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authors Durmisevic, Sanja
year 2002
title Perception Aspects in Underground Spaces using Intelligent Knowledge Modeling
source Delft University of Technology
summary The intensification, combination and transformation are main strategies for future spatial development of the Netherlands, which are stated in the Fifth Bill regarding Spatial Planning. These strategies indicate that in the future, space should be utilized in a more compact and more efficient way requiring, at the same time, re-evaluation of the existing built environment and finding ways to improve it. In this context, the concept of multiple space usage is accentuated, which would focus on intensive 4-dimensional spatial exploration. The underground space is acknowledged as an important part of multiple space usage. In the document 'Spatial Exploration 2000', the underground space is recognized by policy makers as an important new 'frontier' that could provide significant contribution to future spatial requirements.In a relatively short period, the underground space became an important research area. Although among specialists there is appreciation of what underground space could provide for densely populated urban areas, there are still reserved feelings by the public, which mostly relate to the poor quality of these spaces. Many realized underground projects, namely subways, resulted in poor user satisfaction. Today, there is still a significant knowledge gap related to perception of underground space. There is also a lack of detailed documentation on actual applications of the theories, followed by research results and applied techniques. This is the case in different areas of architectural design, but for underground spaces perhaps most evident due to their infancv role in general architectural practice. In order to create better designs, diverse aspects, which are very often of qualitative nature, should be considered in perspective with the final goal to improve quality and image of underground space. In the architectural design process, one has to establish certain relations among design information in advance, to make design backed by sound rationale. The main difficulty at this point is that such relationships may not be determined due to various reasons. One example may be the vagueness of the architectural design data due to linguistic qualities in them. Another, may be vaguely defined design qualities. In this work, the problem was not only the initial fuzziness of the information but also the desired relevancy determination among all pieces of information given. Presently, to determine the existence of such relevancy is more or less a matter of architectural subjective judgement rather than systematic, non-subjective decision-making based on an existing design. This implies that the invocation of certain tools dealing with fuzzy information is essential for enhanced design decisions. Efficient methods and tools to deal with qualitative, soft data are scarce, especially in the architectural domain. Traditionally well established methods, such as statistical analysis, have been used mainly for data analysis focused on similar types to the present research. These methods mainly fall into a category of pattern recognition. Statistical regression methods are the most common approaches towards this goal. One essential drawback of this method is the inability of dealing efficiently with non-linear data. With statistical analysis, the linear relationships are established by regression analysis where dealing with non-linearity is mostly evaded. Concerning the presence of multi-dimensional data sets, it is evident that the assumption of linear relationships among all pieces of information would be a gross approximation, which one has no basis to assume. A starting point in this research was that there maybe both linearity and non-linearity present in the data and therefore the appropriate methods should be used in order to deal with that non-linearity. Therefore, some other commensurate methods were adopted for knowledge modeling. In that respect, soft computing techniques proved to match the quality of the multi-dimensional data-set subject to analysis, which is deemed to be 'soft'. There is yet another reason why soft-computing techniques were applied, which is related to the automation of knowledge modeling. In this respect, traditional models such as Decision Support Systems and Expert Systems have drawbacks. One important drawback is that the development of these systems is a time-consuming process. The programming part, in which various deliberations are required to form a consistent if-then rule knowledge based system, is also a time-consuming activity. For these reasons, the methods and tools from other disciplines, which also deal with soft data, should be integrated into architectural design. With fuzzy logic, the imprecision of data can be dealt with in a similar way to how humans do it. Artificial neural networks are deemed to some extent to model the human brain, and simulate its functions in the form of parallel information processing. They are considered important components of Artificial Intelligence (Al). With neural networks, it is possible to learn from examples, or more precisely to learn from input-output data samples. The combination of the neural and fuzzy approach proved to be a powerful combination for dealing with qualitative data. The problem of automated knowledge modeling is efficiently solved by employment of machine learning techniques. Here, the expertise of prof. dr. Ozer Ciftcioglu in the field of soft computing was crucial for tool development. By combining knowledge from two different disciplines a unique tool could be developed that would enable intelligent modeling of soft data needed for support of the building design process. In this respect, this research is a starting point in that direction. It is multidisciplinary and on the cutting edge between the field of Architecture and the field of Artificial Intelligence. From the architectural viewpoint, the perception of space is considered through relationship between a human being and a built environment. Techniques from the field of Artificial Intelligence are employed to model that relationship. Such an efficient combination of two disciplines makes it possible to extend our knowledge boundaries in the field of architecture and improve design quality. With additional techniques, meta know/edge, or in other words "knowledge about knowledge", can be created. Such techniques involve sensitivity analysis, which determines the amount of dependency of the output of a model (comfort and public safety) on the information fed into the model (input). Another technique is functional relationship modeling between aspects, which is derivation of dependency of a design parameter as a function of user's perceptions. With this technique, it is possible to determine functional relationships between dependent and independent variables. This thesis is a contribution to better understanding of users' perception of underground space, through the prism of public safety and comfort, which was achieved by means of intelligent knowledge modeling. In this respect, this thesis demonstrated an application of ICT (Information and Communication Technology) as a partner in the building design process by employing advanced modeling techniques. The method explained throughout this work is very generic and is possible to apply to not only different areas of architectural design, but also to other domains that involve qualitative data.
keywords Underground Space; Perception; Soft Computing
series thesis:PhD
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