Inverse analysis techniques for parameter identification in simulation of excavation support systems

Two numerical procedures are described that quantitatively identify a set of constitutive parameters that best represents observed ground movement data associated with deep excavations in urban environments. This inverse problem is solved by minimizing an objective (or error) function of the weighted least-squares type that contains the difference between observed and calculated ground displacements. The problem is solved with two different minimization algorithms, one based on a gradient method and the other on a genetic algorithm. The objective function is shown to be smooth with a unique solution. Both methods are applied to lateral movements from synthetic and real excavations to illustrate various aspects of the implementation of the methods. The advantages and disadvantages of each method applied to excavation problems are discussed.