WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Author: search.filters.author.Fedakar, Halil IbrahimScopus Q: search.filters.scopusQuartile.Q2

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  • Article
    Citation - WoS: 11
    Citation - Scopus: 11
    Developing New Empirical Formulae for the Resilient Modulus of Fine-Grained Subgrade Soils Using a Large Long-Term Pavement Performance Dataset and Artificial Neural Network Approach
    (Sage Publications inc, 2021-12-17) Fedakar, Halil Ibrahim
    Artificial neural network (ANN) has been successfully used for developing prediction models for resilient modulus (M-r). However, no reliable M-r formula derived from these models has been proposed in previous studies, although engineers/researchers need empirical formulae for hand calculation of M-r. Therefore, this study aimed to propose reliable empirical formulae for the M-r of fine-grained soils using ANN. For this purpose, thousands of ANN models were developed using the long-term pavement performance (LTPP) and external datasets. The input parameters were the percentage of soil particles passing through #200 sieve (P200), silt percentage (SP), clay percentage (CP), liquid limit (LL), plasticity index (PI), maximum dry density ([rho(dry)](max)), optimum moisture content (w(opt)), confining pressure (sigma(c)), and nominal maximum axial stress (sigma(z)). The ANN models were compared with several constitutive models. The results indicate that the constitutive models failed to predict the M-r, and the best M-r predictions were obtained by the ANN-C9 (P200, SP, CP, LL, PI, sigma(c), and sigma(z)), ANN-C10 (P200, SP, CP, [rho(dry)](max), w(opt), sigma(c), and sigma z), and ANN-C11 (P200, SP, CP, LL, PI, [rho(dry)](max), w(opt), sigma(c), and sigma(z)) models. Thus, the structures of these ANN models were formulated and proposed as the new empirical formulae for the M-r of fine-grained soils. Sensitivity analysis was also performed on these ANN models. It was determined that (rho(dry))(max) is the most influential parameter in the ANN-C10 model, and LL is the most influential parameter in the ANN-C9 and ANN-C11 models. On the other hand, sigma(c) and sigma(z) are the least influential parameters.
  • Article
    Citation - WoS: 17
    Citation - Scopus: 18
    Comparative Analysis of Hybrid Geothermal-Solar Systems and Solar PV With Battery Storage: Site Suitability, Emissions, and Economic Performance
    (Pergamon-Elsevier Science Ltd, 2025-01) Fedakar, Halil Ibrahim; Dincer, Ali Ersin; Demir, Abdullah
    Renewable energy integration has become a critical focus in the global effort to reduce carbon emissions and diversify energy sources. In regions with distinct geographic features, such as Turkiye, combining different renewable technologies can offer enhanced energy security. This study investigates the site suitability and economic and environmental performance of hybrid geothermal-solar systems and solar PV systems with battery storage across the provinces of Osmaniye, Hatay, and Kilis, of Turkiye. Using the fuzzy-AHP method, site suitability is evaluated, addressing a key gap in comparing these systems' adaptability to varying geographic conditions. This study is the first to directly compare these two renewable energy technologies in terms of site suitability. The findings reveal significant differences in site suitability, with solar PV systems with battery storage demonstrating broader applicability across the region. The suitable sites (20-100 % suitability) cover 1260.82 km(2) for solar PV systems with battery storage and only 122.18 km(2) for hybrid geothermal-solar systems. In terms of environmental impact, hybrid geothermal-solar systems exhibit significantly lower carbon emissions, averaging 44.6 kg CO2/MWh, compared to 123.8 kg CO2/MWh for solar PV systems with battery storage. Economically, hybrid geothermal-solar systems also outperform with a lower levelized cost of electricity of $0.091 kWh versus $0.254 kWh for solar PV systems. These results highlight the environmental and economic advantages of hybrid geothermal-solar systems, while also emphasizing their limited scalability to regions with geothermal activity. Conversely, solar PV systems, despite their higher emissions and costs, offer greater flexibility and potential for widespread deployment.