Assigning Robust Default Values in Building Performance Simulation Software for Improved Decision-Making in the...

Area of Science:

• Building Science
• Data Mining
• Architectural Design

Background:

• Building Information Modeling (BIM) databases contain implicit design patterns.
• Accurate early-stage building performance simulations require reliable default values.
• Existing methods for setting default values can lead to significant variations.

Purpose of the Study:

• To demonstrate the significance of a novel method for minimizing variations in default values for BIM-based design.
• To assess the variation in ideal default values under different building design conditions.
• To investigate the influence of specific conditions on these variations.

Main Methods:

• Data mining techniques applied to a database of BIM models.
• Assessment of variations in ideal default values for diverse building design conditions.
• Investigation of the influence of factors like space depth, lighting control, and natural ventilation.

Main Results:

• Space depth significantly impacts the ideal default window-to-wall ratio.
• The presence or absence of lighting control and natural ventilation substantially influences ideal default values.
• Identified specific building conditions critical for determining accurate default values.

Conclusions:

• The proposed method effectively maintains the accuracy of simulation results in early design stages.
• Understanding the influence of design conditions is crucial for setting accurate default values in BIM.
• This approach supports more informed and reliable architectural design decisions.