In inverse modeling, a model is calibrated by iteratively changing the estimates of its input parameters until the value of an objective function, which quantifies the errors between observed data and computed results, is minimized. This book (the Author's PhD Thesis, defended in 2002 at Northwestern University) presents a numerical procedure that improves the state-of-the-practice of controlling ground movements associated with excavations. The methodology, developed and tested using data from a deep excavation through Chicago glacial clays, shows that: inverse analysis based on field monitoring data can be effectively used to update the predicted performance of the supported excavation system, and successful recalibration of the model at an early construction stage positively affects subsequent "predictions" of the soil behavior. Results of a number of studies, conducted to evaluate the effect of modeling assumptions on the inverse analysis, indicate that the three main characteristics of a "well-posed" inverse analysis problem are: effective numerical modeling, acceptable parameterization and appropriate choice of observations.