Building design review is the procedure of checking a design against codes and standard provisions to satisfy the accuracy of the design and identify non-compliances before construction begins. The current approaches for conducting the design review process in an automatic or semi-automatic manner are either based on proprietary, domain-specific or hard-coded rule-based mechanisms. These methods may be effective in their specific applications, but they have the downsides of being costly to maintain, inflexible to modify, and lack a generalized framework of rules and regulations modeling that can adapt to various engineering design realms, and thus don’t support a neutral data standard. They are often referred to as ‘Black Box’ or ‘Gray Box’ approaches. This research offers a new comprehensive framework that reduces the limitations of the cited methods. Building regulations, for instance, are legal documents transcribed and approved by professionals to be interpreted and applied by people. They are hardly as precise as formal logic. Engineers, architects, and contractors can read those technical documents and transform them into scientific notations and software applications. They can extract any data they need, reason about it, and apply it at various phases of the project. How these extraction and use are carried out is a critical component of automating the design review process. The chief goal is to address this issue by developing a Generalized Adaptive Framework (GAF) for a neutral data standard (Industry Foundation Classes (IFC)) that enables automating the code compliance checking processes to achieve design efficiency and cost-effectiveness. The objectives of this study comprise i) to develop a theoretical background to an adaptive framework that supports a neutral data standard for transforming the written code regulations and rules into a computable model, and ii) to define the various modules required for computerizing of the code compliance verification process.