Abstract:Abstract. The article presents the effects of research on models of high density housing. The authors present urban projects for experimental high density housing estates. The design was based on research performed on 38 examples of similar housing in Poland that have been built after 2003. Some of the case studies show extreme density and that inspired the researchers to test individual virtual solutions that would answer the question: How far can we push the limits? The experimental housing projects show str… Show more
“…Yet, few studies discussed occupant perception of density. Bradecki et al (2017); Dave (2011) concluded that the feeling of high density is related to typology rather than being an issue of how many people are living within one square km or unit space. This aligns with what this study found; that perception of density is related to buildings form, typology, location of windows and setbacks.…”
“…Yet, few studies discussed occupant perception of density. Bradecki et al (2017); Dave (2011) concluded that the feeling of high density is related to typology rather than being an issue of how many people are living within one square km or unit space. This aligns with what this study found; that perception of density is related to buildings form, typology, location of windows and setbacks.…”
“…BP neural networks [26], support vector machines [20], Gaussian process regression [43], random forests [44], radial basis function neural networks [45] and other methods in the application of concrete strength prediction [46], beam shear strength prediction [47], column bearing capacity prediction [48], bridge damage detection [49], and frame structure damage prediction [50] all show that machine learning can not only predict the damage of the structure in the macro-aspect but also predict the bearing capacity of structural components. Among many machine learning algorithms, the Gaussian process regression method has advantages in dealing with regression problems such as small sample size, multiple influencing factors, and nonlinearity [51][52][53]. Compared with other machine learning algorithms, such as artificial neural networks and support vector machines, Gaussian process regression is easy to implement, with fewer parameters and strong model interpretability [54][55][56][57][58].…”
The determination of the bearing capacity prediction model of concrete-filled steel tubular columns is a key issue in the structural design of prefabricated buildings, which directly relates to the stability and safety of prefabricated buildings. The purpose of this paper is to study the bearing capacity model of concrete-filled steel tubular columns, and propose an explicit formula based on the Gaussian process regression algorithm to calculate the bearing capacity. In order to solve the problem of low accuracy of the traditional empirical bearing capacity model, this paper first proposes a more accurate bearing capacity prediction model based on Gaussian process regression algorithm to automatically learn and capture the characteristics of 122 groups of test data; the paper then determines the function of high sensitivity parameters and section influence parameters through the established bearing capacity prediction model, and this process gives the display formula. Compared with the implicit formula given by a machine learning model, the explicit formula proposed in this paper is more suitable for practical engineering design. In order to verify the validity of the formula, we generated the bearing capacity data through the proposed formula based on the test data and used the descriptive statistical method to verify. The results show that the proposed formula is superior to other existing methods, the error between the data generated by the proposed formula and the test data is smaller, and its accuracy reaches 93.73%, which is more suitable for calculating the bearing capacity of concrete-filled steel tubes with different cross sections.
“…Changes in the Polish political and economic system since 1989 have resulted in unfavourable processes in the structure of residential areas. Housing complexes which developed very quickly in open green areas, often gated, have strengthened the urban sprawl phenomenon, created spatial chaos and worsened the quality of the environment [16][17][18][19].…”
Cities grow through the addition of new housing structures, but the existing tissue is also modernized. Krakow, like any city with a historical origin, has typologically varied housing tissue. A large area of the city is occupied by multi-family panel-block housing estates which are being revitalised and the scope of this revitalization should include sustainable design elements. This paper determines the potential for implementing integrated water management, that utilizes rainwater in an existing basic urban unit that is a housing estate from the nineteen-seventies, located in Krakow (Poland), in conjunction with the Bio-Morpheme—the fractal reference model unit. The parameters of the Bio-Morpheme were established by earlier research as the optimum for a housing unit with regards to the circular economy and improving water use efficiency. The study covers the need to improve the quality of the housing environment, linked with the presence of natural elements, including a water reservoir, in the direct vicinity of the development. The analyses explored the potential to employ integrated water management with rainwater reuse in a basic urban unit (Krakow-Morpheme) and then compared the findings with the outcomes obtained by the proposed Bio-Morpheme complex. The results indicate that the potential to achieve a lower demand of water from the water supply system and to lower wastewater production were obtained, with a simultaneous opportunity to lay out an open water reservoir into the Krakow-Morpheme urban interior for improvement of the health value and well-being of inhabitants.
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