Automated system for construction specification review using natural language processing

Seonghyeon Moon

doi:10.1016/j.aei.2021.101495

2022-01-01

Seonghyeon Moon

Gitaek Lee

Seokho Chi *

DOI：10.1016/j.aei.2021.101495

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摘要

En 中文

Existing attempts to automate construction document analysis are limited in understanding the varied semantic properties of different documents. Due to the semantic conflicts, the construction specification review process is still conducted manually in practice despite the promising performance of the existing approaches. This research aimed to develop an automated system for reviewing construction specifications by analyzing the different semantic properties using natural language processing techniques. The proposed method analyzed varied semantic properties of 56 different specifications from five different countries in terms of vocabulary, sentence structure, and the organizing styles of provisions. First, the authors developed a semantic thesaurus for construction terms including 208 word-replacement rules based on Word2Vec embedding to understand the different vocabularies. Second, the authors developed a named entity recognition model based on bi-directional long short-term memory with a conditional random field layer, which identified the required keywords from given provisions with an averaged F1 score of 0.928. Third, the authors developed a provision-pairing model based on Doc2Vec embedding, which identified the most relevant provisions with an average accuracy of 84.4%. The web-based prototype demonstrated that the proposed system can facilitate the construction specification review process by reducing the time spent, supplementing the reviewer's experience, enhancing accuracy, and achieving consistency. The results contribute to risk management in the construction industry, with practitioners being able to review construction specifications thoroughly in spite of tight schedules and few available experts.

Keyword:

Construction specification

Automated review

Natural language processing

Machine learning

Text mining