To this day, proper handling of uncertainties -including unknown variables in
primary stages of a design, an actual climate data, occupants` behavior, and
degradation of material properties over the time- remains as a primary challenge
in an architectural design decision-making process. For many years,
conventional methods based on the architects' intuition have been used as a
standard approach dealing with uncertainties and estimating the resulting errors.
However, with buildings reaching great complexity in both their design and
material selections, conventional approaches come short to account for
ever-existing but unpredictable uncertainties and prove incapable of meeting the
growing demand for precise and reliable predictions. This study aims to develop
a probability-based framework and associated prototypes to employ uncertainty
analysis and sensitivity analysis in architectural design decision-making. The
current research explores an advanced physical model for thermal energy
exchange characteristics of a hypothetical building and uses it as a test case to
demonstrate the proposed probability-based analysis framework. The proposed
framework provides a means to employ uncertainty and sensitivity analysis to
improve reliability and effectiveness in a buildings design decision-making
process
