Implementation of structural health monitoring systems on to large scale civil structures continues to face many challenges. The general SHM paradigm, in data-driven modes, calls for at least two learning cases: the structure in some reference state, and the structure in its target damaged state(s). Because of the complexity of civil structures in geometry, material composition, construction, and operational/environmental conditions, the task of developing a robust understanding of how to develop an optimal SHM system design for acquiring data and transforming it into a decision is daunting. The SHEMA structure developed by the UCSD structural health monitoring group will provide a controllable design testbed with which to bridge the gap between nascent SHM solution ideas and and large scale civil structure implementation.