Bayesian Network model for the design of roofpond equipped buildings

The adoption of new and more sustainable construction technologies is sometimes difficult, due to the lack of adequate knowledge to properly perform rough sizing of such systems in the professional environment. The shortage of proper simulation programs for the preliminary design of sustainable construction prevents in fact the application of these systems in the contemporary construction market, oftentimes producing higher design costs and construction durations that exceed those of comparable standard buildings. In this contribution an Object Oriented Bayesian model is developed, intended as an expert system for the design of buildings equipped with roofponds. Thanks to the explicit causal structure of Bayesian Networks, they are able to model also the very complex thermal behavior of roofponds, due to their changeable properties varying with seasons, building characteristics and climatic parameters. This probabilistic model is able to cope with several building configurations, and provides architects with a tool for multi-criteria decision making, besides computing the expected improvements brought by the presence of such a technology in the building to be designed. Furthermore, its graphic interface is adequately simple to be used also by non-expert people, allowing a faster spread of this technology in the market. Finally, it is shown how the model can be applied to perform three main objectives: choice of the best design solution among several possibilities, optimal sizing of design parameters and rough sizing of roofpond buildings under conditions of uncertainty. The very good results obtained by the validation of this model demonstrate the feasibility of this technique that can constitute the right way to lead architects in the rough-sizing process of roofpond buildings.