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Identifying causality and contributory factors of pipeline incidents by employing natural language processing and text mining techniques

Guanyang Liu,M. Boyd,Mengxi Yu,S. Halim,N. Quddus

Published 2021 in Chemical engineering research & design

ABSTRACT

Abstract The key to learning from the past incidents is to identify the underlying causes and contributory factors of the incidents. A large amount of text data on incident narratives has been accumulated over the years and can be a good learning source, if properly utilized. However, the vast amount and unstructured nature of the text data impedes generating insights on occurring patterns of incidents. This research sets upon applying natural language processing (NLP) and text mining techniques to utilize the resource for understanding contributing factors and causations behind the incidents with pipeline industry as an illustrative example. The 3587 records of incident narratives of the ‘comment’ section in the incident database of Pipeline and Hazardous Materials Safety Administration (PHMSA) are exploited. Two methods of text analytics, K-means clustering and co-occurrence network, are employed to infer latent causality of incidents. The results demonstrate that both methods are capable of identifying contributing factors under specific failure types. The co-occurrence network approach exhibits advantages on extracting dependency among the contributory factors, while K-means clustering is only able to indicate general correlations. The workflow proposed in this paper provides new perspectives of identifying contributing factors and their causal dependency from incident text data for promising applications in risk analysis and accident modeling.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    Chemical engineering research & design

  • Publication date

    2021-08-01

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1016/J.PSEP.2021.05.036
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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