Title page for ETD etd-03022009-084850


Type of Document Dissertation
Author Deng, Lu
Author's Email Address ldeng1@lsu.edu,hustdilu@yahoo.com.cn
URN etd-03022009-084850
Title System Identification of Bridge and Vehicle Based on their Coupled Vibration
Degree Doctor of Philosophy (Ph.D.)
Department Civil & Environmental Engineering
Advisory Committee
Advisor Name Title
Steve C.S. Cai Committee Chair
Ayman Okeil Committee Member
Michele Barbato Committee Member
Muhammad A. Wahab Committee Member
Paul A. LaRock Dean's Representative
Keywords
  • bridge dynamic response
  • vibration
  • bridge-vehicle coupled system
  • axle load
  • parameter
  • identification
  • impact factor
Date of Defense 2009-02-27
Availability unrestricted
Abstract
Most current techniques used for system identification of bridges and vehicles are static-test-based methods. Methodologies that can use bridge dynamic responses or modal information are highly desirable and under development. This dissertation aims to develop new identification methodologies for bridge-vehicle systems using the bridge dynamic responses and modal information.

A new bridge model updating method using the response surface method (RSM) was proposed in this dissertation. The RSM was used to design experiments in order to find out the relationships between the bridge responses and parameters to be updated. Results from numerical simulations and a field study show that the proposed methodology can effectively update bridge models with reasonable explanations available.

A new methodology of identifying dynamic vehicle wheel loads was developed using only the measured bridge responses. The proposed methodology has demonstrated its ability to successfully identify dynamic vehicle loads by both numerical simulations and field tests conducted. This methodology can be used to improve the existing weigh-in-motion techniques which usually require slow vehicle movement or good road surface conditions.

A new methodology of identifying the parameters of vehicles traveling on bridges was proposed in this dissertation. The proposed methodology uses the genetic algorithm to search the optimal vehicle parameter values in order to produce satisfactory agreements between the measured bridge responses and predicted bridge responses from the identified vehicle parameters. This methodology can also be used to improve the existing weigh-in-motion techniques with the ability to identify the static axle weights of vehicles.

The dynamic impact factors for multi-girder concrete bridges were investigated in this dissertation. Relationships between the dynamic impact factor and bridge length, vehicle velocity, and road surface condition were investigated. Statistical properties of the impact factor were obtained. Simple expressions for dynamic impact factor were proposed, which can be used as modifications to the LRFD code regarding short bridges and bridges with poor road surface conditions.

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