Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 3Citation - Scopus: 3Developing Cation Exchange Capacity and Soil Index Properties Relationships Using a Neuro-Fuzzy Approach(Springer Verlag, 2014) Pulat, Hasan Fırat; Tayfur, Gökmen; Yükselen Aksoy, YelizArtificial intelligence methods are employed to predict cation exchange capacity (CEC) from five different soil index properties, namely specific surface area (SSA), liquid limit, plasticity index, activity (ACT), and clay fraction (CF). Artificial neural networks (ANNs) analyses were first employed to determine the most related index parameters with cation exchange capacity. For this purpose, 40 datasets were employed to train the network and 10 datasets were used to test it. The ANN analyses were conducted with 15 different input vector combinations using same datasets. As a result of this investigation, the ANN analyses revealed that SSA and ACT are the most effective parameters on the CEC. Next, based upon these most effective input parameters, the fuzzy logic (FL) model was developed for the CEC. In the developed FL model, triangular membership functions were employed for both the input (SSA and ACT) variables and the output variable (CEC). A total of nine Mamdani fuzzy rules were deduced from the datasets, used for the training of the ANN model. Minimization (min) inferencing, maximum (max) composition, and centroid defuzzification methods are employed for the constructed FL model. The developed FL model was then tested against the remaining datasets, which were also used for testing the ANN model. The prediction results are satisfactory with a determination coefficient, R2 = 0.94 and mean absolute error, (MAE) = 7.1.Article Citation - WoS: 3Citation - Scopus: 5Classifications for Planimetric Efficiency of Nursing Unit Floors(Middle East Technical University, 2012) Kazanasmaz, Zehra Tuğçe; Tayfur, GökmenBu çalışma, Türkiye’deki 15 Devlet Hastanesine ait ‘Hasta Bakım Üniteleri’ verilerini kullanarak, mimarlık alanında oldukça yeni olan Bulanık Mantık metodu ile verimlilik tahmini yapmakta; bu çerçevede bulanık mantık algoritması geliştirerek, Türkiye’de örnek olarak seçilen kamu hastanelerinin plan (planimetric) tasarım verimliliği için sınıflandırmalar geliştirmeye çalışmaktadır. Hasta bakım ve tedavi ünitelerinin kat planlarından hasta kullanım alanları ve dolaşım alanları elde edilerek bulanık mantık modeli alt kümeleri için üyelik fonksiyonları oluşturulmuştur. ‘Mamdanni’ kural sistemi, kuralların ağırlıklarını hesaplamada ‘min’ fonksiyonu, ve ‘max’ kompozisyonu ve ‘centroid’ metodu da bulanık işlemcisi için kullanılmıştır. Girdi değişkenleri olarak hasta kullanım alanları ve dolaşım alanları modellenmiştir. Girdi değişkenleri ile çıktı değişkeni olan tasarım verimliliği arasındaki ilişkiler bulanık mantık kuralları ile ortaya çıkarılmıştır. Varolan hasta bakım ünitelerini incelemek için, verimlilik çıktı değerleri modelden elde edilmiştir. Genel tasarım normları, tasarım ölçütleri ve önceki çalışmalar ışığında ve de bu model aracılığıyla verimlilik sınıfları oluşturulmuştur. Modelde test edilen 15 hastane kat planından altısının düşük verimli sınıf içinde, dokuzunun ise orta verimli sınıf içinde olduğu görülmüştür. Hiçbiri güncel standartlara ve gereksinimlere uygun değildir. Bu çalışmada elde edilen modelin faydası, verimlilik sınıflarının sınır değerlerini belirleme yeteneğinde olmasıdır. Hastanelerin karşılaştırılarak incelenmesi için oluşturulan verimlilik sınıflandırılması başarı ile sonuçlanmıştır. Hastane tasarımcıları ve yöneticileri, mevcut hastanelerin değerlendirmesini ve karşılaştırmaları yapabilmek için bu çalışmadan geribildirim yoluyla bilgi edinebilir. Sonuç olarak, ilgili binalar hakkında karar verme aşmasında(örneğin binanın iyileştirme ihtiyacının olup olmadığı, yeni mekanlara gerek duyulup duyulmadığı gibi) bu modelden faydalanabilirler.Article Citation - WoS: 175Citation - Scopus: 203Fuzzy Logic Model for the Prediction of Cement Compressive Strength(Elsevier Ltd., 2004) Akkurt, Sedat; Tayfur, Gökmen; Can, SeverA fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO3, and C3S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If-Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.Article Citation - WoS: 68Citation - Scopus: 86Fuzzy Logic Algorithm for Runoff-Induced Sediment Transport From Bare Soil Surfaces(Elsevier Ltd., 2003) Tayfur, Gökmen; Özdemir, Serhan; Singh, Vijay P.Utilizing the rainfall intensity, and slope data, a fuzzy logic algorithm was developed to estimate sediment loads from bare soil surfaces. Considering slope and rainfall as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relations among rainfall intensity, slope, and sediment transport were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF-THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The sediment load predicted by the fuzzy model was in satisfactory agreement with the measured sediment load data. Predicting the mean sediment loads from experimental runs, the performance of the fuzzy model was compared with that of the artificial neural networks (ANNs) and the physics-based models. The results of showed revealed that the fuzzy model performed better under very high rainfall intensities over different slopes and over very steep slopes under different rainfall intensities. This is closely related to the selection of the shape and frequency of the fuzzy membership functions in the fuzzy model.