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B. Chen^1,2, H. Zhou³, D.C. Xia⁴ and S.H. Huang^1,2

¹Zhejiang University of Water Resources and Electric Power, Hangzhou, China
²Key Laboratory for Technology in Rural Water Management of Zhejiang Province, Hangzhou, China
³Zhejiang Institute of Hydraulics & Estuary, Hangzhou, China
⁴China No. 12 Water Conservancy and Water Power Engineering Bureau, Hangzhou, China

doi:10.4203/ccc.2.6.4

B. Chen, H. Zhou, D.C. Xia, S.H. Huang, "Big-data based concrete mix proportion optimization and application development", in B.H.V. Topping, P. Iványi, (Editors), "Proceedings of the Eleventh International Conference on Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 2, Paper 6.4, 2022, doi:10.4203/ccc.2.6.4

Keywords: concrete; mixture optimization, artificial neural network, support vector machine, particle swarm optimization, artificial bee colony algorithm.

Abstract

Based on big data technology, according to the nonlinear relationship between mix proportion and performance of concrete with multi cementitious materials, the mix proportion optimization method of concrete with multi cementitious materials is proposed. Firstly, 1443 sets of mixed samples were collected for correlation analysis, and the prediction abilities of linear regression, BP artificial neural network and support vector machine (SVM) were compared. The prediction model of concrete strength and workability based on support vector machine was selected. Secondly, the nonlinear optimization model of concrete mix proportion is established by using particle swarm optimization (PSO) algorithm and artificial bee colony algorithm (ABC). Finally, a series of concrete mix proportions are designed and tested to verify the effectiveness of the method. Furthermore, the concrete quality and cost control system (Compos) is developed to facilitate the application of this method.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 2 PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping and P. Iványi Paper 6.4 Big-data based concrete mix proportion optimization and application development B. Chen^1,2, H. Zhou³, D.C. Xia⁴ and S.H. Huang^1,2 ¹Zhejiang University of Water Resources and Electric Power, Hangzhou, China ²Key Laboratory for Technology in Rural Water Management of Zhejiang Province, Hangzhou, China ³Zhejiang Institute of Hydraulics & Estuary, Hangzhou, China ⁴China No. 12 Water Conservancy and Water Power Engineering Bureau, Hangzhou, China doi:10.4203/ccc.2.6.4 Full Bibliographic Reference for this paper B. Chen, H. Zhou, D.C. Xia, S.H. Huang, "Big-data based concrete mix proportion optimization and application development", in B.H.V. Topping, P. Iványi, (Editors), "Proceedings of the Eleventh International Conference on Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 2, Paper 6.4, 2022, doi:10.4203/ccc.2.6.4 Keywords: concrete; mixture optimization, artificial neural network, support vector machine, particle swarm optimization, artificial bee colony algorithm. Abstract Based on big data technology, according to the nonlinear relationship between mix proportion and performance of concrete with multi cementitious materials, the mix proportion optimization method of concrete with multi cementitious materials is proposed. Firstly, 1443 sets of mixed samples were collected for correlation analysis, and the prediction abilities of linear regression, BP artificial neural network and support vector machine (SVM) were compared. The prediction model of concrete strength and workability based on support vector machine was selected. Secondly, the nonlinear optimization model of concrete mix proportion is established by using particle swarm optimization (PSO) algorithm and artificial bee colony algorithm (ABC). Finally, a series of concrete mix proportions are designed and tested to verify the effectiveness of the method. Furthermore, the concrete quality and cost control system (Compos) is developed to facilitate the application of this method. download the full-text of this paper (PDF, 14 pages, 800 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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