Reverse design for mixture proportions of recycled brick aggregate concrete using machine learning-based meta-heuristic algorithm: A multi-objective driven study

IF 7.2 2区 工程技术 Q1 CHEMISTRY, MULTIDISCIPLINARY
Yuhan Wang , Shuyuan Zhang , Zhe Zhang , Yong Yu , Jinjun Xu
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Abstract

Construction and Demolition Wastes (CDW) have a significant impact on global waste streams. Brick waste stands out as a prominent type of CDW, and numerous studies have explored its recycling for the creation of environmentally-friendly concrete. Reverse design of recycled brick aggregate concrete (RBAC) mixture proportion is presented in this paper with a focus on four key objectives, that is: compressive strength, cost, and environmental elements (i.e., energy consumption and carbon emission). Based on compiled experimental datasets of 374 samples, the back propagation neural network (BP), random forest (RF), and four meta-heuristic algorithm optimization models were constructed to achieve the desired compressive strength objective. In all machine learning (ML) methods, the compressive strength of RBAC can be predicted with high accuracy, with the SSA-BP (optimized back propagation neural network model using the sparrow search algorithm) model achieving superior results (i.e., NSE=0.91, RPD=3.2). The SSA-BP is therefore used as the objective function for compressive strength. The economic objective is primarily influenced by material costs, and the objective functions of energy consumption and carbon emission are determined by various aspects of production, transportation, and their mixing processes. In order to obtain the optimal RBAC design, the Non-Dominated Sorting Genetic Algorithm (NSGA-III) was implemented considering imperative constraints. Results indicate that cement amount and recycled brick aggregate (RBA)-to-natural aggregate proportion have a positive impact on the compressive strength. The suggested design framework allows for the creation of RBAC composite designs with varying levels of RBA substitution rates and strength targets, providing valuable guidance for tackling the CDW challenge and optimizing RBA usage.
利用基于机器学习的元启发式算法逆向设计再生砖骨料混凝土的混合比例:多目标驱动研究
建筑和拆除废物(CDW)对全球废物流产生了重大影响。砖块废弃物作为一种突出的建筑和拆迁废弃物,许多研究都探讨了如何将其回收利用以制造环保混凝土。本文介绍了再生砖骨料混凝土(RBAC)混合物配比的逆向设计,重点关注四个关键目标,即抗压强度、成本和环境因素(即能源消耗和碳排放)。基于 374 个样本的实验数据集,构建了反向传播神经网络(BP)、随机森林(RF)和四种元启发式算法优化模型,以实现所需的抗压强度目标。在所有机器学习(ML)方法中,RBAC 的抗压强度都能得到较高的预测精度,其中 SSA-BP(使用麻雀搜索算法的优化反向传播神经网络模型)模型取得了较好的结果(即 NSE=0.91,RPD=3.2)。因此,SSA-BP 被用作抗压强度的目标函数。经济目标主要受材料成本的影响,而能耗和碳排放的目标函数则由生产、运输及其混合过程的各个方面决定。为了获得最佳的 RBAC 设计,考虑到必要的约束条件,采用了非支配排序遗传算法(NSGA-III)。结果表明,水泥用量和再生砖骨料(RBA)与天然骨料的比例对抗压强度有积极影响。建议的设计框架允许创建具有不同水平 RBA 替代率和强度目标的 RBAC 复合材料设计,为应对 CDW 挑战和优化 RBA 使用提供了宝贵的指导。
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来源期刊
Journal of CO2 Utilization
Journal of CO2 Utilization CHEMISTRY, MULTIDISCIPLINARY-ENGINEERING, CHEMICAL
CiteScore
13.90
自引率
10.40%
发文量
406
审稿时长
2.8 months
期刊介绍: The Journal of CO2 Utilization offers a single, multi-disciplinary, scholarly platform for the exchange of novel research in the field of CO2 re-use for scientists and engineers in chemicals, fuels and materials. The emphasis is on the dissemination of leading-edge research from basic science to the development of new processes, technologies and applications. The Journal of CO2 Utilization publishes original peer-reviewed research papers, reviews, and short communications, including experimental and theoretical work, and analytical models and simulations.
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