TY - JOUR
T1 - An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming
AU - Castelli, Mauro
AU - Trujillo, Leonardo
AU - Gonçalves, Ivo
AU - Popovič, Aleš
N1 - Castelli, M., Trujillo, L., Gonçalves, I., & Popovič, A. (2017). An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming. Computers and Concrete, 19(6), 651-658. https://doi.org/10.12989/cac.2017.19.6.651
PY - 2017/6/1
Y1 - 2017/6/1
N2 - High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.
AB - High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.
KW - Concrete strength
KW - Genetic programming
KW - High performance concrete
KW - Local search
KW - Semantics
UR - http://www.scopus.com/inward/record.url?scp=85021177863&partnerID=8YFLogxK
U2 - 10.12989/cac.2017.19.6.651
DO - 10.12989/cac.2017.19.6.651
M3 - Article
AN - SCOPUS:85021177863
SN - 1598-8198
VL - 19
SP - 651
EP - 658
JO - Computers and Concrete
JF - Computers and Concrete
IS - 6
ER -