An Expandable Software Model for Collaborative Decision Making During the Whole Building Life Cycle
Decisions throughout the life cycle of a building, from design through construction and commissioning to operation and demolition, require the involvement of multiple interested parties (e.g., architects, engineers, owners, occupants and facility managers). The performance of alternative designs and courses of action must be assessed with respect to multiple performance criteria, such as comfort, aesthetics, energy, cost and environmental impact. Several stand-alone computer tools are currently available that address specific performance issues during various stages of a building's life cycle. Some of these tools support collaboration by providing means for synchronous and asynchronous communications, performance simulations, and monitoring of a variety of performance parameters involved in decisions about a building during building operation. However, these tools are not linked in any way, so significant work is required to maintain and distribute information to all parties.
In this paper we describe a software model that provides the data management and process control required for collaborative decision making throughout a building's life cycle. The requirements for the model are delineated addressing data and process needs for decision making at different stages of a building's life cycle. The software model meets these requirements and allows addition of any number of processes and support databases over time. What makes the model infinitely expandable is that it is a very generic conceptualization (or abstraction) of processes as relations among data. The software model supports multiple concurrent users, and facilitates discussion and debate leading to decision making. The software allows users to define rules and functions for automating tasks and alerting all participants to issues that need attention. It supports management of simulated as well as real data and continuously generates information useful for improving performance prediction and understanding of the effects of proposed technologies and strategies.