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Öğe Estimation of specific gravity with penetration and penetration index parameters by artificial neural network(International University of Sarajevo, 2017) Serin, Sercan; Karahançer, Şebnem; Erişkin, Ekinhan; Morova, Nihat; Saltan, Mehmet; Terzi, SerdalSpecific Gravity of the bitumen changes according to the ambient temperature. Different specific gravity values can be calculated at different temperature. Estimating models like Artificial Neural Network - ANN could be very useful to obtain the specific gravity value uniform. Specific gravity values obtained from Long-Term Pavement Performance - LTPP were estimated with artificial neural networks. Penetration and Penetration Index of binder were used for estimating the specific gravity of the bitumen. As a result, ANN get 84% of R2 between obtained and estimated values.Öğe Evaluation of rice husk ash as filler in hot mix asphalt concrete(Elsevier Sci Ltd, 2013) Sargın, Şebnem; Saltan, Mehmet; Morova, Nihat; Serin, Sercan; Terzi, SerdalIn the study, it was investigated to use the rice husk ash (RHA) in the hot mix asphalt as mineral filler. For this purpose, four different serial asphalt concrete samples were produced using limestone (LS) in different proportions (4%, 5%, 6%, and 7%) as mineral filler. The amount of optimum bitumen and the value of Marshall Stability (MS) were determined with MS test for the samples. Choosing the series of asphalt having 5% filler which has given the highest stability RHA was changed with LS filler in the rate of 25%, 50%, 75%, and 100%. After that MS test was conducted on the produced samples and the results were evaluated. As a result, it has come in view that RHA can be used as mineral filler in the asphalt concrete. (C) 2013 Elsevier Ltd. All rights reserved.Öğe Investigation of usability of steel fibers in asphalt concrete mixtures(Elsevier Sci Ltd, 2012) Serin, Sercan; Morova, Nihat; Saltan, Mehmet; Terzi, SerdalIn this study, the usability of steel fibers in order to bear the stresses occurring at the surface layer of pavement, which are directly subjected to the traffic effects, was investigated. In this context, specimens were produced and tested under Marshall Stability Test, and the optimum bitumen content value for the aggregates sample to be used was determined. Results showed that based on the determined value for the optimum bitumen content (5.5%), three specimens for each of a series of different fiber rates (0%, 0.25%, 0.50%, 0.75%, 1.0%, 1.5%, 2.0%, 2.5%) were prepared and the optimum value for fiber rate that results in the best stability value was determined as 0.75%. As a result, steel fiber additions can be used in binder course of flexible pavement because of its positive stability impact. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.Öğe Marshall Stability Estimating Using Artificial Neural Network with Polyparaphenylene Terephtalamide Fibre Rate(Ieee, 2016) Karahançer, Şebnem; Çapalı, Buket; Erişkin, Ekinhan; Morova, Nihat; Serin, Sercan; Saltan, Mehmet; Küçükçapraz, Dicle ÖzdemirDue to the complex behaviour of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting stability of asphalt pavement is difficult. To predict, it is required to find the mathematical relation between the input and output data by an accurate and simple method. In recent years, artificial neural networks (ANNs) have been used to model the properties and behaviour of materials, and to find complex relations between different properties in many fields of civil engineering applications, because of their ability to learn and to adapt. In the present study, laboratory data are obtained from an experimental study that was used to develop an ANN model. For predicting the Marshall Stability value of mixture using ANN models, an appropriate selection of input parameters (neurons) is essential. There are four nodes in the input layer corresponding to four variables: Polyparaphenylene Terephtalamide fibre (PTF) rate, binder rate, flow, volume of the specimen. The result indicates that the proposed model can be applied in predicting Marshall Stability of asphalt mixtures. The model is further applied to evaluate the effect of different rates of Polyparaphenylene Terephtalamide on Marshall Stability.