Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model

被引:74
作者
Nunez, Itzel [1 ]
Marani, Afshin [1 ]
Nehdi, Moncef L. [1 ]
机构
[1] Western Univ, Dept Civil & Environm Engn, London, ON N6G 1G8, Canada
关键词
recycled aggregate concrete; machine learning; model; Gaussian process; deep learning; gradient boosting; regression trees; gated recurrent unit; COMPRESSIVE STRENGTH PREDICTION; MECHANICAL-PROPERTIES; FLY-ASH; SILICA FUME; ARTIFICIAL-INTELLIGENCE; ENGINEERING PROPERTIES; HARDENED PROPERTIES; CURING CONDITIONS; DEMOLITION WASTE; COARSE AGGREGATE;
D O I
10.3390/ma13194331
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Recycled aggregate concrete (RAC) contributes to mitigating the depletion of natural aggregates, alleviating the carbon footprint of concrete construction, and averting the landfilling of colossal amounts of construction and demolition waste. However, complexities in the mixture optimization of RAC due to the variability of recycled aggregates and lack of accuracy in estimating its compressive strength require novel and sophisticated techniques. This paper aims at developing state-of-the-art machine learning models to predict the RAC compressive strength and optimize its mixture design. Results show that the developed models including Gaussian processes, deep learning, and gradient boosting regression achieved robust predictive performance, with the gradient boosting regression trees yielding highest prediction accuracy. Furthermore, a particle swarm optimization coupled with gradient boosting regression trees model was developed to optimize the mixture design of RAC for various compressive strength classes. The hybrid model achieved cost-saving RAC mixture designs with lower environmental footprint for different target compressive strength classes. The model could be further harvested to achieve sustainable concrete with optimal recycled aggregate content, least cost, and least environmental footprint.
引用
收藏
页码:1 / 24
页数:24
相关论文
共 50 条
[41]   Study of recycled concrete aggregate quality and its relationship with recycled concrete compressive strength using database analysis [J].
Gonzalez-Taboada, I. ;
Gonzalez-Fonteboa, B. ;
Martinez-Abella, F. ;
Carro-Lopez, D. .
MATERIALES DE CONSTRUCCION, 2016, 66 (323)
[42]   Efficient creep prediction of recycled aggregate concrete via machine learning algorithms [J].
Feng, Jinpeng ;
Zhang, Haowei ;
Gao, Kang ;
Liao, Yuchen ;
Gao, Wei ;
Wu, Gang .
CONSTRUCTION AND BUILDING MATERIALS, 2022, 360
[43]   Automated machine learning techniques for estimating of elastic modulus of recycled aggregate concrete [J].
Chen, Chien-Ta ;
Xiao, Lianghao ;
Tsai, Shing-Wen .
STRUCTURAL CONCRETE, 2024, 25 (02) :1324-1342
[44]   A framework for low-carbon mix design of recycled aggregate concrete with supplementary cementitious materials using machine learning and optimization algorithms [J].
Golafshani, Emadaldin Mohammadi ;
Behnood, Ali ;
Kim, Taehwan ;
Ngo, Tuan ;
Kashani, Alireza .
STRUCTURES, 2024, 61
[45]   Estimation of splitting tensile strength of modified recycled aggregate concrete using hybrid algorithms [J].
Zhu, Yirong ;
Huang, Lihua ;
Zhang, Zhijun ;
Bayrami, Behzad .
STEEL AND COMPOSITE STRUCTURES, 2022, 44 (03) :375-392
[46]   Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate [J].
Pal, Avijit ;
Ahmed, Khondaker Sakil ;
Hossain, F. M. Zahid ;
Alam, M. Shahria .
JOURNAL OF CLEANER PRODUCTION, 2023, 423
[47]   Prediction of creep of recycled aggregate concrete using back-propagation neural network and support vector machine [J].
Rong, Xian ;
Liu, Yinbo ;
Chen, Pang ;
Lv, Xueyuan ;
Shen, Chen ;
Yao, Boqiang .
STRUCTURAL CONCRETE, 2023, 24 (02) :2229-2244
[48]   Fracture prediction in recycled aggregate concrete using experience-based machine learning with a defective database [J].
Han, Xiangyu ;
Zhao, Qilong ;
He, Xinru ;
Jia, Bin ;
Xiao, Yihuan ;
Si, Ruizhe ;
Li, Qionglin ;
Hu, Xiaozhi .
THEORETICAL AND APPLIED FRACTURE MECHANICS, 2025, 139
[49]   Multicriteria based optimization of second generation recycled aggregate concrete [J].
Shmlls, Maysam ;
Abed, Mohammed ;
Horvath, Tamas ;
Bozsaky, David .
CASE STUDIES IN CONSTRUCTION MATERIALS, 2022, 17
[50]   Multicriteria optimization of natural and recycled aggregate concrete for structural use [J].
Tosic, Nikola ;
Marinkovic, Snezana ;
Dasic, Tina ;
Stanic, Milos .
JOURNAL OF CLEANER PRODUCTION, 2015, 87 :766-776