A Hybrid Information Fusion Method for Fusing Data Extracted from Inspection Reports for Supporting Bridge Data Analytics

ABSTRACTThere has been an increasing demand for data-driven and machine learning-based bridge deterioration prediction approaches for supporting enhanced bridge maintenance decision making. Bridge inspection reports, which contain a wealth of information about bridge conditions, open opportunities for data analytics to better understand and predict bridge deterioration. However, learning from the reports is challenging, because they usually contain multiple—even ambiguous, uncertain, and conflicting—information about the same bridge element, its deficiencies, and its deficiency measurements. Learning from such data negatively affects the generalizability and the separability of machine learning models, which compromises the performance of data-driven prediction. To address this challenge, this paper proposes a hybrid information fusion method. The method includes two main components: named entity normalization for fusing concepts in ambiguous surface forms into a canonical form, and data fusion for fusing numerical deficiency measurements containing uncertainties and conflicts into a unified and consistent representation. This paper focuses on analyzing the data fusion requirements, and presenting the proposed data fusion method and its evaluation results. The results indicate that the proposed method can adequately address the fusion requirements.

• Record URL:
  • https://doi.org/10.1061/9780784482445.014
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482445
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers 2019
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Liu, Kaijian
  • El-Gohary, Nora
• Conference:
  • ASCE International Conference on Computing in Civil Engineering 2019
  • Location: Atlanta Georgia, United States
  • Date: 2019-6-17 to 2019-6-19
• Publication Date: 2019

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 105-112
• Monograph Title: Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience

Subject/Index Terms

• TRT Terms: Bridges; Data analysis; Data fusion; Data mining; Decision making; Inspection; Machine learning; Maintenance
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01708136
• Record Type: Publication
• ISBN: 9780784482445
• Files: TRIS, ASCE
• Created Date: Jun 21 2019 10:05AM