Vernacular architecture has evolved over time to reflect and sustainably comply with the environmental and cultural contexts in which it exists. This kind of architecture possesses a variety of original and clever practices and technologies to satisfy various necessities imposed by context. Iran's vernacular architecture has mastered the art of adaptation to context by developing different architectures in different regions of the country. Despite their different appearances, these architectures follow the same logic in spirit: sustainable adaptation to context. This original research work surveys this logic in two regions, one hot-arid and the other hot-arid-windy, in Iran (represented by the city of Yazd and the region of Sistan, respectively) through a comparative study. This paper studies different elements and techniques of sustainability in these areas, reasons for their existence and the factors that have shaped them into the specific way that they are. The main elements that were studied through this survey include: fabric and orientation, sidewalks, facades, materials, entrances, courtyards, basements and cellars, porches, roofs, wind-catchers, and openings. In conclusion, links that connect different specifications of context to different aspects of construction are discovered and their role in overall character of two region's architecture is illustrated so they can be used as guidelines for future designs and constructions.

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<p>Water penetration, changes in the groundwater level and moisture content changes can affect the physical and chemical properties of coal in an open pit mine. Water levels in open coal pit mines can vary throughout the year, resulting in a number of wet and dry cycles for brown coal. Wet and dry cycles occurring throughout the year can affect the mechanical strength of the stone's microstructure and macroscopic structure. Loss of strength can have severe negative impacts if such rock is integral component in landform design. Until now, no research has been conducted on the effects of wet and dry loading cycles on brown coal. This study investigates the effect of wet and dry cycles on brown coal's strength by conducting a series of unconfined compressive strength (UCS) laboratory tests. For this purpose, nine laboratory samples with dimensions of 38 x 76 cm were prepared. Samples were placed inside distilled water chambers in a temperature-controlled environment. Afterwards, the samples were subjected to unconfined compressive strength (UCS) tests following 0, and 3 cycles of wet and dry conditions. The results of the UCS test show that as the number of wetting and drying cycles increased, the UCS of the samples decreased from 2150 to 330 kPa after three cycles of wetting and drying. In addition, the results indicate that the elastic modulus of brown coal has decreased from 10500 to 1200 kPa. Also, the Poisson ratio decreased from 0.34 to 0.27. This study confirms the importance of paying attention to the wet and dry cycles in brown coal mines.</p>

Alum sludge is a byproduct of water treatment plants and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this paper explores the use of artificial intelligence (AI) methods to predict the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR based on a database with nine input parameters. The results showed that all three AI models were able to predict CBR with good accuracy, with coefficient of determination (R2) values ranging from 0.94 to 0.99 and mean absolute error (MAE) values ranging from 0.30 to 0.51. In a novel approach, the genetic programming method was used to produce an equation to estimate CBR, which included seven inputs and accurately predicted CBR. The analysis of sensitivity and importance of parameters showed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study suggests that AI methods can effectively predict the performance of alum sludge as a soil stabilizer, and the proposed equation using genetic programming can be a useful tool for predicting CBR.