Design method for polyurethane-modified asphalt by using Kriging-Particle Swarm Optimization algorithm

被引:8
|
作者
Lu, Pengzhen [1 ]
Ye, Kai [1 ]
Jin, Tian [1 ]
Ma, Yiheng [1 ]
Huang, Simin [1 ]
Zhou, Chenhao [1 ]
机构
[1] Zhejiang Univ Technol, Hangzhou 310014, Zhejiang, Peoples R China
关键词
Polyurethane-modified bitumen; Preparation process; Machine-learning algorithm; Kriging-PSO model; Design method; INDICATOR; MODEL;
D O I
10.1016/j.engappai.2022.105609
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The preparation process of polyurethane (PU)-modified bitumen involves numerous design parameters and performance response indexes. Due to the variety of polyurethane modifiers, the preparation process of the polyurethane-modified bitumen is not universally applicable. However, the traditional methods such as the response surface method and orthogonal design method have some problems such as low accuracy and a large number of samples required in the preparation process design. Therefore, according to different application environments, the problem of determining the process parameters of the polyurethane-modified bitumen accurately and efficiently needs to be solved urgently. Using Kriging-Particle Swarm Optimization (PSO) algorithm, an efficient process design method for the preparation of polyurethane modified asphalt is proposed in this paper. Combined with the sensitivity analysis method, the relatively sensitive response indexes are screened out to reduce the number of samples and improve the design accuracy. Among them, the dispersion coefficient was evaluated by fluorescence microscopy test using the Christiansen coefficient method to evaluate the uniformity of the dispersed phase of the polyurethane modifier. According to the target performance, the main process parameters of PU modified asphalt were obtained by the Kriging-Particle Swarm Optimization algorithm: shear time 86 min, shear speed 2450 rpm, shear temperature 148 degrees C, and polyurethane content 18.6%. The polyurethane-modified bitumen prepared by this optimal process met the expected performance indicators. This study achieved the expected results with a small number of samples, indicating that this method can achieve the purpose of designing the ideal process parameters of polyurethane-modified asphalt efficiently.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Distribution Systems Reconfiguration using a modified particle swarm optimization algorithm
    Abdelaziz, A. Y.
    Mohammed, F. M.
    Mekhamer, S. F.
    Badr, M. A. L.
    ELECTRIC POWER SYSTEMS RESEARCH, 2009, 79 (11) : 1521 - 1530
  • [32] Synthesis of the antenna array using a modified particle swarm optimization algorithm
    Chen, TB
    Jiao, YC
    Zhang, FS
    COMPUTATIONAL INTELLIGENCE AND SECURITY, PT 1, PROCEEDINGS, 2005, 3801 : 651 - 656
  • [33] Computing of Flight Envelope Using Modified Particle Swarm Optimization Algorithm
    Zhu Sibin
    Li Guixian
    Han Junwei
    2010 2ND INTERNATIONAL ASIA CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS (CAR 2010), VOL 1, 2010, : 291 - 294
  • [34] INSERTION LOSS METHOD AND PARTICLE SWARM OPTIMIZATION ALGORITHM IN FILTER DESIGN
    Kon, Ante
    Burum, Niksa
    Vilovic, Ivan
    NASE MORE, 2010, 57 (1-2): : 56 - 61
  • [35] An intelligent method to design laser resonator with particle swarm optimization algorithm
    韩克祯
    黄燕
    刘芳芳
    庞鑫
    胡平
    刘国伟
    秦华
    张芳
    葛筱璐
    刘晓娟
    耿雪
    Optoelectronics Letters, 2018, 14 (06) : 425 - 428
  • [36] An intelligent method to design laser resonator with particle swarm optimization algorithm
    Han K.-Z.
    Huang Y.
    Liu F.-F.
    Pang X.
    Hu P.
    Liu G.-W.
    Qin H.
    Zhang F.
    Ge X.-L.
    Liu X.-J.
    Geng X.
    Optoelectronics Letters, 2018, 14 (6) : 425 - 428
  • [37] Components optimization of polyurethane-modified asphalt binder towards compatibility: Insight from molecular dynamics simulations
    Zhang, Mingliang
    Hu, Zhe
    Zhao, Jing
    Li, Hanjun
    Zhang, Jiupeng
    Lyu, Lei
    Wang, Xiaoqian
    Niu, Zhenxing
    Cai, Jun
    Pei, Jianzhong
    CONSTRUCTION AND BUILDING MATERIALS, 2024, 448
  • [38] Design Optimization of Pin Fin Geometry Using Particle Swarm Optimization Algorithm
    Hamadneh, Nawaf
    Khan, Waqar A.
    Sathasivam, Saratha
    Ong, Hong Choon
    PLOS ONE, 2013, 8 (05):
  • [39] Optimization of welding process parameters by combining Kriging surrogate with particle swarm optimization algorithm
    Ping Jiang
    Longchao Cao
    Qi Zhou
    Zhongmei Gao
    Youmin Rong
    Xinyu Shao
    The International Journal of Advanced Manufacturing Technology, 2016, 86 : 2473 - 2483
  • [40] Optimization of welding process parameters by combining Kriging surrogate with particle swarm optimization algorithm
    Jiang, Ping
    Cao, Longchao
    Zhou, Qi
    Gao, Zhongmei
    Rong, Youmin
    Shao, Xinyu
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 86 (9-12): : 2473 - 2483