Application of ultra high performance concrete (UHPC) as a thin-bonded overlay for concrete bridge decks

As transportation infrastructure across the globe approaches the end of its service life, new innovative materials and applications are needed to sustainably repair and prevent damage to these structures. Bridge structures in the United States in particular are at risk as a large percentage will be reaching their design service lives in the coming decades. Superstructure deterioration occurs due to a variety of factors, but a major contributor comes in the form of deteriorating concrete bridge decks. Within a concrete bridge deck system, deterioration mechanisms can include spalling, delaminations, scaling from unsuitable material selection, freeze-thaw damage, and corrosion of reinforcing steel due to infiltration of chloride ions and moisture. This thesis presents findings pertaining to the feasibility of using UHPC as a thin-bonded overlay on concrete bridge decks, specifically in precast bridge deck applications where construction duration and traffic interruption can be minimized, as well as in cast-in-place field applications. UHPC has several properties that make it a desirable material for this application. These properties include post-cracking tensile capacity, high compressive strength, high resistance to environmental and chemical attack, negligible permeability, negligible dry shrinkage when thermally cured, and the ability to self consolidate. The compatibility of this bridge deck overlay system was determined to minimize overlay thickness and dead load without sacrificing bond integrity or lose of protective capabilities. A parametric analysis was conducted using a 3D finite element model of a simply supported bridge under HS-20 truck and overload. Experimental tests were conducted to determine the net effect of UHPC volume change due to restrained shrinkage and tensile creep relaxation. The combined effects from numerical models and test results were then considered in determining the optimum overlay thickness for cast-in-place and precast applications.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

BIAS-CORRECTED MAXIMUM LIKELIHOOD ESTIMATION OF THE PARAMETERS OF THE WEIGHTED LINDLEY DISTRIBUTION

This report discusses the calculation of analytic second-order bias techniques for the maximum likelihood estimates (for short, MLEs) of the unknown parameters of the distribution in quality and reliability analysis. It is well-known that the MLEs are widely used to estimate the unknown parameters of the probability distributions due to their various desirable properties; for example, the MLEs are asymptotically unbiased, consistent, and asymptotically normal. However, many of these properties depend on an extremely large sample sizes. Those properties, such as unbiasedness, may not be valid for small or even moderate sample sizes, which are more practical in real data applications. Therefore, some bias-corrected techniques for the MLEs are desired in practice, especially when the sample size is small. Two commonly used popular techniques to reduce the bias of the MLEs, are ‘preventive’ and ‘corrective’ approaches. They both can reduce the bias of the MLEs to order O(n−2), whereas the ‘preventive’ approach does not have an explicit closed form expression. Consequently, we mainly focus on the ‘corrective’ approach in this report. To illustrate the importance of the bias-correction in practice, we apply the bias-corrected method to two popular lifetime distributions: the inverse Lindley distribution and the weighted Lindley distribution. Numerical studies based on the two distributions show that the considered bias-corrected technique is highly recommended over other commonly used estimators without bias-correction. Therefore, special attention should be paid when we estimate the unknown parameters of the probability distributions under the scenario in which the sample size is small or moderate.