The authors employ fuzzy regression to investigate the modeling uncertainty in the prediction of bridge pier scour. Fuzzy bias factors, which describe the bias between observed field data and scour estimates based on equations developed from laboratory tests, were estimated. The use of small-scale laboratory data to model large-scale, real-world problems creates the bias. Fuzzy regression is a method of calibrating fuzzy numerical coefficients in a linear equation. Because the regression coefficients are fuzzy parameters, the output, in this case scour depth, is also a fuzzy number. The fuzzy bias factors derived from the fuzzy regression equations are compared for a variety of input data. The fuzzy bias factor offers practical information to engineers in the application of bridge pier scour equations currently available. The results are an appropriate guide for experimentalists in the interpretation of small-scale laboratory test results and for engineers in adjusting scour estimates.


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  • Accession Number: 00716558
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 29 1996 12:00AM