Particle shape is widely considered to be one of the most important factors affecting the behaviour of granular soils. This effect may change, based on variations in the grain size distribution. Therefore the effect of particle shape on the behaviour of granular soils should be investigated in soils of similar gradations. In this study, 20 different sands of five origins are initially subjected to image analyses in order to obtain data concerning shape characteristics. Then Proctor tests at three energy levels are conducted to obtain the densification characteristics of these soils. Basic statistical analyses are employed to obtain the distributions that the particle shape characteristics best fit. Empirical equations are developed to estimate the relative density of sands. Constant-head permeability tests are performed to determine the most suitable empirical formula for estimating the coefficient of permeability for granular soils. A new method for obtaining the shape coefficient for the Kozeny–Carman equation is proposed. Overall, relationships between a number of particle shape and fractal dimension parameters, as well as several statistics related to the relative density and permeability of soils, are investigated within an experimental framework.

In this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. The city is located at a hot–humid climatic region. In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor climatic conditions, day of year and indoor thermal comfort parameters. The indoor thermal comfort parameters, namely, IT and IRH values between 6 June and 21 September 2009 were collected via HOBO data logger. Fraction of variance ( R²) and root-mean squared error values calculated by the use of the outputs of different ANN architectures were compared. Moreover, several multiple regression models were developed to question their performance in comparison with those of ANNs. The results showed that an ANN model trained with inconsiderable amount of data was successful in the prediction of IT and IRH parameters in education buildings. It should be emphasized that this model can be benefited in the prediction of indoor thermal comfort conditions, energy requirements, and heating, ventilating and air conditioning system size.