Constraint satisfaction methods for applications in engineering

Constraints provide declarative descriptions of important requirements related to engineering projects. Most existing algorithms for constraint satisfaction require input consisting of binary constraints on variables that have discrete values. Such restrictions limit their use in engineering since complex constraints, involving several variables that have discrete and numeric values, are common. This paper provides an approach for decision support through approximating solution spaces that are defined by constraints. Our algorithm is not limited to a specific type of constraint, but handles numeric and discrete variables in the same framework. Since a new type of local consistency narrows down the search space affectively, full-scale engineering tasks, such as designs involving hundreds of variables, are accommodated without excessive computational complexity. The approach is demonstrated for selection of appropriate wind bracing for single story steel-framed buildings. Results may be used for input into other tools containing algorithms such as those offering (i) higher levels of consistency, (ii) optimally directed point-solution search, and (iii) simulation behavior. Finally, extension to dynamic constraint satisfaction using different combinations of activation conditions is straightforward. It is expected that this approach will improve the performance of many existing and future computeraided engineering tools.