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Recently, the automation of architectural design process has been focused specifically in Healthcare buildings (HCB) for easier implementation and faster feedback. Hence, a great evolution of Computational Implementations (CIs) were appeared and studied for early stages of design process of HCB, such as generating design solutions, evaluation of design solutions or others to provide helpful tools for designers. This paper provides comparative analyses of a set of CIs that can be applied on different architectural design stages in HCB, these stages includes: a) architectural programming and topological diagram, b) generating design alternatives and c) evaluation of design solutions. The focused CIs in this study have been classified to computational tools, computational algorithms, computational approaches and hybrid methods; they are compared using a set of criteria with various evaluation methods. This comparative analysis helps designers to determine the appropriate CI or CIs' combinations for each main early stage of HCB design, and also the possibilities of connecting two of the studied CIs in a framework to get wider outcomes were studied. The main study findings are the capabilities, limitation and features of each CI, in addition to the best selections in each early stage for different HCB design cases. Also, possible connections between different CIs were discussed on simple examples as a fruitful outcome of analyzing CIs' combinations.

Self-learning is receiving great attention internationally in different fields, along with the best utilization of different computational applications or methods. This paper introduces a novel computational approach for supporting Architectural Design Education (ADE) in its early stages; a computational implementation through MATLAB has been developed to conduct the proposed processes. As a scope, spaces’ furnishing design has been selected to demonstrate the proposed computational approach and implementation, while office workspaces have been selected as a representative case. However, the proposed approach provides and enhances ADE through three main concepts: (a) generating design alternatives for different cases of furnishing spaces, (b) providing accurate and flexible evaluations to students’/designers’ works with different levels, and (c) tracking students based on their defaults and relevant sensitive modifications. Different applications of the proposed approach have been generated, analyzed, and validated.