Evaluation of the effect of aggregate on concrete permeability using grey correlation analysis and ANN

DOI QR코드

DOI QR Code

Kong, Lijuan;Chen, Xiaoyu;Du, Yuanbo

  • 투고 : 2015.08.01
  • 심사 : 2016.02.04
  • 발행 : 2016.05.25

초록

In this study, the influence of coarse aggregate size and type on chloride penetration of concrete was investigated, and the grey correlation analysis was applied to find the key influencing factor. Furthermore, the proposed 6-10-1 artificial neural network (ANN) model was constructed, and performed under the MATLAB program. Training, testing and validation of the model stages were performed using 81 experiment data sets. The results show that the aggregate type has less effect on the concrete permeability, compared with the size effect. For concrete with a lower w/b, the coarse aggregate with a larger particle size should be chose, however, for concrete with a higher w/c, the aggregate with a grading of 5-20 mm is preferred, too large or too small aggregates are adverse to concrete chloride diffusivity. A new idea for the optimum selection of aggregate to prepare concrete with a low penetration is provided. Moreover, the ANN model predicted values are compared with actual test results, and the average relative error of prediction is found to be 5.62%. ANN procedure provides guidelines to select appropriate coarse aggregate for required chloride penetration of concrete and will reduce number of trial and error, save cost and time.

키워드

coarse aggregate;permeability;concrete;grey correlation analysis;artificial neural network

참고문헌

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피인용 문헌

  1. 1. Multiscale analysis of the correlation of processing parameters on viscidity of composites fabricated by automated fiber placement vol.4, pp.10, 2017, doi:10.12989/cac.2016.17.5.613

과제정보

연구 과제 주관 기관 : Natural Science Foundation of China, Natural Science Foundation of Hebei Province of China