Civil Engineering / İnşaat Mühendisliği

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

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Subject: search.filters.subject.Artificial neural networksWoS Q: search.filters.wosQuartile.Q4

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Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Estimation of Mechanical Properties of Limestone Using Regression Analyses and Ann
    (Foundation Cement, Lime, Concrete, 2012) Teomete, Egemen; Tayfur, Gökmen; Aktaş, Engin
    Estimation of mechanical properties of rocks is important for researchers and field engineers working in cement and concrete industry. Limestone is used in cement production. In this study, Schmidt hammer, ultrasonic pulse velocity, porosity, uniaxial compression and indirect tension tests were conducted on limestone obtained from a historical structure. Regression analyses were used to develop models relating mechanical properties of limestone. Artificial Neural Network (ANN) was performed to determine the mechanical properties. The performance of regression models and ANN were compared by existing models in the literature. The results showed that the regression models and ANN yield satisfactory performance with minimum error. The regression models between tensile strength and wave velocity, tensile strength and porosity, wave velocity and porosity have been developed for the first time in literature. The ANN is used for the first time to estimate the mechanical properties of limestone. The use of separate training and testing sets in the regression analyses of mechanical properties of limestone is conducted for the first time. The models developed in this study can be used by researchers and field engineers to relate the mechanical properties of limestone.
  • Article
    Citation - WoS: 11
    Citation - Scopus: 12
    Passenger Flows Estimation of Light Rail Transit (lrt) System in Izmir, Turkey Using Multiple Regression and Ann Methods
    (Faculty of Transport and Traffic Sciences, University of Zagreb, 2012) Özuysal, Mustafa; Tayfur, Gökmen; Tanyel, Serhan
    Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.