Development on Machine Learning for Durability Prediction of Concrete Materials; [机器学习预测混凝土材料耐久性的研究进展] - Details

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Abstract：

The　durability　evaluation　of　concrete　materials　based　on　the　related　experiments　has　a　low　economic　efficiency　ratio.　The　prediction　accuracy　of　the　conventional　empirical　formula　for　durability　is　restricted　and　the　proportions　of　concrete　mix　cannot　be　calculated　according　to　the　performance.　It　is　thus　necessary　to　develop　a　novel　and　efficient　material　quality　control　and　performance　prediction　tool.　In　this　review,　the　process　of　building　machine　learning　models　was　stated.　The　basic　working　flow　and　advantages　of　common　algorithms,　as　well　as　the　durability　index　prediction　algorithms　based　on　machine　learning　were　summarized.　Its　application　effects　and　development　direction　were　discussed.　This　review　can　provide　a　basis　for　the　in-depth　development　and　application　of　machine　learning　technology　in　the　field　of　concrete.　©　2023　Chinese　Ceramic　Society.　All　rights　reserved.

Keyword：

concrete prediction machine learning durability algorithm

Author Community：

[ 1 ] [Liu X.]National Engineering Laboratory for Industrial Big-Data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Liu X.]Key Laboratory of Advanced Functional Materials of Ministry of Education, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Wang S.]National Engineering Laboratory for Industrial Big-Data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Wang S.]Key Laboratory of Advanced Functional Materials of Ministry of Education, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Lu L.]National Engineering Laboratory for Industrial Big-Data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Lu L.]Key Laboratory of Advanced Functional Materials of Ministry of Education, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Chen M.]State Key Laboratory of Silicate Materials for Architectures, Wuhan University of Technology, Wuhan, 430070, China
[ 8 ] [Zhai Y.]National Engineering Laboratory for Industrial Big-Data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China
[ 9 ] [Zhai Y.]Key Laboratory of Advanced Functional Materials of Ministry of Education, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China
[ 10 ] [Cui S.]National Engineering Laboratory for Industrial Big-Data Application Technology, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China
[ 11 ] [Cui S.]Key Laboratory of Advanced Functional Materials of Ministry of Education, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China

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Source ：

Journal of the Chinese Ceramic Society

ISSN： 0454-5648

Year： 2023

Issue： 8

Volume： 51

Page： 2062-2073

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 5

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 9

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