Loading [MathJax]/extensions/tex2jax.js

Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autorschaft

  • Min Wang
  • Mingfeng Du
  • Xiaoying Zhuang
  • Hui Lv

Organisationseinheiten

Externe Organisationen

  • Ltd.
  • Chongqing University
  • Tongji University

Details

OriginalspracheEnglisch
Seiten (von - bis)143–161
Seitenumfang19
FachzeitschriftFrontiers of Structural and Civil Engineering
Jahrgang19
Ausgabenummer1
Frühes Online-Datum8 Jan. 2025
PublikationsstatusVeröffentlicht - Jan. 2025

Abstract

Ultra-high performance concrete (UHPC) has gained a lot of attention lately because of its remarkable properties, even if its high cost and high carbon emissions run counter to the current development trend. To lower the cost and carbon emissions of UHPC, this study develops a multi-objective optimization framework that combines the non-dominated sorting genetic algorithm and 6 different machine learning methods to handle this issue. The key features of UHPC are filtered using the recursive feature elimination approach, and Bayesian optimization and random grid search are employed to optimize the hyperparameters of the machine learning prediction model. The optimal mix ratios of UHPC are found by applying the multi-objective algorithm non-dominated sorting genetic algorithm-III and multi-objective evolutionary algorithm based on adaptive geometric estimation. The results are evaluated by technique for order preference by similarity to ideal solution and validated by experiments. The outcomes demonstrate that the compressive strength and slump flow of UHPC are correctly predicted by the machine learning models. The multi-objective optimization produces Pareto fronts, which illustrate the trade-off between the mix’s compressive strength, slump flow, cost, and environmental sustainability as well as the wide variety of possible solutions. The research contributes to the development of cost-effective and environmentally sustainable UHPC, and aids in robust, intelligent, and sustainable building practices.

Zitieren

Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods. / Wang, Min; Du, Mingfeng; Zhuang, Xiaoying et al.
in: Frontiers of Structural and Civil Engineering, Jahrgang 19, Nr. 1, 01.2025, S. 143–161.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Wang, M, Du, M, Zhuang, X, Lv, H, Wang, C & Zhou, S 2025, 'Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods', Frontiers of Structural and Civil Engineering, Jg. 19, Nr. 1, S. 143–161. https://doi.org/10.1007/s11709-025-1152-0
Wang, M., Du, M., Zhuang, X., Lv, H., Wang, C., & Zhou, S. (2025). Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods. Frontiers of Structural and Civil Engineering, 19(1), 143–161. https://doi.org/10.1007/s11709-025-1152-0
Wang M, Du M, Zhuang X, Lv H, Wang C, Zhou S. Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods. Frontiers of Structural and Civil Engineering. 2025 Jan;19(1):143–161. Epub 2025 Jan 8. doi: 10.1007/s11709-025-1152-0
Wang, Min ; Du, Mingfeng ; Zhuang, Xiaoying et al. / Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods. in: Frontiers of Structural and Civil Engineering. 2025 ; Jahrgang 19, Nr. 1. S. 143–161.
Download
@article{33bffbd35622488cac3e4f15da369189,
title = "Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods",
abstract = "Ultra-high performance concrete (UHPC) has gained a lot of attention lately because of its remarkable properties, even if its high cost and high carbon emissions run counter to the current development trend. To lower the cost and carbon emissions of UHPC, this study develops a multi-objective optimization framework that combines the non-dominated sorting genetic algorithm and 6 different machine learning methods to handle this issue. The key features of UHPC are filtered using the recursive feature elimination approach, and Bayesian optimization and random grid search are employed to optimize the hyperparameters of the machine learning prediction model. The optimal mix ratios of UHPC are found by applying the multi-objective algorithm non-dominated sorting genetic algorithm-III and multi-objective evolutionary algorithm based on adaptive geometric estimation. The results are evaluated by technique for order preference by similarity to ideal solution and validated by experiments. The outcomes demonstrate that the compressive strength and slump flow of UHPC are correctly predicted by the machine learning models. The multi-objective optimization produces Pareto fronts, which illustrate the trade-off between the mix{\textquoteright}s compressive strength, slump flow, cost, and environmental sustainability as well as the wide variety of possible solutions. The research contributes to the development of cost-effective and environmentally sustainable UHPC, and aids in robust, intelligent, and sustainable building practices.",
keywords = "life-cycle assessment, machine learning, multi-objective optimization, ultra-high performance concrete",
author = "Min Wang and Mingfeng Du and Xiaoying Zhuang and Hui Lv and Chong Wang and Shuai Zhou",
note = "Publisher Copyright: {\textcopyright} Higher Education Press 2025.",
year = "2025",
month = jan,
doi = "10.1007/s11709-025-1152-0",
language = "English",
volume = "19",
pages = "143–161",
number = "1",

