Estimating bridge performance based on a matrix-driven fuzzy linear regression model

Nang Fei Pan, Tzu Chieh Lin, Nai Hsin Pan

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


Determining a reliable bridge maintenance and rehabilitation strategy relies on accurate predictions of bridge conditions. Conventional regression cannot handle visual inspection results that are inherently non-crisp or linguistic. On the other hand, fuzzy regression provides an effective means for coping with such fuzzy data or linguistic variables. However, many of the existing fuzzy regression models require substantial computations due to complicated fuzzy arithmetic. This paper presents a multiple fuzzy linear regression using matrix algebra. The proposed model is capable of dealing with a mixture of fuzzy data and crisp data. Moreover, the approach is intuitive and easy to implement as compared to other related fuzzy regression models. A case study using bridge inspection data is presented to establish estimated fuzzy regression equations produced by the proposed approach and examine the factors contributing to overall bridge performance. The results demonstrate the capability of the approach, which can assist bridge managers to make better maintenance policies based on the future bridge conditions predicted by the model.

Original languageEnglish
Pages (from-to)578-586
Number of pages9
JournalAutomation in construction
Issue number5
Publication statusPublished - 2009 Aug

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction


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