A Novel Hybrid Transformer Approach for Text-Based Defect Classifications in the Construction Industry

In the construction industry, defects and their rework cause significant unplanned overheads. Defects are recorded in natural language, and while traditional manual evaluation and classification of defects is labor- and resource-intensive, transformer-based deep learning methods enable automated and...

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Библиографические подробности

Главные авторы:	Ricardo Buettner, Pascal Penava, Max Wiedemann
Формат:	Artigo
Язык:	Inglês
Опубликовано:	IEEE 2025-01-01
Серии:	IEEE Access
Предметы:	Defect classification construction natural language processing machine learning deep learning transformer
Online-ссылка:	https://ieeexplore.ieee.org/document/11271422/
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