Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Department: search.filters.department.İzmir Institute of Technology. Civil EngineeringSubject: search.filters.subject.Artificial intelligence

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  • Article
    Citation - WoS: 31
    Citation - Scopus: 34
    Data Pre-Post Processing Methods in Ai-Based Modeling of Seepage Through Earthen Dams
    (Elsevier Ltd., 2019) Sharghi, Elnaz; Nourani, Vahid; Behfar, Nazanin; Tayfur, Gökmen
    In this paper, seepage of Sattarkhan earthen dam in northwest Iran was simulated using various artificial intelligence (AI) models (e.g., Feed forward neural network, Adaptive neural fuzzy inference system and Support vector regression) and linear ARIMA model based on different input combinations. Both jittering pre-processing and ensembling post-processing methods were also used in order to enhance the performance of the used AI-based data driven methods. For this purpose, various jittered datasets were produced by imposing noises (at different levels) to the original time series to enlarge the training data sample space. Further, three techniques of simple linear, weighted linear and nonlinear neural averaging were considered for pre-post processing purpose. The obtained results indicated that using both jittering and ensembling (especially neural ensemble) enhanced the modeling performance by almost 30% in the testing phase. (C) 2019 Elsevier Ltd. All rights reserved.
  • 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.