ABSTRACT
The four groups ofmethods presented in previous chapters of this book can be classified according to their solution techniques, which are the Frequency and Time Domain Method (FTDM), the Time Domain Method (TDM), the State Space Method (STM) and the Finite Element Method (FEM). If the method of formulation of the moving load problem is considered, they can be grouped into only two categories, namely those based on the Modal Superposition Technique (MST) and those based on the Finite Element Method (FEM). The capability of these methods in the moving force identification has been illustrated with numerical examples (Zhu and Law, 2002c) and experimental studies with the laboratory data (Zhu and Law, 2003a). A comparison between the FTDM and TDM is given in Chapters 5 and 6; of the two TDM methods in Chapter 6; the two types of STMs in Chapter 7 and comparison between the FEM and the EST method in Chapter 9. The Modal Superposition Technique (MST), represented by the Exact Solution
Technique (EST) Method in Chapter 6, has a good accuracy of identification but it demands heavy computation when multiple vehicles cross a multi-span bridge structure. The FEMapproach is flexible when dealing with vehicle axle-loadsmoving on top of a bridge-vehicle system with complex boundary conditions. However a great deal of care must be used in transforming the displacements or strains into velocities and accelerations as the numerical differentiation may lead to large errors in the identified results. The FEM formulated Method, however, has acquired efficient computational capability and good identification accuracy when merged with the orthogonal function smoothing technique to obtain the velocities and accelerations from the measured strains as shown in Chapter 8. Both the FEM formulation and the EST Method have been shown to be able to
identify the bridge-vehicle interaction forces from strains with road surface roughness and vehicle braking on the bridge. The FEM gives consistently smaller error in the results for all noise levels while the accuracy of EST Method is greatly affected by noise. This indicates that the importance of having pre-processing of the measured data to remove the measurement noise before the identification is not over-emphasized. The orthogonal function approximation of the measured strains is also shown to be effective in filtering the high frequency noise components in the responses.
