Advances in Weigh-in-Motion Using Pattern Recognition and Prediction of FatigueLife of Highway Bridges. Volume 1. Final Report

摘要

The two main objectives of the present study were to: (1) demonstrate theadvantages 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. The volume is the first in a series of two.