There is an increased interest in sensing technologies to meet the challenges in the engineering and construction fields. These technologies will help us better monitor and evaluate structural performance and health. This research presents a framework for a stochastic design of an early warning system for buildings by introducing a risk measure as the reference variable that encapsulates the different effects retrieved by the monitoring instruments. Bayesian Network was implemented to develop the proposed early warning system. Within a decision-making framework, the risk measure serves as the index for defining the system warning thresholds. In order to develop the framework, it is necessary to build a sensor equipped monitoring system first. This paper addresses development of a structural health monitoring system for Katherine Harper Hall at Appalachian State University, NC, USA as a very useful approach to improve the safety of the building. It proposes a framework for a stochastic design of an early warning system to avoid, or at least mitigate the impact posed to building occupants by a threat related to live loads.