Applied Sciences · 2026

Norm-Driven Generative BIM Design: Semantic Parsing and Automated Layout for Small-Scale Power Infrastructure

Therefore, model training and assessment produce Accuracy, Precision, Recall, and F1 of 98.05%, 95.49%, 95.88%, and 95.59% separately, with 100% precision for logical comparison and conjunction labels; thus, this provides a steady semantic base for the rule base.

Authors
Y Chen; Chunli Ying; Hao Zhu; Jun Chen; Daguang Han
Journal
Applied Sciences, 2026
DOI
10.3390/app16083804
Licence
CC-BY
Citations
0 (OpenAlex, 3 Aug 2026)
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Key findings

  • Therefore, model training and assessment produce Accuracy, Precision, Recall, and F1 of 98.05%, 95.49%, 95.88%, and 95.59% separately, with 100% precision for logical comparison and conjunction labels; thus, this provides a steady semantic base for the rule base.
  • Hence, validation on a real case in a digital operation center for the power system shows that the design time for the third-floor administrative office area was cut from about 20 h to around 4 h, and the first-time solution met all code restrictions, which improves efficiency and compliance in a significant way.

Abstract

To deal with the high standards, strong restrictions, and high repeatability that are inside State Grid small-scale infrastructure projects, this research puts forward a norm-driven generative design method, which conquers the low efficiency, compliance dangers, and semantic breakage that are usual in manual modeling. Taking standards such as Q/GDW 11382.3-2015 as the knowledge origin, we construct an ALBERT-BiLSTM-CRF semantic parsing model and change natural-language clauses into executable design restrictions via normative text pre-processing, BIO sequence marking, and rule triplet mapping. Therefore, model training and assessment produce Accuracy, Precision, Recall, and F1 of 98.05%, 95.49%, 95.88%, and 95.59% separately, with 100% precision for logical comparison and conjunction labels; thus, this provides a steady semantic base for the rule base. At the component level, a three-part coding plan and unit module collection are built based on OmniClass and GB/T 51269, which makes semantic consistency and traceability between components and space functions possible. At the system level, a continuous work process is carried out through the Revit API, which covers scheme making, automatic arrangement, and deliverable output. Hence, validation on a real case in a digital operation center for the power system shows that the design time for the third-floor administrative office area was cut from about 20 h to around 4 h, and the first-time solution met all code restrictions, which improves efficiency and compliance in a significant way. The results point out that norm-driven generative design can supply deployable automation and high-quality outputs for small-scale power infrastructure, which provides a sustainable database for digital twins and smart O&M.

Cite this work

@article{han2026normdrivengenera,
  title   = {Norm-Driven Generative BIM Design: Semantic Parsing and Automated Layout for Small-Scale Power Infrastructure},
  author  = {Y Chen and Chunli Ying and Hao Zhu and Jun Chen and Daguang Han},
  journal = {Applied Sciences},
  year    = {2026},
  doi     = {10.3390/app16083804},
}

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Author identity: ORCID 0000-0003-3787-963X · Google Scholar