Bell, Michael2017-12-182017-12-182016-05http://hdl.handle.net/10125/51363Ph.D. University of Hawaii at Manoa 2016.Includes bibliographical references.An internal model based method is used to estimate the structural displacements under ambient excitation using only acceleration measurements. Strain measurements are incorporated to expand the method to single span concrete bridges subjected to moving vehicle loads. The structural response is assumed to remain the linear range for the duration of the loading. The excitation is assumed to be with zero mean and relatively broad bandwidth such that at least one of the fundamental modes of the structure is excited and dominates in the response. Using the structural modal parameters and partial knowledge of the load, their respective internal models can be established. These internal models can then be used to form an autonomous state-space representation of the system. It is shown that structural displacements, velocities, and accelerations are the states of such a system, and it is fully observable when the measured output contains structural accelerations only. Reliable estimates of structural displacements are obtained using the standard Kalman filtering technique. These displacement estimates can be used to determine the moment demand and provide insight into whether this demand is exceeding the capacity of the bridge. The effectiveness and robustness of the proposed method has been demonstrated and evaluated via numerical simulations of an eight-story lumped mass model along with a simply supported single span concrete bridge subjected to a moving traffic load. Experimental data of a three-story frame excited by ground accelerations from an actual earthquake record is also used. Lastly, field data from an inverted arch concrete bridge is analyzed as proof of concept for deployment of a structural health monitoring system for the purpose of displacement estimations.engStructural health monitoringbridge deflectionsrecovering displacementsstate-spaceinternal modelKalman filterThesis