American Society of Civil Engineers


Modeling Strength of High-Performance Concrete Using an Improved Grammatical Evolution Combined with Macrogenetic Algorithm


by Li Chen, (corresponding author), (Professor, Dept. of Civil Engineering and Engineering Informatics, Chung Hua Univ., Hsinchu, Taiwan 30012, Republic of China E-mail: lichen@chu.edu.tw) and Tai-Sheng Wang, (Graduate student, Dept. of Civil Engineering and Engineering Informatics, Chung Hua Univ., Hsinchu, Taiwan 30012, Republic of China)

Journal of Computing in Civil Engineering, Vol. 24, No. 3, May/June 2010, pp. 281-288, (doi:  http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000031)

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Document type: Journal Paper
Abstract: The main purpose of this paper is to propose an incorporating improved grammatical evolution (GE) into the genetic algorithm (GA), called GEGA, to estimate the compressive strength of high-performance concrete (HPC). The GE is a recently developed evolutionary programming type system. It is used to automatically discover complex relationships between significant factors and the strength of HPC. This method is transparent and can enhance our understanding of the mechanisms of HPC strength. A GA was used afterward with GE to optimize the appropriate function type and its associated coefficients. In addition, macroevolution algorithm was processed to improve search efficiency during the GA optimization procedure. The case study includes over 1,000 examples of HPC for which experimental data were available. This novel model, GEGA, can obtain a highly nonlinear mathematical equation for predicting the HPC’s compressive strength. The results show that GEGA has lower estimating errors, which outperforms another evolutionary strategy called genetic programming and two popular types of traditional multiple regression analysis.


ASCE Subject Headings:
Algorithms
Compressive strength
High-strength concrete
Computer programming
Regression analysis

Author Keywords:
Grammatical evolution
Genetic algorithm
Compressive strength
High-performance concrete
Genetic programming
Regression analysis