TY - JOUR
T1 - Modeling the compressive strength of high-strength concrete
T2 - An extreme learning approach
AU - Al-Shamiri, Abobakr Khalil
AU - Kim, Joong Hoon
AU - Yuan, Tian Feng
AU - Yoon, Young Soo
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) under a grant funded by the Korean government ( MSIP ) (NRF-2016R1A2A1A05005306); and the Industrial Strategic Technology Development Program (10066488) funded by the Ministry of Trade, Industry & Energy (MI, Korea)
Publisher Copyright:
© 2019
PY - 2019/5/30
Y1 - 2019/5/30
N2 - Compressive strength is a major and significant mechanical property of concrete which is considered as one of the important parameters in many design codes and standards. Early and accurate estimation of it can save in time and cost. In this study, extreme learning machine (ELM) was used to predict the compressive strength of high-strength concrete (HSC). ELM is a relatively new method for training artificial neural networks (ANN), showing good generalization performance and fast learning speed in many regression applications. ELM model was developed using 324 data records obtained from laboratory experiments. The compressive strength was modeled as a function of five input variables: water, cement, fine aggregate, coarse aggregate, and superplasticizer. The performance of the developed ELM model was compared with that of ANN model trained by using back propagation (BP) algorithm. The simulation results show that the proposed ELM model has a strong potential for predicting the compressive strength of HSC.
AB - Compressive strength is a major and significant mechanical property of concrete which is considered as one of the important parameters in many design codes and standards. Early and accurate estimation of it can save in time and cost. In this study, extreme learning machine (ELM) was used to predict the compressive strength of high-strength concrete (HSC). ELM is a relatively new method for training artificial neural networks (ANN), showing good generalization performance and fast learning speed in many regression applications. ELM model was developed using 324 data records obtained from laboratory experiments. The compressive strength was modeled as a function of five input variables: water, cement, fine aggregate, coarse aggregate, and superplasticizer. The performance of the developed ELM model was compared with that of ANN model trained by using back propagation (BP) algorithm. The simulation results show that the proposed ELM model has a strong potential for predicting the compressive strength of HSC.
KW - Artificial neural network
KW - Compressive strength
KW - Extreme learning machine
KW - High-strength concrete
KW - Regression
UR - http://www.scopus.com/inward/record.url?scp=85062350517&partnerID=8YFLogxK
U2 - 10.1016/j.conbuildmat.2019.02.165
DO - 10.1016/j.conbuildmat.2019.02.165
M3 - Article
AN - SCOPUS:85062350517
SN - 0950-0618
VL - 208
SP - 204
EP - 219
JO - Construction and Building Materials
JF - Construction and Building Materials
ER -