基于NSGA-II高性能混凝土配合比的多目标智能优化设计方法研究

硅酸盐通报 ›› 2024, Vol. 43 ›› Issue (10): 3645-3654.

基于NSGA-II高性能混凝土配合比的多目标智能优化设计方法研究

胡以婵, 翁贻令, 池浩, 胡雷, 彭浩, 梁健, 周富坚, 黄文胜, 解威威

广西路桥工程集团有限公司,南宁 530011

收稿日期:2024-01-06 修订日期:2024-04-01 出版日期:2024-10-15 发布日期:2024-10-16
通信作者: 解威威,博士,高级工程师。E-mail:ww.xie@foxmail.com
作者简介:胡以婵(1992—),女,工程师。主要从事人工智能算法对混凝土性能预测与配合比优化等的研究。E-mail:310233130@qq.com
基金资助:
中央引导地方科技发展资金项目(20221229);广西重点研发计划项目(桂科AB23026126);交通运输行业重点科技项目(2121MSI026)

Multi-Objective Intelligent Optimization Design Method Based on NSGA-II High Performance Concrete Mix Proportion

HU Yichan, WENG Yiling, CHI Hao, HU Lei, PENG Hao, LIANG Jian, ZHOU Fujian, HUANG Wensheng, XIE Weiwei

Guangxi Road and Bridge Engineering Group Co., Ltd., Nanning 530011, China

Received:2024-01-06 Revised:2024-04-01 Published:2024-10-15 Online:2024-10-16

摘要/Abstract

摘要： 为解决传统配合比设计存在的设计参数有限、过度依赖人工调整以及未能充分利用现有试验数据等问题,本文提出了一种新型的高性能混凝土(HPC)配合比智能优化策略,融合了人工智能算法与元启发式搜索技术,可实现考虑HPC多目标需求下的配合比设计。首先,构建大量HPC配合比数据及性能试验资料的数据库;其次,采用XGBoost、MLP、RF三种机器学习算法建立混凝土抗压强度与材料用量的非线性映射关系,并选择性能最佳的模型作为多目标智能优化中的强度目标函数及约束;然后,通过建立以抗压强度、经济性和环保性为目标的数学模型,并利用NSGA-II算法求解Pareto最优前沿;最后,结合理想点法获得唯一最优解。通过实际工程算例对所提出的方法进行了验证,结果表明,所提出的HPC配合比的多目标智能优化方法充分考虑了各变量之间的相互影响,极大地减少了试验数量,显著地提高了配合比设计效率,可为HPC多目标智能配合比优化设计提供参考。

关键词: 高性能混凝土, 强度预测模型, NSGA-II算法, 多目标优化, XGBoost算法

Abstract: In order to solve the problems of limited design parameters, over-reliance on manual adjustment and failure to make full use of the existing test data in the traditional proportion design, a novel intelligent optimization strategy for high performance concrete (HPC) was proposed, which integrates artificial intelligence algorithms and meta-heuristic search technology, and can realize the proportion design under the consideration of the multi-objective demand of HPC. Firstly, a database with a large amount of HPC proportion data and performance test information was constructed. Secondly, three machine learning algorithms, XGBoost, MLP and RF algorithm, were used to establish the nonlinear mapping relationship between compressive strength of concrete and the amount of material used, and the model with the best performance was selected as the strength objective function and constraints in the multi-objective optimization intelligent. Then, the Pareto optimal predicate was solved by establishing the mathematical model with the objectives of compressive strength, economy, and environmental protection, and the Pareto optimal predicate was solved by using the NSGA-II algorithm. Lastly, the unique optimal solution was obtained by combining with the ideal-point method. The proposed method was validated by practical engineering examples, and the results show that the proposed multi-objective intelligent optimization design method of high-performance concrete proportion takes into full consideration of interactions between variables, greatly reduces the number of tests, significantly improves the efficiency of the proportion design, and can provide a reference for the multi-objective intelligent proportion optimization design of HPC.

Key words: high performance concrete, strength prediction model, NSGA-II algorithm, multi-objective optimization, XGBoost algorithm

中图分类号:

胡以婵, 翁贻令, 池浩, 胡雷, 彭浩, 梁健, 周富坚, 黄文胜, 解威威. 基于NSGA-II高性能混凝土配合比的多目标智能优化设计方法研究[J]. 硅酸盐通报, 2024, 43(10): 3645-3654.

HU Yichan, WENG Yiling, CHI Hao, HU Lei, PENG Hao, LIANG Jian, ZHOU Fujian, HUANG Wensheng, XIE Weiwei. Multi-Objective Intelligent Optimization Design Method Based on NSGA-II High Performance Concrete Mix Proportion[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2024, 43(10): 3645-3654.

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