Öğe Modeling Marshall stability of light asphalt concretes fabricated using expanded clay aggregate with artificial neural networks(2012) Morova, Nihat; Karahançer, Şebnem Sargın; Terzi, Serdal; Saltan, Mehmet; Serin, SercanIn this study, an Artificial Neural Network (ANN) model has been developed to estimate Marshall Stability (MS) of lightweight asphalt concrete containing expanded clay. In the model, amount of bitumen (%), transition speed of ultrasound (?s), unit weight (gr/cm 3) were used as inputs and Marshall Stability (kg) was used as output. Developed ANN model results and the experimental results were compared and good relationship was found. © 2012 IEEE.Öğe Prediction of the marshall stability of reinforced asphalt concrete with steel fiber using fuzzy logic(Ios Press, 2014) Serin, Sercan; Morova, Nihat; Saltan, Mehmet; Terzi, Serdal; Karaşahin, MustafaIn this study, Marshall Stability (MS) of steel fiber reinforced asphalt concrete has been predicted using steel fiber rate (0%, 0.25%, 0.50%, 0.75%, 1.0%, 1.5%, 2.0% and 2.5%), bitumen content (5%, 5.5% and 6.0%) and unit weights (2,465- 2,515 (gr/cm(3))) by Fuzzy Logic (FL). Results have shown that developed FL model has a strong potential for predicting the MS of asphalt concrete without performing any experimental studies.Öğe Prediction of the Pavement Serviceability Ratio of Rigid Highway Pavements by Artificial Neural Networks(Düzce Üniversitesi, 2013) Morova, Nihat; Serin, Sercan; Terzi, Serdal; Saltan, MehmetThe term ‘‘present serviceability’’ was adopted to represent the momentary ability of pavement to serve traffic, and the performance of the pavement was represented by its serviceability history in conjunction with its load application history. Serviceability was found to be influenced by longitudinal and transverse profile as well as the extent of cracking and patching. The amount of weight to assign to each element in the determination of the over-all serviceability is a matter of subjective opinion. In this study, the present serviceability index of rigid highway pavements has been predicted by an artificial neural network (ANN) model. For this model, the 49 experimental data obtained from AASHTO include slope variance, faulting, cracking, spalling and patching. The developed ANN model has a higher regression value than the AASHO model. This approach can be easily and realistically performed to solve the problems which do not have a formulation or function for the solution.Öğe Utility of polyparaphenylene terephtalamide fiber in hot mix asphalt as a fiber(Elsevier Sci Ltd, 2016) Morova, Nihat; Serin, Sercan; Terzi, Serdal; Saltan, Mehmet; Küçükçapraz, D. Özdemir; Karahançer, Şebnem Sargın; Erişkin, EkinhanIn this study, utility of polyparaphenylene terephtalamide (PT) was investigated in hot mix asphalt as a fiber. For this aim samples were prepared with limestone aggregate at different proportions. Marshall Stability test was applied and optimum bitumen content was determined. In the second stage of the study, new samples were prepared using different polyparaphenylene terephtalamide fiber (PTF) rates of 0.25%, 0.50%, 0.75%, 1.00%, 1.50%, 2.00% based on optimum bitumen content. When examining test results, samples which prepared using 0.25% PTF rate gave highest Marshall Stability result. At the final stage of the study, different bitumen contents (4.15%, 4.65% and 5.15%) were conducted for the best fiber rate (0.25%) and close to this result (0.50% and 0.75%). Thus, the effect of bitumen content on determined fiber rate at the second stage of the study was investigated. Also Indirect Tensile (IDT) Strength Test was performed on hot mix asphalt (HMA) samples preparing at 0.25%, 0.50% and 0.75% fiber rates and moisture sensitivities were determined. All results showed that, the best fiber rate was 0.25% and determined optimum bitumen content remain constant with the fiber additive for the reference samples. Besides, some sample groups which prepared using different PTF rates proved the specification limits and it was said that; PTF can be used in asphalt concrete as a fiber additive. (c) 2015 Elsevier Ltd. All rights reserved.
