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- BildiriAçık Erişim2025Investigation of Mechanical Properties of Hot Mix Asphalt Modified with Elvaloy and Polyphosphoric Acid
Yilmaz, Mehmet; Yalcin, Beyza Furtana; El Naggar, H.; Barros, J.; Cachim, P.
In order for the bituminous hot mix layers to resist the increased traffic load and volume as well as the adverse environmental conditions for a long time without being deformed, the bituminous binder used in the mixture has been commonly modified with the Styrene-Butadiene-Styrene (SBS) polymer. The high demand for SBS makes it difficult to supply and therefore brings on the use of alternative additives. In this study, the combined use of Elvaloy and phosphoric acid (PPA) was investigated. Additionally, the effects on the mechanical properties of bituminous mixtures were examined. Modified binders were prepared using Elvaloy/PPA additives mixed under specified conditions. Hot mix asphalt (HMA) samples prepared with these binders were subjected to Marshall stability and flow, indirect tensile stiffness modulus, and indirect tensile fatigue tests to comprehensively evaluate the effects of modified bi nders on the mechanical properties of bituminous hot mixtures. As a result, the bituminous hot mix tests revealed that asphalt mixtures improved their mechanical properties at the specified ratios.
- BildiriAçık Erişim2025Deep Learning-Based Approaches for Short-Term Residential Electricity Consumption Prediction: A Comparative Study of LSTM, CNN-LSTM, and CNN-GRU Models
Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Benedicenti, L.; Liu, Z.
The rapid increase in the world population has increased electricity consumption and demand for electricity. In order for electricity distribution companies to determine electricity demand, household electricity consumption must be predicted. In this study, an open access dataset was used to predict individual household electricity consumption. For this purpose, deep learning-based LSTM, CNN-LSTM and CNN-GRU models were developed and a comparative analysis was performed. The performance of the models was evaluated using four evaluation metrics commonly used in prediction model performance evaluation. According to the results obtained, it was seen that the models used in the study were successful in predicting short-term electricity consumption. In particular, the CNNLSTM model was found to have a lower error rate compared to the other two models.
- BildiriAçık Erişim2025Health Status Classification of Electric Motors Using CNN-Based Models and SDP Images Under Varying Noise Conditions
Ertarğın, Merve; Yordanov, Nikolay; Zhilevski, Marin; Benedicenti, L.; Liu, Z.
The increasing application of electric motors across various industrial sectors requires effective monitoring and early diagnostics to prevent potential failures. This study explores the use of Convolutional Neural Network (CNN)-based models together with Symmetrized Dot Pattern (SDP) sound representations for classifying the health status of electric motors under different noise conditions. Acoustic data from a brushless DC motor were transformed into SDP images, which were then used to train CNN models. The dataset included recordings of motors in “Good”, “Broken” and “Heavy Load” conditions, captured under various noise environments such as pure, talking, white noise, atmospheric, and stress test conditions. The classification tasks were conducted under three conditions: assessing motor health status, evaluating both motor health status and noise types, and excluding the stress test noise type for a balanced dataset. The results demonstrated that the CNN models achieved high accuracy rates in classifying motor health status, with the Custom CNN model performing best in simpler tasks and MobileNet excelling in more complex scenarios. The study highlights the feasibility of using SDP images with CNN-based models for fault classification in motors and suggests future research directions for improving classification accuracy through advanced feature extraction techniques and multimodal data representations.
AtıfScopus1
İncele - BildiriAçık Erişim2024Investigation of the Effects of Different Modified Asphalts on Storage Stability
Yalcin, Beyza Furtana; Yilmaz, Mehmet; Yalcin, Erkut; Ozdemir, Ahmet Münir; El Naggar, H.; Barros, J.; Cachim, P.
In this study, the storage stability of modified bitumen prepared with bitumen additives styrene-butadiene-styrene (SBS) and American Gilsonite (AG) was evaluated. For this purpose, modified bitumen was prepared by using different ratios of each additive type. The prepared modified bitumens were subjected to storage stability test according to EN 13399 standard. Penetration, softening point and rotational viscometry tests were performed on the samples subjected to storage stability test. As a result of the penetration and softening point tests of the samples prepared with SBS after storage stability, it was determined that the penetration and softening point values of the samples obtained from the upper part of the tube were higher. This is thought to be due to the accumulation of SBS, which has a lower specific gravity than bitumen, in the upper part of the tube during the storage stability test. Considering the viscosity test results, it was determined that the viscosity values of the modified bitumen samples obtained from the upper part of the tubes were higher than the samples obtained from the lower part of the tubes.
- BildiriAçık Erişim2024Effect Of The Actıvatıon Force On The Healıng Level Of Asphalt Mıxture Wıth Embedded Capsules
Yalcin, Erkut; Yilmaz, Mehmet; Yalcin, Beyza Furtana; El Naggar, H.; Barros, J.; Cachim, P.
By increasing the asphalt mixtures' inherent capacity for self-healing, encapsulated rejuvenators can extend the service lives of asphalt pavements. The capsules are shattered when crack damage occurs, allowing the rejuvenator ingredient to seep into the cracks. In this work, a brand-new kind of capsule was created. Sunflower oil was a rejuvenator in these capsules. We looked at how well asphalt mixture samples with and without capsules self-healed under static loading. The capacity for self-healing of asphalt mixtures incorporating a novel class of capsules that contain sunflower oil as a rejuvenating agent. For this reason, the ability of the rejuvenating ingredient to diffuse inside of samples of virgin bitumen and the healing potential of combinations of cracked asphalt containing capsules have both been assessed. Due to the mechanical properties of the polymeric capsules examined. Then, using a unique mechanical test, the impact of capsule content on the crack-healing characteristics of asphalt mixtures was assessed at various healing durations (from 5 to 216h) and loads (from 0 to 85kN). As the applied load increases, the healing levels of the asphalt samples with capsules rise; later, the healing levels of the asphalt samples with capsule remain constant. According to the findings, the maximal healing level was typically attained after 96 hours and remained constant until 216 hours.
- BildiriAçık Erişim2024Classifying induction motor faults using spectrogram images with deep transfer learning
Ertargin, Merve; Yildirim, Ozal; Orhan, Ahmet; Benedicenti, L.
For industrial systems to be reliable and efficient, motor faults detection is important. Conventional methods of fault detection are typically expensive and time-consuming. The ability to detection motor faults has advanced significantly in recent years thanks to the application of deep learning techniques. Deep learning algorithms have the ability to automatically extract complicated features from big data sets, which can speed up and improve the accuracy of motor faults detection. Since collecting motor acoustic data is cost-effective and easy, it is advantageous to use it in fault detection. In this study, motor acoustic signals were converted into spectrograms and faults in induction motors were detected using a transfer learning approach with pre-trained models. Totally 8-class fault detection was performed with an accuracy rate of 91.52% using VGG16 and 92.11% using VGG19.
AtıfScopus4OpenAlex4İncele - DergiWorld Congress on Civil, Structural, and Environmental Engineering
- DergiProceedings of the World Congress on Electrical Engineering and Computer Systems and Science