}

Download

TY - JOUR

T1 - Multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods

AU - Wang, Min

AU - Du, Mingfeng

AU - Zhuang, Xiaoying

AU - Lv, Hui

AU - Wang, Chong

AU - Zhou, Shuai

N1 - Publisher Copyright: © Higher Education Press 2025.

PY - 2025/1

Y1 - 2025/1

N2 - Ultra-high performance concrete (UHPC) has gained a lot of attention lately because of its remarkable properties, even if its high cost and high carbon emissions run counter to the current development trend. To lower the cost and carbon emissions of UHPC, this study develops a multi-objective optimization framework that combines the non-dominated sorting genetic algorithm and 6 different machine learning methods to handle this issue. The key features of UHPC are filtered using the recursive feature elimination approach, and Bayesian optimization and random grid search are employed to optimize the hyperparameters of the machine learning prediction model. The optimal mix ratios of UHPC are found by applying the multi-objective algorithm non-dominated sorting genetic algorithm-III and multi-objective evolutionary algorithm based on adaptive geometric estimation. The results are evaluated by technique for order preference by similarity to ideal solution and validated by experiments. The outcomes demonstrate that the compressive strength and slump flow of UHPC are correctly predicted by the machine learning models. The multi-objective optimization produces Pareto fronts, which illustrate the trade-off between the mix’s compressive strength, slump flow, cost, and environmental sustainability as well as the wide variety of possible solutions. The research contributes to the development of cost-effective and environmentally sustainable UHPC, and aids in robust, intelligent, and sustainable building practices.

AB - Ultra-high performance concrete (UHPC) has gained a lot of attention lately because of its remarkable properties, even if its high cost and high carbon emissions run counter to the current development trend. To lower the cost and carbon emissions of UHPC, this study develops a multi-objective optimization framework that combines the non-dominated sorting genetic algorithm and 6 different machine learning methods to handle this issue. The key features of UHPC are filtered using the recursive feature elimination approach, and Bayesian optimization and random grid search are employed to optimize the hyperparameters of the machine learning prediction model. The optimal mix ratios of UHPC are found by applying the multi-objective algorithm non-dominated sorting genetic algorithm-III and multi-objective evolutionary algorithm based on adaptive geometric estimation. The results are evaluated by technique for order preference by similarity to ideal solution and validated by experiments. The outcomes demonstrate that the compressive strength and slump flow of UHPC are correctly predicted by the machine learning models. The multi-objective optimization produces Pareto fronts, which illustrate the trade-off between the mix’s compressive strength, slump flow, cost, and environmental sustainability as well as the wide variety of possible solutions. The research contributes to the development of cost-effective and environmentally sustainable UHPC, and aids in robust, intelligent, and sustainable building practices.

KW - life-cycle assessment

KW - machine learning

KW - multi-objective optimization

KW - ultra-high performance concrete

UR - http://www.scopus.com/inward/record.url?scp=85214211002&partnerID=8YFLogxK

U2 - 10.1007/s11709-025-1152-0

DO - 10.1007/s11709-025-1152-0

M3 - Article

AN - SCOPUS:85214211002

VL - 19

SP - 143

EP - 161

JO - Frontiers of Structural and Civil Engineering

JF - Frontiers of Structural and Civil Engineering

SN - 2095-2430

IS - 1

ER -