Advancing the Capabilities of Adaptive Management Techniques in Geotechnics

Northwestern University, Evanston IL

Investigators

Abstract

Adaptive management techniques provide a means to incorporate advances in sensor development, information technology, and numerical analyses to a variety of problems in geotechnical engineering. If one wants to predict and subsequently evaluate the overall performance of a design, the ?observational method? espoused by Peck is a well-known framework wherein construction and design procedures and details are adjusted based upon observations and measurements made as construction proceeds. Adaptive management techniques allow one to automate the observational approach so that quantitative information can be distributed to interested shareholders in a timely enough fashion to be of use in a number of applications. This methodology has particular application when extending civil infrastructure into the subsurface environment. The increase in population density, as well as new imperatives for infrastructure investment, homeland security, energy conservation and sustainable design, will require major advances in our ability to create underground space more efficiently and safely. Many factors affect the ground movements caused by excavations, including stratigraphy, soil properties, support system details, construction activities, contractual arrangements and workmanship. While numerical simulations have become more common when analyzing ground response to excavations as part of the design process, finite element predictions contain uncertainties related to soil properties, support system details, and construction procedures. These factors are explicitly considered when applying adaptive management methods to the problem, although the methodology has its limitations as subsequently discussed. Stricter limits on allowable ground movements associated with deep excavations are being imposed in many locales by either regulatory agencies or by recognition of adverse effects of excessive ground movements. Excavations in some urban areas are being subjected to movement limitations that are much smaller than even just 5 years ago. For example, excavation-induced ground movements in the Seattle area are limited to 1 inch for cuts at deep as 70 ft. Requirements for excavations in Chicago are now targeted for maximum ground movements of 1-1/2 to 2 inches, down from 4 inches just over a decade ago. These issues are compounded in Chicago by the fact that excavations now are being made to depths of 75 ft, much deeper than the typical depth of 40 ft. Many excavations in the Boston area are limited to 1 inch of ground movement. Given that the subsurface conditions at Boston and Chicago consist of relatively soft clays, these requirements present a challenge in excavation support design and construction. Consequently, the state-of-the-art of predicting ground deformations has reached a point where major advances in practice are required to make accurate design assessments when movements are limited to such small amounts. These advances also are needed to make the adaptive management approach applicable to these types of problems. The purpose of this research is to extend the adaptive management approach so that it applies to a range of problems where ground deformations must be limited to prevent damage to adjacent buildings and other infrastructure. In particular it is proposed to quantify the relative effects of small strain non-linearity of soils, non-linear stiffness of walls and shrinkage of floor slabs used in top-down construction on the deformations associated with excavations. This research will include laboratory experiments on block samples cut from excavations in Chicago and Boston to characterize the constitutive behavior of the soils with emphasis on the small strain responses, detailed field experiments at deep excavations where ground deformation and structural responses of the support system are measured and related to the construction activities at the site, and finite element simulations and after-the-fact adaptive management evaluations using inverse analysis based on observed field observations.