Modeling the compressive strength of high-strength concrete

An extreme learning approach

Abobakr Khalil Al-Shamiri, Joong Hoon Kim, Tian Feng Yuan, Young Soo Yoon

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)204-219
Number of pages16
JournalConstruction and Building Materials
Volume208
DOIs
Publication statusPublished - 2019 May 30

Fingerprint

Compressive strength
Learning systems
Concretes
Neural networks
Backpropagation algorithms
Cements
Mechanical properties
Water
Costs
Experiments

Keywords

  • Artificial neural network
  • Compressive strength
  • Extreme learning machine
  • High-strength concrete
  • Regression

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Materials Science(all)

Cite this

Modeling the compressive strength of high-strength concrete : An extreme learning approach. / Al-Shamiri, Abobakr Khalil; Kim, Joong Hoon; Yuan, Tian Feng; Yoon, Young Soo.

In: Construction and Building Materials, Vol. 208, 30.05.2019, p. 204-219.

Research output: Contribution to journalArticle

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