Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models


ÖZCAN G., KOÇAK Y., Gulbandilar E.

COMPUTERS AND CONCRETE, vol.19, no.3, pp.275-282, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 3
  • Publication Date: 2017
  • Doi Number: 10.12989/cac.2017.19.3.275
  • Journal Name: COMPUTERS AND CONCRETE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.275-282
  • Keywords: blast furnace slag, waste tire rubber powder, compressive strength, random forest, ada boost, SVM, Bayes classifier models, BLAST-FURNACE SLAG, ARTIFICIAL NEURAL-NETWORK, WASTE TIRE RUBBER, MECHANICAL-PROPERTIES, FLY-ASH, PORTLAND-CEMENT, MARBLE DUST, CONCRETE, PREDICTION, FUZZY
  • Bursa Uludag University Affiliated: Yes

Abstract

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.