• An investigation into the effectiveness of simulation-extrapolation for correcting measurement error-induced bias in multilevel models

      Custer, Christopher (2015-04)
      This paper is an investigation into correcting the bias introduced by measurement errors into multilevel models. The proposed method for this correction is simulation-extrapolation (SIMEX). The paper begins with a detailed discussion of measurement error and its effects on parameter estimation. We then describe the simulation-extrapolation method and how it corrects for the bias introduced by the measurement error. Multilevel models and their corresponding parameters are also defined before performing a simulation. The simulation involves estimating the multilevel model parameters using our true explanatory variables, the observed measurement error variables, and two different SIMEX techniques. The estimates obtained from our true explanatory values were used as a baseline for comparing the effectiveness of the SIMEX method for correcting bias. From these results, we were able to determine that the SIMEX was very effective in correcting the bias in estimates of the fixed effects parameters and often provided estimates that were not significantly different than those from the estimates derived using the true explanatory variables. The simulation also suggested that the SIMEX approach was effective in correcting bias for the random slope variance estimates, but not for the random intercept variance estimates. Using the simulation results as a guideline, we then applied the SIMEX approach to an orthodontics dataset to illustrate the application of SIMEX to real data.
    • Structural health monitoring of Klehini River bridge

      Xiao, Feng (2012-08)
      The objective of the research is to improve the safety of bridge structures in the state of Alaska through implementation of innovative structural health monitoring (SHM) technologies. The idea is to evaluate structural integrity and serviceability, and to provide reliable information for changing structural response, etc. of monitored bridges. Based on the finite element model's moving load analysis, modal analysis results and field inspection, this study was used to establish a bridge SHM system for a particular bridge including a preferred sensor layout, system integrator and instrumentation suitable for Alaska's remote locations with harsh weather. A variety of sensors were proposed to measure and monitor structural and environmental conditions to assist in the evaluation of the performance of the Klehini River Bridge. This system is able to provide more reliable information on the real structural health condition. It can be used to improve safe performance of this bridge. As a new safety and management tool, this SHM system will complement traditional bridge inspection methods. Implementation of an effective monitoring system will likely result in a reduction in inspection manpower, early detection of deterioration/damage, development of optimum inspection cycle and repair schedules before deterioration/damage grows to a condition where major repairs are required.