CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures
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It is the view of the author that a student can obtain much more from her or his first course in CAADD if some fundamental concepts are covered specifically and dramatically, rather than assumed or conveyed by osmosis. On the other hand, one does not want to significantly delay the teaching of he principal objective: how to use a computer as a partner in design and production. The answer to meeting these two divergent objectives is two-fold: (1) careful organization with computer based tutorials, and (2) integration of architectonic lessons during the introduction.
The objectives of he initial five weeks are (1) to demystify computers, (2) teach the fundamental concepts of computer systems relating to hardware (disks, cpu, memory, display), and software (programs, data, files), (3) illustrate programming and program design, and (4) convey the concept of discrete symbol manipulation and its relation to graphics and text.
Part One establishes facts: the human and financial investment that universities have made in CAD, based on results from publications and a national ACADIA survey, and the investment of architectural firms in CAD, based on recent national and regional in-depth studies.
Part Two examines goals of the use of CAD in the design studio. For better analysis, goals are divided into two extreme categories: tool independent and tool dependent. Tool independent goals are born out of the need to improve the existing design education, independent from technological development. Tool dependent goals are tailored to the alleged capabilities of new software and hardware and to pressure from the professional community. The actual definition of goals for design education will lie somewhere in between.
Part three examines the reality in the design studio. It tries to determine the place of the computer in the design process from a student's view, and an educator's view. The last section is dedicated to the testing of the developed theory against actual studios.
One cannot conduct such studies on real cities except, perhaps, as a point of departure at some specific point in time to provide an initial layout for a city knowing that future forms derived by the studies will diverge from that recorded in history. An entirely imaginary city is therefore chosen. Although the components of this city at the level of individual buildings are taken from known cities in history, this choice does not preclude alternative forms of the city. To some degree, building types are invariants and, as argued in the Appendix, so are the urban typologies into which they may be grouped. In this imaginary city students of urban history play the role of citizens or groups of citizens. As they defend their interests and make concessions, while interacting with each other in their respective roles, they determine the nature of the city as it evolves through the major periods of Western urban history in the form of threedimensional computer models.
My colleague R.J. van Pelt and I presented this approach to the study of urban history previously at ACADIA (Seebohm and van Pelt 1990). Yet we did not pay sufficient attention to the manner in which such urban models should be structured and how the efforts of the participants should be coordinated. In the following sections I therefore review what the requirements are for three-dimensional modeling to support studies in urban history as outlined both from the viewpoint of file structure of the models and other viewpoints which have bearing on this structure. Three alternative software schemes of progressively increasing complexity are then discussed with regard to their ability to satisfy these requirements. This comparative study of software alternatives and their corresponding file structures justifies the present choice of structure in relation to the simpler and better known generic alternatives which do not have the necessary flexibility for structuring the urban model. Such flexibility means, of course, that in the first instance the modeling software is more timeconsuming to learn than a simple point and click package in accord with the now established axiom that ease of learning software tools is inversely related to the functional power of the tools. (Smith 1987).
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