Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 5Citation - Scopus: 5A Novel Risk Analysis Approach for Occupational Safety Using Bayesian Network and Interval Type-2 Fuzzy Sets: the Case of Underground Mining(IOS Press BV, 2022) Yaşlı, Fatma; Yaşlı, Fatma; Bolat, Bersam; 01.01. Units Affiliated to the Rectorate; 01. Izmir Institute of TechnologyOccupational safety problems are no longer acceptable for any industrial environment. Lack of comprehensive and reliable evaluations for occupational safety causes many undesired events and harm to employees during the industrial process. In this study, it is aimed to develop an applicable risk analysis methodology for evaluating the undesired occupational events that occurred in the multi-process system where no historical accident records. The difficulty in obtaining and analyzing the data required for the determination of the occupational safety risks especially in the manually executed processes has been overcome with the Bayesian Network and interval type-2 fuzzy sets by using the expert judgments. While BN enables to development of a comprehensive reasoning approach about the occurrence of the events, interval type-2 fuzzy sets better represent the ambiguity in the judgments by covering the uncertainty in a wider mathematical range with less computational effort according to other fuzzy sets. During multi-processes in industrial activity, various occupational undesired events may occur, including rare events with very serious consequences or frequent events with very low severity consequences. To able to consider all kinds of events occurring in an industrial environment from a holistic risk perspective, a novel fuzzy scale for specifying the probability and consequence of the events are proposed by the interval type-2 fuzzy numbers. Therefore, all undesired events regardless the probability and consequence which may occur during the multi-processes in a system and the main root causes of these events can be observed within the proposed methodology. A case study is used to emphasize the effect of the proposed methodology for risk analysis of occupational safety in underground mining The results have indicated that occupational safety education is the most contributing factor to occurring the undesired occupational events in underground mining We believe that this study could help evaluate the safety risk of the multi-process systems comprehensively and holistically and proposing strategic planning for mitigating the occupational safety risks.Article Citation - WoS: 54Citation - Scopus: 69Strength 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; Tayfur, Gökmen; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyHigh-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.Article Citation - WoS: 37Citation - Scopus: 49Predicting Suspended Sediment Loads and Missing Data for Gediz River, Turkey(American Society of Civil Engineers (ASCE), 2009) Ülke, Aslı; Tayfur, Gökmen; Özkul, Sevinç; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyPrediction of suspended sediment load (SSL) is important for water resources quantity and quality studies. The SSL of a stream is generally determined by direct measurement of the suspended sediment concentration or by employing sediment rating curve method. Although direct measurement is the most reliable method, it is very expensive, time consuming, and, in many instances, problematic for inaccessible sections, especially during floods. On the other hand, measuring precipitation and flow discharge is relatively easier and hence, there are more rain and flow gauging stations than SSL gauging stations in Turkey. Furthermore, due to its cost, measurements of SSL are carried out in longer periods compared to precipitation and flow measurements. Although daily precipitation and flow measurements are available for most of the Turkish river basins, at best semimonthly measurements are available for SSL. As such, it is essential to predict SSL from precipitation and flow data and to fill the gap for the missing data records. This study employed artificial intelligence methods of artificial neural networks (ANN) and neurofuzzy inference system, the sediment rating curve method, multilinear regression, and multinonlinear regression methods for this purpose. The comparative analysis of the results showed that the artificial intelligence methods have superiority over the other methods for predicting semimonthly suspended sediment loads. The ANN using conjugate gradient optimization method showed the best performance among the proposed models. It also satisfactorily generated daily SSL data for the missing period record of Gediz River, Turkey.Annotation Citation - WoS: 1Citation - Scopus: 1Closure To "ann and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff" by Gokmen Tayfur and Vijay P. Singh(American Society of Civil Engineers (ASCE), 2008) Tayfur, Gökmen; Tayfur, Gökmen; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyWe would like to thank Dr. Wong for his interest in and thoughts on our analysis of runoff hydrograph prediction and the goodnessof-fit measurement. We agree that visual comparison of simulated and measured hydrographs is an important indicator for assessing the performance of models. Visual inspection allows one to see intricate differences between hydrographs.Article Citation - WoS: 103Citation - Scopus: 126Ann and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff(American Society of Civil Engineers (ASCE), 2006) Tayfur, Gökmen; Tayfur, Gökmen; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThis study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44 km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.