Optimization of simulation parameters for wet concrete particles based on response surface methodology and PSO-BP-GA method

Liu, Xiaohui (author), Dong, Siyu (author), Xue, Kaidong (author), Wang, Penghui (author), Ren, Yongyi (author)

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 %.

• Language: English
• Keywords: wet concrete particles, particle simulation parameter optimization, response surface analysis, PSO-BP-GA
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: Str. 29-39
• Numbering: Vol. 72, no. 1/2
• PID: 20.500.12556/RUL-181064
• UDC: 532.135:691.32:519.876.5
• ISSN on article: 0039-2480
• DOI: 10.5545/sv-jme.2025.1440
• COBISS.SI-ID: 272796163
• Publication date in RUL: 24.03.2026

Record is a part of a journal

• Title: Strojniški vestnik
• Shortened title: Stroj. vestn.
• Publisher: Zveza strojnih inženirjev in tehnikov Slovenije [etc.], = Association of Mechanical Engineers and Technicians of Slovenia [etc.
• ISSN: 0039-2480
• COBISS.SI-ID: 762116

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Title: Optimizacija simulacijskih parametrov mokrih betonskih delcev na osnovi metode odzivne površine in metode PSO-BP-GA

Abstract

Za reševanje izzivov nizke učinkovitosti umerjanja in omejene natančnosti pri diskretnem večparametričnem modeliranju posedanja mokrega betona, je bila v tej študiji razvita shema umerjanja parametrov, ki združuje načrtovanje eksperimentov in inteligentne algoritme. S tem je bila dosežena učinkovita in visoko natančna optimizacija inverzne funkcije za določitev kontaktnih parametrov, kar predstavlja zanesljivo osnovo za sorodne inženirske simulacije. Najprej je bil z eksperimentom poseda določen kot naravnega poseda mokrega betona, ki znaša 32,07°. Z uporabo Plackett– Burmanovega (PB) eksperimenta in eksperimenta najstrmejšega vzpona so bili identificirani trije parametri z največjim vplivom na kot naravnega poseda mokrega betona ter njihova optimalna območja vrednosti: statični koeficient trenja (X1), koeficient kotalnega trenja (X2) in površinska energija (X3). Nato je bilo z Box–Behnkenovim (BB) eksperimentalnim načrtom določenih 17 optimalnih kombinacij podatkov za te tri ključne parametre. Za vzpostavitev ciljne funkcije med kotom naravnega poseda mokrega betona in vplivnimi parametri ter za pridobitev optimalnih vrednosti parametrov je bila uporabljena metoda roja delcev, povratnega širjenja in genetskega algoritma (PSO-BP-GA). Najprej je bilo 80 % izmed 17 nizov podatkov, pridobljenih z BBtestom, uporabljenih kot učni vzorci za nevronsko mrežo s povratnim širjenjem (BPNN), preostalih 20 % pa kot testni vzorci. Nato je bil algoritem PSO uporabljen za optimizacijo uteži in pragov znotraj BPNN. Po določitvi ciljne funkcije je bil genetski algoritem (GA) uporabljen za inverzno optimizacijo, pri čemer je bil ciljni kot naravnega nasipa 32,07°. Končno so bile določene optimalne vrednosti parametrov: statični koeficient trenja med delci mokrega betona (X1) 0,158, koeficient kotalnega trenja (X2) 0,187 in površinska energija (X3) 1,580 J/m2. Na podlagi teh parametrov je bilo izvedenih pet simulacij, pri čemer je bil povprečni izračunani kot naravnega poseda 32,31°. V primerjavi z dejansko vrednostjo je relativna napaka znašala 0,748 %.

• Keywords: mokri betonski delci, optimizacija parametrov simulacije delcev, analiza odzivne površine, PSO-BP-GA

Projects

• Funder: Other - Other funder or multiple funders
• Funding programme: Nation Natural Science Foundation of China
• Project number: 52375080

• Funder: Other - Other funder or multiple funders
• Project number: 2024GX-YBXM-247
• Name: Key Research and Development Project in Shaanxi Province