Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Author: search.filters.author.Erdem, Tahir KemalSubject: search.filters.subject.Compressive strength

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  • Article
    Citation - WoS: 70
    Citation - Scopus: 86
    Permeability Properties of Self-Consolidating Concrete Containing Various Supplementary Cementitious Materials
    (Elsevier Ltd., 2015) Saleh Ahari, Reza; Erdem, Tahir Kemal; Ramyar, Kambiz
    In this study, permeability properties of 17 self-consolidating concrete (SCC) mixtures containing various supplementary cementitious materials (SCM) were investigated by different experimental approaches. The effects of SCM type and content on the compressive strength, rapid chloride ion permeability (RCPT), water penetration depth, water absorption and sorptivity were studied. For these purposes, various amounts of silica fume (SF), metakaolin (MK), Class F fly ash (FAF), Class C fly ash (FAC) and granulated blast-furnace slag (BFS) were utilized in binary, ternary, and quaternary cementitious blends. Results showed that partial replacement of PC by SCM increased the compressive strength of control mixtures at 28 and 90 days (except for FAF at 28 days). Mixtures containing MK presented a better performance compared to other SCM at 7 days. The utilization of SCM reduced the RCPT results of almost all mixtures compared to the control mixtures and the reduction was more significant with an increase in the SCM content. All of the mixtures containing SCM had lower penetration depths when compared to reference mixtures at 28 and 90 days. Good correlations were established between the percentage of permeable voids and water absorption. Moreover, there was an inverse but almost linear relationship between permeable voids content and compressive strength of the mixtures.
  • Article
    Citation - WoS: 54
    Citation - Scopus: 69
    Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks
    (American Society of Civil Engineers (ASCE), 2014) Tayfur, Gökmen; Erdem, Tahir Kemal; Kırca, Önder
    High-strength concretes (HSC) were prepared with five different binder contents, each of which had several silica fume (SF) ratios (0-15%). The compressive strength was determined at 3, 7, and 28 days, resulting in a total of 60 sets of data. In a fuzzy logic (FL) algorithm, three input variables (SF content, binder content, and age) and the output variable (compressive strength) were fuzzified using triangular membership functions. A total of 24 fuzzy rules were inferred from 60% of the data. Moreover, the FL model was tested against an artificial neural networks (ANNs) model. The results show that FL can successfully be applied to predict the compressive strength of HSC. Three input variables were sufficient to obtain accurate results. The operators used in constructing the FL model were found to be appropriate for compressive strength prediction. The performance of FL was comparable to that of ANN. The extrapolation capability of FL and ANNs were found to be satisfactory.