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Computers and Concrete Volume 23, Number 4, April 2019 , pages 255-265 DOI: https://doi.org/10.12989/cac.2019.23.4.255 |
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Knowledge-based learning for modeling concrete compressive strength using genetic programming |
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Hsing-Chih Tsai and Min-Chih Liao
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| Abstract | ||
| The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams\' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams\' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes. | ||
| Key Words | ||
| genetic programming; concrete compressive strength; design codes; functional mapping | ||
| Address | ||
| Hsing-Chih Tsai and Min-Chih Liao: Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei, Taiwan, R.O.C. | ||