Research into system design rationale in the past has focused on the representation of design deliberations and has omitted the connections between design rationales and design artefacts. Without such connections, designers and architects cannot easily assess how changing requirements or design decisions may affect the system. In this article, we introduce the Architecture Rationale and Element Linkage (AREL) model to represent the causal relationships between architecture elements and decisions. We further model AREL as a Bayesian Belief Network (BBN) to capture the probabilistic dependency relationships between the architecture elements and decisions in such an architecture design model. Such probabilistic modelling enables architects to quantitatively analyse (i.e. predict and diagnose) the impact of change in either the requirements or the design. Using a partial design of a cheque image processing system, we illustrate how AREL is used to represent the decision model and how the BBN is used to predict and diagnose change impact in the architecture design. We use a UML tool to capture the AREL model and a BBN tool to compute the probabilities of change impact.