Keywords: fuzzy, Mahoney Tables, climate, building design, bioclimatic, comfort.

Under the economical and environmental concerns of sustainable construction, it is essential to take into account the effect of the local climate in building design. Within this context, in order to improve the building performances such as thermal comfort and energy efficiency, several methods were developed to characterize the local climate to propose design guidelines based on the climatic data of the site, such as mean temperature, humidity, precipitation and wind. However, the application of the most of the methods is restricted to few very well defined climates. This is because of the difficulty in treating the uncertainties regarding the bounds of climatic groups and the range of the validity of the design.

An example of the methods is Mahoney Tables [1], which may be summarized in three steps: the input and determination of the climatic data of the site; the calculation of the humidity and arid indicators that characterize the local climate with strategies to provide thermal comfort; and the assessment of design recommendations. In the method, the bounds of the groups and comfort limits are defined by using crisp intervals of climatic data. As a result, it fails to generate consistent design recommendations when applied to the analysis of a region whose climate is a transition one: When using two sets of climatic data that are statistically equals, the application of Mahoney Tables may produce contradictory results.

With the aim of developing of a method that provides consistent building design guidelines for the regions with transition climates, the authors of the present work remodeled the Mahoney Tables through the replacement the crisp intervals by fuzzy sets. In addition to this, fuzzy logic [2] was applied to replace the crisp interval operations to assess the humidity and arid indicators, which are used for the characterization of local climate.

The present paper shows the fundamentals, the computer code of the improved method and some results of case studies. The results show that the inconsistencies of the traditional approach can be eliminated by the remodelling, and it clearly that the main shortcoming of the traditional Mahoney Tables is the inadequate modeling of the fuzzy information by means of a deterministic approach, which is very common in many existing design methods. The tuning up of the improved method and the fuzzy approach for the assessment of the design guidelines are being undertaken.

1

United Nations, "Department of Economic and Social Affairs. Climate and House Design: Design of low-cost housing and community facilities", New York, 1971.

2

J. Yan, M. Ryan, J. Power, "Using Fuzzy Logic", Prentice Hall, 1994.

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