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13.03.2020 | Original Article

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

verfasst von: Jin Duan, Panagiotis G. Asteris, Hoang Nguyen, Xuan-Nam Bui, Hossein Moayedi

Erschienen in: Engineering with Computers | Ausgabe 4/2021

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Abstract

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.

Vorheriger Artikel Numerical study of the variable-order fractional version of the nonlinear fourth-order 2D diffusion-wave equation via 2D Chebyshev wavelets

Nächster Artikel Computational modification of neural systems using a novel stochastic search scheme, namely evaporation rate-based water cycle algorithm: an application in geotechnical issues

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Titel: A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model
verfasst von: Jin Duan
Panagiotis G. Asteris
Hoang Nguyen
Xuan-Nam Bui
Hossein Moayedi
Publikationsdatum: 13.03.2020
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe 4/2021
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-020-01003-0

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