Optimized Design of Low-Carbon Mix Ratio for Non-Dominated Sorting Genetic Algorithm II Concrete Based on Genetic Algorithm-Improved Back Propagation.

Fan Zhang, Bo Wen, Ditao Niu, Anbang Li, Bingbing Guo

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

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1 citing paper in PubMed.

Frontiers in bioengineering and biotechnology · 2025
Article

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5 · Who and what money

Authors and funding

5 authors.

Fan ZhangDepartment of School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Bo WenDepartment of School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.ORCID 0000-0002-5738-4450

Ditao NiuDepartment of School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Anbang LiDepartment of School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.ORCID 0000-0001-5531-5155

Bingbing GuoDepartment of School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Funding

National Natural Science Foundation of China No. 52341803

6 · The paper itself

Abstract

In order to achieve low-carbon optimization in the intelligent mix ratio design of concrete materials, this work first constructs a concrete mix ratio database and performs a statistical characteristics analysis. Secondly, it employs a standard back propagation (BP) and a genetic algorithm-improved BP (GA-BP) to predict the concrete mix ratio. The NSGA-II algorithm is then used to optimize the mix ratio. Finally, the method's accuracy is validated through experiments. The study's results indicate that the statistical characteristics of the concrete mix ratio data show a wide distribution range and good representativeness. Compared to the standard BP, the fitting accuracies of each GA-BP set are improved by 4.9%, 0.3%, 16.7%, and 4.6%, respectively. According to the Fast Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization for meeting C50 concrete strength requirements, the optimal concrete mix ratio is as follows: cement 331.3 kg/m

Indexed as

BPGA-BPintensity predictionmachine learningNSGA-II optimization

Identifiers

PMID39203254

DOI10.3390/ma17164077

PMCPMC11356359

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.