WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 3Citation - Scopus: 3Social and Spatial Equity/Equality in Relation to High-Speed Trains: Lessons From Turkey's High-Speed Train Experience(Sage Publications inc, 2023-03-06) Bas, Ahmet; Delaplace, MarieIn the twentieth century, high-speed trains (HSTs) were added to the choice of transportation modes in Japan and Europe, and in the twenty-first century HST networks in developing countries have been advanced. It is planned to enhance these networks further in the future. Developing countries are characterized by income inequalities and, thus, it is important to find out who uses HSTs. If they are only viable in the wealthiest regions, then this mode of transportation will induce spatial inequity. If HST travel is too expensive, then HSTs will induce social inequity. Numerous studies have explored the relationship between HSTs and equity, but they have mostly covered economically developed countries, with only a few studies being carried out in economically developing countries apart from China. As such, the aim of this article is to fill the gap in the literature by analyzing the case of Turkey's HSTs. The study presents a review of the literature pertaining to HSTs and the issue of equity, then uses Turkey's socioeconomic development index to make comparisons of HST service accessibility according to different social groups, ages, and occupations. Ticket price and accessibility indicators are used to work out how HSTs can be a tool for reducing accessibility inequalities. The results indicate that HSTs do not necessarily reinforce the existing accessibility inequalities in Turkey, but can be a tool for improving equity in three ways: their ticket pricing policy; their considerable range, in that they serve numerous cities all over the country; and the way they are used in relation to different groups.Article Citation - WoS: 11Citation - Scopus: 11Developing 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 IbrahimArtificial 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.