Digital Transformation and Reconstruction of Morphological Language in the Foundation of Architectural Art
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Abstract
The digital transformation of architectural art requires efficient approaches for generating, evaluating, and reconstructing complex morphological languages. This study proposes a digital reconstruction framework integrating parametric design, generative algorithms, and building information modeling to support data-driven architectural form generation. Architectural morphology is represented through parameterized descriptors, while clustering algorithms and generative design techniques are employed to reorganize and optimize formal characteristics. The proposed workflow includes parameter database construction, control-variable definition, algorithm-driven form generation, and quantitative evaluation of generated solutions. Experimental results demonstrate that the framework improves formgeneration efficiency by 63.2% and increases morphological diversity by 48.7% compared with conventional manual approaches. The study advances the transition from experience-driven design to computational morphology and provides methodological references for digital modeling, spatial information processing, and computational design optimization.
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