Perception Aspects in Underground Spaces using Intelligent Knowledge Modeling

Architectur

Knowledge management by information mining

Architectur

Pattern Grammar within the Language of Architecture

Architectur

A Fuzzy-Neural Tree Knowledge Model for the Assessment of Building’s Transformation

One building is more flexible in terms of use than the other and to determine how much a building ‘X’ is more flexible than a building ‘Y’ is a rather complex task. This research focuses on houses for the elderly in terms of future use, since the requirements have changed and many of the existing buildings do not meet new requirements. To asses a transformation of a building one needs to take many aspects into account such as: spatial transformation, technical transformation and their various sub-aspects. There are also different future use scenarios, defined by Netherlands Board for Healthcare Institutions, and one scenario is more suitable for a building than another. Firstly, in order to deal with this complex topic there is a need for a systematic approach where all relevant aspects determining a transformation value of a building will be defined. Thereafter, fuzzy-neural tree structure is used as a suitable method for knowledge representation and knowledge modeling.Architecture and The Built Environmen

Quantifying the Qualitative Design Aspects

Architectur

Architectural pattern generation by discrete wavelet transform and utilisation in structural design

Since computers were introduced in architectural design as a valuable tool, there was a growing need to develop tools that would support the designer from the initial phase of the design till the detailing. In a computer aided architectural design environment it is feasible to stimulate the spatial design ideas and create alternatives in an efficient way. Pattern Grammar approach is one of the design alternatives where patterns, based on complex spatial geometry, are used as an underlayer for a design. In this research, the wavelets techniques are used as pattern grammar and applied to spatial information processing for the generation and analysis of the architectural patterns as well as for supporting decision-makings in structural realisations.Computer ScienceArchitectur

Soft Computing in Construction Information Technology

The last decade, civil engineering has exercised a rapidly growing interest in the application of neurally inspired computing techniques. The motive for this interest was the promises of certain information processing characteristics, which are similar to some extend, to those of human brain. The immediate examples of these include an ability to learn and generalize. In parallel to this and further developments in the information systems technology, established the essential motivation that the construction industry should benefit from these developments for enhanced and effective executions. Today such information processing methods are collectively referred to as soft computing (SC). Explicitly, soft computing is an emerging approach to computing which parallels the remarkable ability of the human mind to reason and learn in an environment of certainty and imprecision. SC consists of several computing paradigms, including neural networks, fuzzy set theory, approximate reasoning and combinatorial optimization methods such as genetic algorithms. SC finds important applications in diverse disciplines. As a branch of artificial intelligence, soft computing is closely related to computational intelligence where in essence SC methods are implemented by machine learning techniques. Paper deals with SC in the context of construction information technology (CIT) pointing out the important role it can play. It exemplifies the SC applications in CIT supported by pilot implementations, which are carried out as a part of ongoing departmental research program.Architecture and The Built Environmen

Multi-objective design for space layout topology

Architectur

On the classification enhancement of radial basis function networks

Artificial neural networks are powerfultools for analysing information expressed as data sets, which contain complex nonlinear relationships to be identified and classified. In particular radial basis function (RBF) neural networks have outstanding features for this. However, due to far reaching implications of the basis functions in the functionality of RBF networks they are still subject to study for best performance, in a general sense. One important parameter is the width of the radial basis functions. Here, we investigate the formation of a RBF neural network for its enhanced performance, which is closely related to the width parameter. For this aim, two key implementations are orthogonal least squares for training and multiresolutional decomposition of the sequence at the output of the network by wavelets.Architecture and The Built Environmen

Knowledge modeling for performance measurement of built environment

Architecture and The Built Environmen