Sercan Serin

Planning maintenance works on pavements through ant colony optimization

Pavements constructed for the purpose of meeting the demand of highways which were emerged with the improving technological developments increased. And consequently, more resources were demanded to be directed to pavement maintenance and rehabilitation. Hereby, the concept of pavement management emerged. Although project-level analyses were found adequate previously, network-level evaluations were needed in order to do detailed planning as a result of resource allocation and transfer problems that were emerged later. Therefore, pavement management system has become compulsory for all pavements to be controlled together. In this framework, programming is needed in order to schedule maintenance–rehabilitation and develop costs with respect to budget. In the study carried out, a mode was developed in order to program the routine network maintenance activities in terms of Pavement Maintenance and Management Systems, and it was concluded that this problem can be solved through ant colony, using Visual Basic.

Effects of Freezing and Thawing Cycles on the Engineering Properties of Soils

In this study, particular engineering characteristics of soil exposed to freezing and thawing cycles were investigated. Low plasticity clay (CL) soil samples (classified according to the USCS soil classification system) were sampled in situ, and some basic properties of these soil samples were investigated by performing sieve analyses, hydrometer tests, specific gravity tests, and liquid, plastic, and shrinkage limits tests. The same tests were also conducted after freezing and thawing cycles. Additionally, scanning electron microscope (SEM) tests to determine the microstructures of the soil samples and energy dispersive X-ray-EDX tests to determine the chemical compositions of the samples were performed. Finally, triaxial compression tests were conducted before and after the freezing and thawing cycles to determine the strength parameters of the soil samples. The experimental results show that the physical and mechanical properties of the soil changed significantly after the freezing and thawing cycles.

Prediction of the marshall stability of reinforced asphalt concrete with steel fiber using fuzzy logic

In 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/cm3 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.

Effects of Freezing and Thawing on the Consolidation Settlement of Soils

The effects of freezing and thawing on consolidation parameters and other properties of soil were investigated experimentally. Samples of soils were collected in-situ and characterized in the laboratory. Index properties of soil samples were determined by conducting sieve analyses, hydrometer tests, specific gravity tests, and liquid limit, plastic limit, and shrinkage limit tests before and after 30 freezing-thawing cycles. Microstructure and elemental composition of the soil samples were determined by scanning electron microscope (SEM) and energy dispersive X-ray (EDX) analysis, respectively. To determine the effects of freezing thawing onto the consolidation parameters of soil, consolidation tests were conducted on the samples before and after the freezing-thawing cycles. After 30 freezing-thawing cycles, consolidation settlements increased by about 23%.

Modeling Marshall Stability of light asphalt concretes fabricated using expanded clay aggregate with Artificial Neural Networks

In 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 (μs), unit weight (gr/cm3) 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.

Modelling Marshall Stability of fiber reinforced asphalt mixtures with ANFIS

In this study, an Adaptive Neural Fuzzy Inference System (ANFIS) model for predicting the Marshall Stability (MS) of basalt fiber reinforced asphalt concrete mixtures and various mix proportions has been developed. Experimental details were used to construct the model. The amounts of bitumen (%), Fiber (Basalt) Ratio (%) were used as input variables and Marshall Stability (kg) values were used as output variables. Statistical equations were used to evaluate the Developed ANFIS model. Results showed that developed ANFIS model has strong potential to predict Marshall Stability of asphalt concrete using related inputs in a short time. Also, the Marshall Stability of Fiber-Reinforced asphalt concrete and various mix proportions can be found without performing any experiments.

Marshall stability estimating using artificial neural network with polyparaphenylene terephtalamide fibre rate

Due 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.

Determining amount of bituminous effects on asphalt concrete strength with artificial intelligence and statistical analysis methods

In this study, an experimental study has been conducted to determine compressive strength of asphalt concrete. The scope of study by preparing 45 Marshall samples Marshall stability experiment was conducted and compressive strength of asphalt concrete was determined. Compressive strength of asphalt concrete as depending on bituminous amount prediction models were developed by using obtained experiment results. Compressive strength of asphalt concrete values as depending on bituminous amount have been estimated on prediction models developed with regression analyses and Artificial Neural Network (ANN) Methods. Results obtained from models were compared with experiment results. Prediction performances of developed models were evaluated as compared. As a result it was determined that possible to estimate the compressive strength of asphalt concrete as depending on bituminous amount with developed ANN model and that ANN model was more successful than regression model for estimating the compressive strength of asphalt concrete.