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A hybrid structured deep neural network with Word2Vec for construction accident causes classification
Dalarna University, School of Technology and Business Studies, Energy Technology.
2019 (English)In: International Journal of Construction Management, ISSN 1562-3599Article in journal (Refereed) Epub ahead of print
Abstract [en]

According to the latest fatal work injury rates reported by the Bureau of Labors Statistics, construction sites remain the most hazardous workplaces. In the construction sector, fatality investigation summary reports are available for past accidents and by investigating such reports, valuable insights can be gained. In this study, text mining algorithms are explored for automatic construction accident causes classification. To be more specific, Word2Vec skip-gram model is utilized to learn word embedding from a domain-specific corpus and a hybrid structured deep neural network is proposed by incorporating the learned word embedding for accident reports classification. Dataset from Occupational Safety and Health Administration (OSHA) is employed in the experiment to evaluate the performance of the proposed approach. Besides, five baseline models: support vector machine (SVM), linear regression (LR), K-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB) are employed to compare with the proposed approach. Experiment results show that the proposed model achieves the highest average weighted F1 score among all models considered in this study. The result also proves the effectiveness of applying Word2Vec skip-gram algorithm for semantic information augmentation. As a result, robustness of the model is improved when classifying cases of low support values.

Place, publisher, year, edition, pages
Taylor & Francis, 2019.
Keywords [en]
Construction safety, construction automation, machine learning, natural language processing, deep neural networks, Word2Vec
National Category
Civil Engineering Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-31174DOI: 10.1080/15623599.2019.1683692ISI: 000495217500001Scopus ID: 2-s2.0-85075085414OAI: oai:DiVA.org:du-31174DiVA, id: diva2:1375948
Available from: 2019-12-06 Created: 2019-12-06 Last updated: 2019-12-18

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Zhang, Fan

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf