Static-based techniques work under a similar premise as the dynamic-based techniques, onl5 using static excitations and measured responses. In this case a static load is applied to the structure and the response, typically displacement or strain, is measured at one or more locations. Sanayei and Onipede (1991) used a finite element model and measured deflections to determine the stiffness properties of the structure. Banan et al. (1994a,b) developed a framework for identifying parameters in a structure based on static response quantities. The problem was cast as a constrained, nonlinear minimization problem, using either a force-error estimator or a displacement-error estimator. The problem was solved by recursive quadratic programming. The procedure was tested on a 25 member Bowstring truss. Hjelmstad and Shin (1997) used a similar approach along with an adaptive parameter grouping scheme and a data perturbation scheme to address the problems of sparce and noisy data. Sanayei and Saletnik (1996a,b) modified the procedure developed earlier by Sanayei and Onipede (1991) to use static strain measurements instead of displacements. Parameters were estimated by minimizing the error between the theoretical and measured strains in the structure resulting from a series of concentrated forces. Liu and Chian (1997) developed a procedure for identifying the crosssectional areas of a truss using static strain measurements resulting from a series of concentrated forces. A closed-form solution is obtained for the truss. A numerical example is presented along with model test results. Most recently, Chou and Ghaboussi (2001) used a genetic algorithm to identify damage in a truss structure based on measured deflections.
The static-based techniques proposed to date also face practical challenges when applied to large, civil structures. First, it is nearly impossible to apply a controlled lateral load to a large civil structure. This would require a reaction structure of size equal to that of the structure and a tremendous force actuator. Second, some of the techniques mentioned use static deflection measurements in the identification procedure. However, in most cases it is impractical to measure deflections in a large structure, simply because there is not a fixed/absolute reference to make the measurement against.
An alternative method for damage detection in large civil structures has been developed that is aimed at eliminating some of the practical problems associated with the other methods mentioned previously. While the procedure could be used on an "asneeded" basis, it is suited more toward permanent long-term health monitoring of civil structures. The static-based procedure uses the redistribution of dead load in the structure that takes place when damage occurs. The primary load carried by a large civil structure is its own weight; in many cases (take, for example, a large bridge in a nonseismic region) the live load effects are insignificant relative to the dead load effects. Once built, the dead load results in a certain distribution of stress in the structure, which for all intent and purposes should remain constant throughout the life of the structure. However, when damage occurs, whether it occurs gradually or instantaneously, this will result in a redistribution of dead load stress in the structure. At the same time the magnitude of the dead load remains nearly, if not absolutely, constant. The nature and extent of redistribution of the dead load stress in the structure can be used to identify the location and extent of damage in the system.
An overview and discussion of the assumptions for the damage identification procedure is first presented in the paper. The structure considered is a fixed-fixed beam with a single damage location. Next, an analytical model of the damaged beam is developed; closed form solutions are obtained for the response of the beam to a uniformly distributed load. The damage identification procedure is then described. This includes discussion of the genetic optimization algorithm used to minimize the objective function. The methodology is then demonstrated for several dif- ferent test cases. These include damage at different locations, different severities of damage, and a test of the false-positive case.