Enhancing mix proportion design of low carbon concrete for shield segment using a combination of Bayesian optimization-NGBoost and NSGA-III algorithm

Abstract

The demand for segment concrete increases rapidly with the expansion of urban rail transit and underground space, which may lead to carbon emissions (CE). In the production of segment concrete, using supplement cementitious materials (SCM) can be used to reduce CE instead of cement. However, SCM contents are usually determined by many orthogonal experiments, which are often time-consuming and only consider the limited experimental variables. Therefore, a novel hybrid intelligent algorithm combining Bayesian optimization (BO), natural gradient boosting (NGBoost) and non-dominated sorting genetic algorithm (NSGA)-III is proposed, which overcomes these disadvantages. Firstly, NGBoost model after hyperparameter optimization using BO is used to establish a nonlinear mapping relationship between segment concrete mix proportion and performance, which was used as the fitness function of the non-dominated sorting genetic algorithm (NSGA-III). Secondly, the optimal Pareto solutions were obtained through NSGA-III, and the reasonable range of segment concrete mixing proportion was obtained. Finally, a segment concrete production case was used to verify the effectiveness of the proposed algorithm. The results showed that the proposed hybrid algorithm combining proposed scheme decision strategy can obtain an optimal design scheme for concrete mix proportion. In addition, the comprehensive optimization rate of the obtained optimal scheme compared to the experimental optimal group was 11.5%. Moreover, the amount of phosphorous slag (PS) and granulated blast furnace slag (GBFS) in the optimal scheme was 26%, with a mass ratio of 1:2. Compared to the experimental optimal group, the optimal scheme can reduce the cost and CE per cubic meter by 31.64 yuan and 31.04 kg, respectively. Furthermore, the hybrid algorithm proposed can accurately obtains an optimal amount of SCM, which could be used as a supporting tool to reduce the production cost and CE of segment concrete. Overall, this research contributes to combining machine learning algorithms with actual production processes, which overcomes the data limitations of traditional experiments. It also provides a new intelligent approach and reference cases for the low-carbon design and sustainable development of building materials such as concrete.

Publication DOI: https://doi.org/10.1016/j.jclepro.2024.142746
Divisions: College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
Funding Information: This work is financially supported by the Natural Science Foundation of Hubei Province (Grant No. 2023AFC026), the Social ScienceFoundation of Hubei Province (Grant No. 22ZD004) and the National Key R&D Program of China (Grant No. 2021YFF0501003).
Additional Information: Copyright © 2024 Elsevier Ltd. This accepted manuscript version is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License [https://creativecommons.org/licenses/by-nc-nd/4.0/].
Uncontrolled Keywords: BO-NGBoost-NSGA-III,Carbon emission,Concrete performance,Low-carbon concrete,Mix design,Multi-objective optimization,Renewable Energy, Sustainability and the Environment,General Environmental Science,Strategy and Management,Industrial and Manufacturing Engineering
Publication ISSN: 1879-1786
Data Access Statement: Data will be made available on request.
Last Modified: 09 Dec 2024 09:14
Date Deposited: 07 Jun 2024 14:22
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 959652624021942 (Publisher URL)
PURE Output Type: Article
Published Date: 2024-08-01
Published Online Date: 2024-06-03
Accepted Date: 2024-05-29
Authors: Cao, Yuan
Su, Feiming
Antwi Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)
Lei, Jian
Wu, Xianguo
Liu, Yang

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 3 June 2025.

License: Creative Commons Attribution Non-commercial No Derivatives


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