İnşaat Mühendisliği Bölümü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12416/17

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Citation - WoS: 11
Citation - Scopus: 15
Assessment of the Effectiveness and the Initial Cost Efficiency of Hot Recycled Asphalt Using Polymer Modified Bitumen
(Elsevier, 2023) Almusawi, Ali; Shoman, Sarmad; Lupanov, Andrei P.
The drastic increase in environmental concerns and increasing costs of road construction materials necessitate evaluating some alternative solutions. One of the most suitable alternatives is recycling old asphalt pavement to produce reclaimed asphalt pavement (RAP). The RAP materials have been commonly combined with asphalt mixtures during pavement construction. Incorporating RAP material should demonstrate an equivalent or better performance than conventional asphalt mixtures. Conversely, the inclusion of RAP mainly needs to improve performance compared to conventional asphalt mixtures. The key issue of using RAP is to restore the loss properties of aged materials and normally asphalt Agent Rejuvenator (ARA) was used. Also, adding polymers with RAP into the asphalt mixture becomes necessary to obtain the required performance. This study investigated the RAP effects of elastomeric polymer on the performance of the asphalt mixture following Russian standards (GOST). The impact of using PMB with RAP material on the asphalt mixture's performance was primarily considered by employing tests that can reveal the adhesion property. Additionally, the performance of the pavement was evaluated in terms of strength and low-temperature cracking. For this purpose, numerous test methods were implemented to appraise the asphalt performance, such as compressive strength, moisture susceptibility, shear resistance, tensile strength, porosity of the mineral particles, and residual porosity. The results indicated that the overall performance of the asphalt mixtures prepared with RAP and combined with polymer depicted a better performance. Moreover, the initial construction cost for each asphalt composition was estimated and compared. The utilization of PMB increased the cost of the asphalt mixture. However, such an increase in the cost would lead to an increase in the overall performance, especially for RAP mixtures.
Citation - WoS: 12
Citation - Scopus: 12
Economic and Environmental Impacts of Utilizing Lower Production Temperatures for Different Bitumen Samples in a Batch Plant
(Elsevier, 2022) Almusawi, Ali; Sengoz, Burak; Ozdemir, Derya Kaya; Topal, Ali
The utilization of hot mix asphalt (HMA) for road construction necessitates high temperatures during mixing bitumen and aggregate at asphalt plant. The required (mixing) production temperature is calculated by the standard method (ASTM 2493). The application of this method for polymer modified bitumen (PMB) and warm mix asphalt (WMA) have tendency of higher temperatures. Therefore, some alternative methods suggested by literatures for the determination of production temperature for PMB and WMA have been implemented aiming to determine lower temperatures than the standard method (ASTM 2493). Moreover, the economic impacts of the determined production temperatures through different models are evaluated by the estimation of energy consumption in terms of electricity and natural gas costs for the batch type asphalt plants. Besides, the possible environmental effects are calculated by considering the carbon dioxide emissions. The results of this study have shown that the reduction in production temperatures led to a significant decrease in the total construction cost of each type of asphalt and a significant reduction in the estimated carbon dioxide emission. The results of this study can be used as a reference point for the estimation of both economic and environmental impacts of utilizing lower production temperatures for different bitumen samples.
Citation - WoS: 17
Citation - Scopus: 20
Effect of Sm on Crystallization Kinetics of Cu-Zr Metallic Glasses
(Elsevier, 2020) Sikan, F.; Polat, G.; Kalay, I.; Kalay, Y. E.
The effect of Sm micro-alloying on non-isothermal and isothermal crystallization kinetics of (Zr50Cu40Al10)(100-x)Sm-x (x = 0, 2, 4 at. % Sm) alloys were investigated using differential scanning calorimetry (DSC), transmission electron microscopy (TEM), and X-ray diffraction (XRD). Crystallization activation energies for each composition were calculated in non-isothermal conditions using Kissinger and Ozawa methods and in isothermal conditions using Johnson-Mehl-Avrami model. XRD analysis showed that crystallization product Cu10Zr7 changes to Cu2Sm with Sm presence in isothermal conditions. Both isothermal and isochronal calculations yield that the energy barrier for crystallization has increased with Sm addition. On the other hand, crystallization point drops to lower temperature at the expense of an increase in the pre-exponential factor. The Avrami exponents for all compositions were found to be below 2.5, indicating that crystallization was governed by a diffusion-controlled three-dimensional growth with a decreasing nucleation rate. The apparent increase in crystallization activation energies with increasing Sm content can be one of the affecting factors for commonly held idea of increased glass forming ability for rare-earth containing Zr-based metallic glasses.
Citation - WoS: 9
Citation - Scopus: 13
A Metaheuristic-Guided Machine Learning Approach for Concrete Strength Prediction With High Mix Design Variability Using Ultrasonic Pulse Velocity Data
(Elsevier, 2023) Selcuk, S.; Tang, P.
Assessment of concrete strength in existing structures is a common engineering problem. Several attempts in the literature showed the potential of ML methods for predicting concrete strength using concrete properties and NDT values as inputs. However, almost all such ML efforts based on NDT data trained models to predict concrete strength for a specific concrete mix design. We trained a global ML-based model that can predict concrete strength for a wide range of concrete types. This study uses data with high variability for training a metaheuristic-guided ANN model that can cover most concrete mixes used in practice. We put together a dataset that has large variations of mix design components. Training an ANN model using this dataset introduced significant test errors as expected. We optimized hyperparameters, architecture of the ANN model and performed feature selection using genetic algorithm. The proposed model reduces test errors from 9.3 MPa to 4.8 MPa.

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