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

Kaijian Liu, Nora El-Gohary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There 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.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2019
Subtitle of host publicationSmart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
EditorsYong K. Cho, Fernanda Leite, Amir Behzadan, Chao Wang
PublisherAmerican Society of Civil Engineers
Pages105-112
Number of pages8
ISBN (Electronic)9780784482445
DOIs
StatePublished - Jan 1 2019
EventASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019 - Atlanta, United States
Duration: Jun 17 2019Jun 19 2019

Publication series

NameComputing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019
CountryUnited States
CityAtlanta
Period6/17/196/19/19

ASJC Scopus subject areas

  • Computer Science(all)
  • Civil and Structural Engineering

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  • Cite this

    Liu, K., & El-Gohary, N. (2019). A Hybrid Information Fusion Method for Fusing Data Extracted from Inspection Reports for Supporting Bridge Data Analytics. In Y. K. Cho, F. Leite, A. Behzadan, & C. Wang (Eds.), Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (pp. 105-112). (Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019). American Society of Civil Engineers. https://doi.org/10.1061/9780784482445.014