Many systems exist without us knowing structural system models and require us to discover structural system models from system data. A major shortcoming of current statistical modeling and data mining techniques is their focus on building relations of variables that hold for all values of variables. This paper presents the new partial-value association discovery (PVAD) algorithm to discover relations of variables that may exist for only certain values or different value ranges of variables and to use these partial-value variable relations for constructing structural system models. The PVAD algorithm along with its performance and computational complexity is presented.