On Risk and Reliability Studies of Climate-Related Building Performance
Book chapter, 2018

A design strategy based on integration of the building form and structure with its external environment in order to take advantage of natural forces (wind and buoyancy effects) has been evaluated in terms of risk and reliability measures. Tools for the probabilistic analysis (first-order reliability method (FORM), Monte Carlo) have been presented and applied in the probabilistic modelling and sensitivity analysis of the response function of the studied building physics problem. Sensitivity analysis of the influence of basic random variables on the probability distribution of a response function is straightforward in FORM methodology.

The case-based studies of probabilistic modelling of uncertainties coupled to wind speed and temperature difference through the specified building/environment system have been presented (i.e., the distribution models of the air change rate ACH and the dynamic U value characterising thermal performance of dynamic insulation). Sensitivities of the probability model of ACH to the parameters of wind speed and temperature distributions have been estimated for the consecutive values of the air change rate using FORM methodology. Reliability of ACH turned out to be most sensitive to the shape parameter of the wind speed distribution (in two-parameter Weibull model).

The probabilistic risk analysis along with the effective tools for sensitivity analysis can be used to support design decisions and also to develop better models for evaluation of building performance.

dynamic U value

FORM

air infiltration

climate mitigation

reliability

ACH

probabilistic approximation

climate

building performance

wind

sensitivity

risk

Author

Krystyna Pietrzyk

Chalmers, Architecture and Civil Engineering

Ireneusz Czmoch

Risk Assessment

87-110

Driving Forces

Sustainable development

Areas of Advance

Building Futures (2010-2018)

Subject Categories

Other Civil Engineering

Building Technologies

Probability Theory and Statistics

DOI

10.5772/intechopen.71684

More information

Latest update

3/2/2018 5