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. Articles in Press, [S.l.], v. 0, n.0, p. , 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: 01 jan. 2026. doi:http://dx.doi.org/.
Liu, X., Dong, S., Xue, K., Wang, P., & Ren, Y. (0). Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method. Articles in Press, 0(0), . doi:http://dx.doi.org/
@article{.,
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 = {Articles in Press},
volume = {0},
number = {0},
year = {0},
keywords = {wet concrete particles; particle simulation parameter optimization; response surface analysis; PSO-BP-GA; },
abstract = {In order to obtain the contact parameters of wet concrete particles, this paper evaluates the repose angle of wet concrete as 32.07° based on the heap experiment. Through the Plackett-Burman(PB) experiment and the steepest ascent experiment, three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were screened out. These three parameters are the static friction (X1), the coefficient of rolling friction (X2), the surface energy (X3). Subsequently, the optimal 17 sets of combined data for these three significant factors were determined, when using the Box-Behnken(BB) test. To establish the objective function between the repose angle of wet concrete and these parameters and obtain the 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 of data obtained from the Box-Behnken test were used as the training samples for the BP neural network, with the remaining 20% serving as the test samples. Then, the PSO is used to optimize the weights and thresholds of the input data in the BP neural network. After obtaining the objective function, the GA was utilized to perform inverse function optimization, targeting repose angle of 32.07°. Finally, the static friction (X1) between wet concrete particles was determined to be 0.158, the rolling friction (X2) to be 0.187, and the surface energy (X3) to be 1.580. 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 = {}, doi = {},
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. 0 November 0. Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method. Articles in Press. [Online] 0:0
%A Liu, Xiaohui %A Dong, Siyu %A Xue, Kaidong %A Wang, Penghui %A Ren, Yongyi %D 0 %T Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method %B 0 %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 In order to obtain the contact parameters of wet concrete particles, this paper evaluates the repose angle of wet concrete as 32.07° based on the heap experiment. Through the Plackett-Burman(PB) experiment and the steepest ascent experiment, three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were screened out. These three parameters are the static friction (X1), the coefficient of rolling friction (X2), the surface energy (X3). Subsequently, the optimal 17 sets of combined data for these three significant factors were determined, when using the Box-Behnken(BB) test. To establish the objective function between the repose angle of wet concrete and these parameters and obtain the 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 of data obtained from the Box-Behnken test were used as the training samples for the BP neural network, with the remaining 20% serving as the test samples. Then, the PSO is used to optimize the weights and thresholds of the input data in the BP neural network. After obtaining the objective function, the GA was utilized to perform inverse function optimization, targeting repose angle of 32.07°. Finally, the static friction (X1) between wet concrete particles was determined to be 0.158, the rolling friction (X2) to be 0.187, and the surface energy (X3) to be 1.580. 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 %& %P 1 %J Articles in Press %V 0 %N 0 %@ 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." Articles in Press [Online], 0.0 (0): . Web. 01 Jan. 2026
TY - JOUR AU - Liu, Xiaohui AU - Dong, Siyu AU - Xue, Kaidong AU - Wang, Penghui AU - Ren, Yongyi PY - 0 TI - Optimization of Simulation Parameters for Wet Concrete Particles Based on Response Surface Methodology and PSO-BP-GA Method JF - Articles in Press DO - KW - wet concrete particles; particle simulation parameter optimization; response surface analysis; PSO-BP-GA; N2 - In order to obtain the contact parameters of wet concrete particles, this paper evaluates the repose angle of wet concrete as 32.07° based on the heap experiment. Through the Plackett-Burman(PB) experiment and the steepest ascent experiment, three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were screened out. These three parameters are the static friction (X1), the coefficient of rolling friction (X2), the surface energy (X3). Subsequently, the optimal 17 sets of combined data for these three significant factors were determined, when using the Box-Behnken(BB) test. To establish the objective function between the repose angle of wet concrete and these parameters and obtain the 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 of data obtained from the Box-Behnken test were used as the training samples for the BP neural network, with the remaining 20% serving as the test samples. Then, the PSO is used to optimize the weights and thresholds of the input data in the BP neural network. After obtaining the objective function, the GA was utilized to perform inverse function optimization, targeting repose angle of 32.07°. Finally, the static friction (X1) between wet concrete particles was determined to be 0.158, the rolling friction (X2) to be 0.187, and the surface energy (X3) to be 1.580. 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{{}{.},
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 = {Articles in Press},
volume = {0},
number = {0},
year = {0},
doi = {},
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/}
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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 - Articles in Press; Vol 0, No 0 (0): Articles in Press DO - KW - wet concrete particles, particle simulation parameter optimization, response surface analysis, PSO-BP-GA, N2 - In order to obtain the contact parameters of wet concrete particles, this paper evaluates the repose angle of wet concrete as 32.07° based on the heap experiment. Through the Plackett-Burman(PB) experiment and the steepest ascent experiment, three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were screened out. These three parameters are the static friction (X1), the coefficient of rolling friction (X2), the surface energy (X3). Subsequently, the optimal 17 sets of combined data for these three significant factors were determined, when using the Box-Behnken(BB) test. To establish the objective function between the repose angle of wet concrete and these parameters and obtain the 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 of data obtained from the Box-Behnken test were used as the training samples for the BP neural network, with the remaining 20% serving as the test samples. Then, the PSO is used to optimize the weights and thresholds of the input data in the BP neural network. After obtaining the objective function, the GA was utilized to perform inverse function optimization, targeting repose angle of 32.07°. Finally, the static friction (X1) between wet concrete particles was determined to be 0.158, the rolling friction (X2) to be 0.187, and the surface energy (X3) to be 1.580. 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" Articles in Press [Online], Volume 0 Number 0 (28 November 2025)
Articles in Press
In order to obtain the contact parameters of wet concrete particles, this paper evaluates the repose angle of wet concrete as 32.07° based on the heap experiment. Through the Plackett-Burman(PB) experiment and the steepest ascent experiment, three parameters with the greatest influence on the repose angle of wet concrete and their optimal value ranges were screened out. These three parameters are the static friction (X1), the coefficient of rolling friction (X2), the surface energy (X3). Subsequently, the optimal 17 sets of combined data for these three significant factors were determined, when using the Box-Behnken(BB) test. To establish the objective function between the repose angle of wet concrete and these parameters and obtain the 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 of data obtained from the Box-Behnken test were used as the training samples for the BP neural network, with the remaining 20% serving as the test samples. Then, the PSO is used to optimize the weights and thresholds of the input data in the BP neural network. After obtaining the objective function, the GA was utilized to perform inverse function optimization, targeting repose angle of 32.07°. Finally, the static friction (X1) between wet concrete particles was determined to be 0.158, the rolling friction (X2) to be 0.187, and the surface energy (X3) to be 1.580. 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%.