values, the particle swarm optimization (PSO) - back propagation (BP) - genetic algorithm (GA) method (PSO-BP-GA) is adopted. First, 80 % of the 17 sets obtained from the BB test were used as the training samples for the BP neural network (BPNN), while the remaining 20 % served as test samples. Then, the PSO is used to optimize the weights and thresholds within the BPNN. After deriving the objective function, GA was utilized to perform inverse function optimization, targeting repose angle of 32.07°. Finally, the static friction coefficient (X1) between wet concrete particles was determined to be 0.158, the rolling friction coefficient (X2) 0.187, and the surface energy (X3) 1.580 J/m2. With these parameters, five simulations were conducted, yielding an average repose angle of 32.31°. Compared with the actual repose angle, the relative error was 0.748 %.
UR - https://www.sv-jme.eu/article/optimization-of-simulation-parameters-for-wet-concrete-particles-based-on-response-surface-methodology-and-pso-bp-ga-method/
@article{{sv-jme}{sv-jme.2025.1440},
author = {Liu, X., Dong, S., Xue, K., Wang, P., Ren, Y.},
title = {Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {72},
number = {1-2},
year = {2026},
doi = {10.5545/sv-jme.2025.1440},
url = {https://www.sv-jme.eu/article/optimization-of-simulation-parameters-for-wet-concrete-particles-based-on-response-surface-methodology-and-pso-bp-ga-method/}
}
TY - JOUR
AU - Liu, Xiaohui
AU - Dong, Siyu
AU - Xue, Kaidong
AU - Wang, Penghui
AU - Ren, Yongyi
PY - 2025/11/28
TI - Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method
JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 72, No 1-2 (2026): Strojniški vestnik - Journal of Mechanical Engineering
DO - 10.5545/sv-jme.2025.1440
KW - wet concrete particles, particle simulation parameter optimization, response surface analysis, PSO-BP-GA,
N2 - To address the challenges of low calibration efficiency and limited accuracy in the discrete element modeling of wet concrete within high-dimensional parameter spaces, this study developed a parameter calibration scheme that integrates experimental design and intelligent algorithms. It achieved efficient and high-precision inverse function optimization for determing contact parameters, thereby providing a robust foundation for related engineering simulations. Specifically, the repose angle of wet concrete was determined to be 32.07° based on the heap experiment. Through the Plackett-Burman (PB) experiment and steepest ascent experiment, the three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were identified. These parameters are static friction (X1), the coefficient of rolling friction (X2), and surface energy (X3). Subsequently, using the Box-Behnken (BB) test, the optimal 17 sets of combined data for these three significant factors were determined. To establish the objective function between the repose angle of wet concrete and its influencing parameters, and to obtain optimal parameter values, the particle swarm optimization (PSO) - back propagation (BP) - genetic algorithm (GA) method (PSO-BP-GA) is adopted. First, 80 % of the 17 sets obtained from the BB test were used as the training samples for the BP neural network (BPNN), while the remaining 20 % served as test samples. Then, the PSO is used to optimize the weights and thresholds within the BPNN. After deriving the objective function, GA was utilized to perform inverse function optimization, targeting repose angle of 32.07°. Finally, the static friction coefficient (X1) between wet concrete particles was determined to be 0.158, the rolling friction coefficient (X2) 0.187, and the surface energy (X3) 1.580 J/m2. With these parameters, five simulations were conducted, yielding an average repose angle of 32.31°. Compared with the actual repose angle, the relative error was 0.748 %.
UR - https://www.sv-jme.eu/article/optimization-of-simulation-parameters-for-wet-concrete-particles-based-on-response-surface-methodology-and-pso-bp-ga-method/
Liu, Xiaohui, Dong, Siyu, Xue, Kaidong, Wang, Penghui, AND Ren, Yongyi.
"Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 72 Number 1-2 (28 November 2025)
