A Study on the Stress Histories and the Fatigue Life of Highway Bridges
Author: Constantine G. Caracostis
Publisher:
Published: 1972
Total Pages: 146
ISBN-13:
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Author: Constantine G. Caracostis
Publisher:
Published: 1972
Total Pages: 146
ISBN-13:
DOWNLOAD EBOOKAuthor: William Hamilton Walker
Publisher:
Published: 1980
Total Pages: 220
ISBN-13:
DOWNLOAD EBOOKAuthor: William Hamilton Walker
Publisher:
Published: 1980
Total Pages: 236
ISBN-13:
DOWNLOAD EBOOKAuthor: Constantine G. Caracostis
Publisher:
Published: 1972
Total Pages: 292
ISBN-13:
DOWNLOAD EBOOKAuthor: William Hamilton Walker
Publisher:
Published: 1978
Total Pages: 206
ISBN-13:
DOWNLOAD EBOOKAuthor:
Publisher:
Published: 1972
Total Pages: 444
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DOWNLOAD EBOOKAuthor: Egbert Hsi-Ting Chang
Publisher:
Published: 1971
Total Pages: 294
ISBN-13:
DOWNLOAD EBOOKAuthor:
Publisher: Highway Research Board
Published: 1972
Total Pages: 76
ISBN-13:
DOWNLOAD EBOOKAuthor: William Hamilton Walker
Publisher:
Published: 1978
Total Pages:
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DOWNLOAD EBOOKAuthor: Nicolas Gagarine
Publisher:
Published: 1992
Total Pages: 104
ISBN-13:
DOWNLOAD EBOOKThe two main objectives of the present study were to: (1) demonstrate the advantages of using the Weigh-in-Motion and Response (WIM+R) system to evaluate the fatigue life of existing bridges and (2) introduce pattern recognition methods in the analysis of WIM+R data. Four steel girder bridges were instrumented to obtain strain data at fatigue critical details, and at sections of maximum strain to compute the gross vehicle weight (GVW) of each truck. Two were simple spans, and two continuous spans. A comparative study of three of the four alternatives suggested by AASHTO showed that the fatigue life computed with direct measurements of the stress ranges were greater than those computed with the simplified approaches. The effect of secondary cycles was negligible for the four bridges. The damage equivalent secondary cycle factor for fatigue was defined. The applicability of three pattern recognition methods for WIM+R was investigated. The dynamic time warping, hidden Markov model, and feed forward neural network methods can classify trucks with the measured strain patterns alone.