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Predicting high-strength concrete parameters using weighted genetic programming

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Abstract

Genetic programming (GP) is an evolutionary algorithm-based methodology that employs a binary tree topology with optimized functional operators. This study introduced weight coefficients to each GP linkage in a tree in order to create a new weighted genetic programming (WGP) approach. Two distinct advantages of the proposed WGP include (1) balancing the influences of the two front input branches and (2) incorporating weights throughout generated formulas. Resulting formulas contain a certain quantity of optimized functions and weights. Genetic algorithms are employed to accomplish WGP optimization of function selection and proper weighting tasks. Case studies presented herein highlight a high-strength concrete reference study. Results showed that the proposed WGP not only improves GP in terms of introduced weight coefficients, but also provides both accurate results and formula outputs.

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Authors and Affiliations

  1. Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd, Taipei, 106, Taiwan, ROC

    Hsing-Chih Tsai & Yong-Huang Lin

  2. Construction Occupational Health and Safety Center, NTUST, #43, Sec. 4, Keelung Rd, Taipei, 106, Taiwan, ROC

    Yong-Huang Lin

  3. Ecological and Hazard Mitigation Engineering Research Center, NTUST, #43, Sec. 4, Keelung Rd, Taipei, 106, Taiwan, ROC

    Yong-Huang Lin

Authors
  1. Hsing-Chih Tsai
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  2. Yong-Huang Lin
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Correspondence to Hsing-Chih Tsai.

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Tsai, HC., Lin, YH. Predicting high-strength concrete parameters using weighted genetic programming. Engineering with Computers 27, 347–355 (2011). https://doi.org/10.1007/s00366-011-0208-z

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  • DOI: https://doi.org/10.1007/s00366-011-0208-z

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