Machine Learning Driven Fluidity and Rheological Properties Prediction of Fresh Cement-Based Materials

被引:0
|
作者
Liu, Yi [1 ,2 ]
Mohammed, Zeyad M. A. [1 ,2 ]
Ma, Jialu [1 ,2 ]
Xia, Rui [1 ,2 ]
Fan, Dongdong [3 ]
Tang, Jie [3 ]
Yuan, Qiang [1 ,2 ]
机构
[1] Cent South Univ, Sch Civil Engn, Changsha 410075, Peoples R China
[2] Natl Engn Res Ctr High speed Railway Construct Tec, Changsha 410075, Peoples R China
[3] Anhui Engineer Mat Technol Co Ltd, CTCE Grp, Hefei 230041, Peoples R China
基金
国家重点研发计划;
关键词
machine learning; workability; rheological property; feature importance analysis; PLASTIC VISCOSITY; OPTIMIZATION; PERFORMANCE; CONCRETE; MIXTURE;
D O I
10.3390/ma17225400
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
Controlling workability during the design stage of cement-based material mix ratios is a highly time-consuming and labor-intensive task. Applying artificial intelligence (AI) methods to predict and optimize the workability of cement-based materials can significantly enhance the efficiency of mix design. In this study, experimental testing was conducted to create a dataset of 233 samples, including fluidity, dynamic yield stress, and plastic viscosity of cement-based materials. The proportions of cement, fly ash (FA), silica fume (SF), water, superplasticizer (SP), hydroxypropyl methylcellulose (HPMC), and sand were selected as inputs. Machine learning (ML) methods were employed to establish predictive models for these three early workability indicators. To improve prediction capability, optimized hybrid models, such as Particle Swarm Optimization (PSO)-based CatBoost and XGBoost, were adopted. Furthermore, the influence of individual input variables on each workability indicator of the cement-based material was examined using Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses. This study provides a novel reference for achieving rapid and accurate control of cement-based material workability.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method
    Huang, Jiandong
    Zhou, Mengmeng
    Yuan, Hongwei
    Sabri, Mohanad Muayad Sabri
    Li, Xiang
    MATERIALS, 2022, 15 (10)
  • [2] Prediction of cement-based mortars compressive strength using machine learning techniques
    Asteris, Panagiotis G.
    Koopialipoor, Mohammadreza
    Armaghani, Danial J.
    Kotsonis, Evgenios A.
    Lourenco, Paulo B.
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (19) : 13089 - 13121
  • [3] XFEM and machine learning combined approach for failure prediction of microcapsules in cement-based self-healing materials
    Song, Qiao
    Wang, Xianfeng
    Fang, Yuan
    Xing, Feng
    CONSTRUCTION AND BUILDING MATERIALS, 2023, 407
  • [4] Rheological Properties and Flow Behaviour of Cement-Based Materials Modified by Carbon Nanotubes and Plasticising Admixtures
    Skripkiunas, Gintautas
    Karpova, Ekaterina
    Bendoraitiene, Joana
    Barauskas, Irmantas
    FLUIDS, 2020, 5 (04)
  • [5] The study of effect of carbon nanotubes on the compressive strength of cement-based materials based on machine learning
    Li, Yue
    Li, Hongwen
    Jin, Caiyun
    Shen, Jiale
    CONSTRUCTION AND BUILDING MATERIALS, 2022, 358
  • [6] Influence of Nano Silica on Fresh and Hardened Properties of Cement-based Materials - A Review
    Gayathiri, K.
    Praveenkumar, S.
    SILICON, 2022, 14 (14) : 8327 - 8357
  • [7] A machine learning model for predicting the mechanical strength of cement-based materials filled with waste rubber modified by PVA
    He, Zhengfeng
    Wu, Zhuofan
    Niu, Wenjun
    Wang, Fengcai
    Zhong, Shunjie
    Han, Zeyu
    Zhao, Qingxin
    FRONTIERS IN MATERIALS, 2024, 11
  • [8] Interactions between coral sand and polycarboxylate superplasticizer and their effects on rheological properties of cement-based materials -- A review
    Jiang, Changbiao
    Liu, Jianhui
    Liu, Leping
    Chen, Zheng
    Shi, Caijun
    CONSTRUCTION AND BUILDING MATERIALS, 2023, 389
  • [9] PREDICTION OF WATER SORPTIVITY IN CEMENT-BASED MATERIALS
    Wang, Li-Cheng
    4TH INTERNATIONAL SYMPOSIUM ON LIFETIME ENGINEERING OF CIVIL INFRASTRUCTURE, 2009, : 1042 - 1047
  • [10] Effects of CCCW on properties of cement-based materials: A review
    Hu, Xiaoyan
    Xiao, Jia
    Zhang, Zedi
    Wang, Conghao
    Long, Congyun
    Dai, Liang
    JOURNAL OF BUILDING ENGINEERING, 2022, 50