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author:

Ji, Tao (Ji, Tao.) [1] | Yang, Yu (Yang, Yu.) [2] | Fu, Mao-Yuan (Fu, Mao-Yuan.) [3] | Chen, Bao-Chun (Chen, Bao-Chun.) [4] | Wu, Hwai-Chung (Wu, Hwai-Chung.) [5]

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EI

Abstract:

An optimum mixture proportion design method of reactive powder concrete (RPC) based on an artificial neural network (ANN) and harmony search (HS) algorithm was developed. ANNs were adopted to establish the relationship between design parameters (water-binder ratio, silica fume content, sand-binder ratio, and steel fiber content) and properties (compressive strength under standard curing and autoclaved curing, splitting tensile strength under autoclaved curing, and slump) of RPC, and the HS algorithm was used to design and optimize RPC mixture proportions with the objective criterion of minimum cost while meeting all property requirements. The proposed method can consider the influence of curing regimes, and its reliability was verified by experiment data. Copyright © 2017, American Concrete Institute. All rights reserved.

Keyword:

Compressive strength Concrete mixtures Costs Curing Design Mixtures Neural networks Silica fume Steel fibers Tensile strength

Community:

  • [ 1 ] [Ji, Tao]College of Civil Engineering, Fuzhou University, Fuzhou; Fujian Province, China
  • [ 2 ] [Yang, Yu]College of Civil Engineering, Fuzhou University, Fuzhou; Fujian Province, China
  • [ 3 ] [Fu, Mao-Yuan]College of Civil Engineering, Fuzhou University, Fuzhou; Fujian Province, China
  • [ 4 ] [Chen, Bao-Chun]College of Civil Engineering, Fuzhou University, Fuzhou; Fujian Province, China
  • [ 5 ] [Wu, Hwai-Chung]College of Civil Engineering, Fuzhou University, Fuzhou; Fujian Province, China
  • [ 6 ] [Wu, Hwai-Chung]Department of Civil and Environmental Engineering, Wayne State University, Detroit; MI, United States

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Source :

ACI Materials Journal

ISSN: 0889-325X

Year: 2017

Issue: 1

Volume: 114

Page: 41-47

1 . 2 5 2

JCR@2017

1 . 9 0 0

JCR@2023

ESI HC Threshold:306

JCR Journal Grade:3

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 13

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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