A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

被引：0
作者：
Jin Duan
Panagiotis G. Asteris
Hoang Nguyen
Xuan-Nam Bui
Hossein Moayedi
机构：
[1] Central South University,School of Civil Engineering
[2] School of Pedagogical and Technological Education,Computational Mechanics Laboratory
[3] Duy Tan University,Institute of Research and Development
[4] Hanoi University of Mining and Geology,Department of Surface Mining, Mining Faculty
[5] Hanoi University of Mining and Geology,Center for Mining, Electro
[6] Ton Duc Thang University,Mechanical Research
[7] Ton Duc Thang University,Department for Management of Science and Technology Development
来源：
Engineering with Computers | 2021年 / 37卷
关键词：
Green construction; Recycled aggregate concrete; ICA-XGBoost; Hybrid artificial intelligence; Compressive strength;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the compressive strength of this concrete material is considered as a crucial parameter and an important concern for construction engineers regarding its application. In the present work, the 28-days compressive strength of recycled aggregate concrete is investigated through four artificial intelligence techniques based on a meta-heuristic search of sociopolitical algorithm (i.e. ICA) and XGBoost, called the ICA-XGBoost model. Based on performance indices, the optimum among these developed models proved to be ICA-XGBoost model. Namely, findings demonstrated that the proposed ICA-XGBoost model performed better than the other models (i.e. ICA-ANN, ICA-SVR, and ICA-ANFIS models) in estimating compressive strength of recycled aggregate concrete. The suggested model can be used in construction engineering in order to ensure adequate mechanical performance of the recycled aggregate concrete and allow its safe use for building purposes.
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页码：3329 / 3346
页数：17
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