Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 3Citation - Scopus: 5Helenistik Kulelerde Yanal Yüke İlişkin Düzlem İçi ve Düzlem Dışı Duvar Davranışının Duvar Profilleri ve Açıklık Düzenlerine Bağlı Olarak İncelenmesi(Gazi Üniversitesi, 2020) Gençer, Funda; Turan, Mine; Aktaş, EnginDry masonry wall profiles constructed without using bonding material between blocks are encountered in different building types dated to Greek, Hellenistic and Roman periods. Depending on development of earthquake experiences, dry masonry wall profiles vary in terms of wall thickness, number of layers, relationship of layers and size, form, organization and relationship of blocks. In this study, the construction details increasing the structural strength in watch towers constructed in Hellenistic Period are aimed to be determined. This study is limited with investigation of effect of wall profiles and opening organizations on in-plane and out-of-plane wall behaviors under lateral loading. Primarily, wall profiles and opening types of dry masonry towers in ancient Caria, Pamphylia and Cilicia regions, that have not lost authenticity and integrity or had information about their authentic form, were documented. By using gathered data, hypothetic towers with different wall profiles and opening types are designed. To determine in-plane and out-of-plane wall behavior of hypothetic towers, simulation of quasi-static tilt analysis based on equilibrium were carried out, and form changes at walls and collapse angles of towers were identified. Wall profile and opening properties effecting on structural strength under lateral loading were determinedd.Book Part Numerical Modeling of Transport Processes at Hillslope Scale Accounting for Local Physical Features(Nova Science Publishers, Inc., 2011) Tayfur, GökmenHillslope is the basic unit of a watershed. Typical hillslopes may have a size of 1000 m long and 500 m wide. For watershed modeling, it is essential to accurately describe the illslope-scale processes of flow, erosion and sediment transport, and solute transport. Although these processes are usually considered in experimental studies and theoretical subjects, the existing numerical models that are designed to simulate transport processes at hillslope scale rarely take microtopographic variations into account. Instead, those models assume constant slope, roughness, and infiltration rate for a given basic computational unit (i.e., hillslope). As a result, effects of microtopographic features (e.g., rills) on the aforementioned processes cannot be reflected in modeling results. However, the effects could be important because rill and sheet flows exhibit distinctly different dynamics that influence the transport processes. The objective of this chapter is to review the numerical studies for investigating the transport processes at hillslope scale. The chapter focuses particularly on the modeling efforts with the effects of microtopographic features on the dynamics of the transport processes incorporated.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; Singh, Vijay P.This 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.Article Citation - WoS: 27Citation - Scopus: 101Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network(American Society of Civil Engineers (ASCE), 2005) Tayfur, G; Singh, VPAn artificial neural network (ANN) model was developed to predict the longitudinal dispersion coefficient in natural streams and rivers. The hydraulic variables [flow discharge (Q), flow depth (H), flow velocity (U), shear velocity (u*), and relative shear velocity (U/u*)] and geometric characteristics [channel width (B), channel sinuosity (sigma), and channel shape parameter (beta)] constituted inputs to the ANN model, whereas the dispersion coefficient (K-x) was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and dispersion coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the dispersion coefficient. The dispersion coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted using several equations that have been suggested in the literature and it was found that the ANN model was superior in predicting the dispersion coefficient. The results of sensitivity analysis indicated that the Q data alone would be sufficient for predicting more frequently occurring low values of the dispersion coefficient (K-x < 100 m(2)/s). For narrower channels (B/H < 50) using only U/u* data would be sufficient to predict the coefficient. If beta and sigma were used along with the flow variables, the prediction capability of the ANN model would be significantly improved.