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Loblolly pine is a commonly planted pine species in the Southern US which is intensively managed as well as a major contributor to the timber industry. Various silvicultural treatments are commonly applied to pine plantations including thinning and sometimes pruning. Tree crowns contain the active photosynthesis region and play a vital role in tree growth. Among various tree crown measurements, live crown ratio (LCR) is derived from height to live crown base (HLCB) and total tree height. Accurate measurement of HLCB is basis for live crown ratio prediction. Due to numerous definitions and practical considerations, HLCB and crown structure are difficult and slow to measure accurately. Despite this, LCR is a useful predictor in various growth and yield models. Due to the challenges in measuring tree crowns, accurate live crown ratio prediction models are useful. The LCR model of (Dyer and Burkhart, 1987) was refit with intensively managed plantation (IMP) data. The parameters were significant, and the residual plots showed no concerning patterns but the prediction of height to live crown base for pruned trees was not logical as it sometimes predicted HLCB lower than pruning height. To address this, the base model was modified to accommodate the pruning effect and provide logical predictions. LCR is in range of 0 - 1 and HLCB is greater or equal to pruning height. If trees are not pruned, it reverts to the original model. The models were validated with a dataset of IMP measurements not used in fitting. Validation statistics suggest the model performs nearly as well as the original, unconstrained base model. It is expected that the new model will be useful for forest managers to predict LCR of both pruned and unpruned trees. The second part of the study is to understand the importance of common tree variables in predicting the lumber recovery in planted loblolly pine. A random forest model was used to determine the variable importance of DBH, total tree height and live crown ratio for total board ft., high-grade, and high-grade lumber proportion compared to total board ft. DBH ranked at the first position followed by total tree height and live crown ratio similarly ranked for volume and high-grade lumber volume. For proportions of high-grade lumber, tree height was at top rank followed by LCR, and DBH. However, the effect of these variables for lumber recovery was not explored. It is suggested that future work can explore parametric model forms for accurately predicting lumber recovery using simple, easy to measure tree variables. / Master of Science / Loblolly pine plantations are found in large areas of southern United States. Significant investments are made for maximizing wood production and economic benefits. The growth of trees is largely governed by its crown which is the green foliage found in upper parts of tree. Planted trees are often thinned to provide crown sufficient light for improved growth. Tree crowns are sometimes pruned to reduce knot size and improve lumber quality. If we can understand how these activities affected the crown length of trees, we can manage our plantation accordingly. With this motivation, the live crown ratio which is the ratio of length of live crown and total tree height was modeled. An existing live crown ratio model for planted loblolly pine trees was modified to provide logical predictions for pruned trees and was updated with newer data. Simple tree measurements like tree diameter, height and age were used in the model. Common tree variables were used because LCR is derived from height to live crown base which is difficult to measure in field. The new model will be useful to forest managers for predicting the growth of loblolly pine plantations subjected to pruning. Similarly, an attempt is made to explore the various standing tree characteristics affecting the amount of lumber that can be obtained. This will assist in understanding the lumber grade by using common tree measurements and value of a stand from lumber production side can be known.

A semiparametric proportional likelihood ratio model was proposed by Luo and Tsai (2012) which is suitable for modeling a nonlinear monotonic relationship between the response variable and a covariate. Extending the generalized linear model, this model leaves the probability distribution unspecified but estimates it from the data. In this thesis, we propose to extend this model into analyzing the longitudinal data by incorporating random effects into the linear predictor. By using this model as the conditional density of the response variable given the random effects, we present a maximum likelihood approach for model estimation and inference. Two numerical estimation procedures were developed for response variables with finite support, one based on the Newton-Raphson algorithm and the other one based on generalized expectation maximization (GEM) algorithm. In both estimation procedures, Gauss-Hermite quadrature is employed to approximate the integrals. Upon convergence, the observed information matrix is estimated through the second-order numerical differentiation of the log likelihood function. Asymptotic properties of the maximum likelihood estimator are established under certain regularity conditions and simulation studies are conducted to assess its finite sample properties and compare the proposed model to the generalized linear mixed model. The proposed method is illustrated in an analysis of data from a multi-site observational study of prodromal Huntington's disease.

ABSTRACT A LABORATORY MODEL STUDY ON SETTLEMENT REDUCTION Effect OF STONE COLUMNS IN SOFT CLAY S&uuml / nnetcioglu, Mehmet Emrah M.Sc., Department of Civil Engineering Supervisor: Prof. Dr. Mehmet Ufuk Ergun August 2012, 177 pages An experimental study was conducted in order to examine settlement reduction ratios of footing supported by both floating and end bearing type of stone columns. For the floating types, tests were done with varying column lengths of one and two widths of footing (L=B,2B). Tests were conducted in 200 mm* 200 mm* 200 mm cubic loading tanks. The reinforcement effect was achieved by the installation of four stone columns with 20 mm diameter under 70 mm* 70mm model footing. Parameters such as area replacement ratio (a_s), loading plate dimensions, consolidation and vertical pressures applied, and the relative density (D_R) of the granular column were kept constant, the column length (L) was set as the only variable in the experimental tests conducted. In the tests, footing settlements together with subsurface settlements at depths equal to footing width (B) and two times the footing width (2B) were measured by specially designed telltales. The settlement reduction ratios both at surface and subsurface were evaluated in order to determine the effect of column length on settlement improvement. It has been found out that as the column length increases the settlement reduction ratios decrease for all depth intervals. However, there exists a threshold column length (L=2B), beyond which the composite ground demonstrates little settlement improvement.

The dynamics around managing urban infrastructure are changing dramatically. Today???s infrastructure management challenges ??? in the wake of shrinking coffers and stricter stakeholders??? requirements ??? include finding better condition assessment tools and prediction models, and effective and intelligent use of hard-earn data to ensure the sustainability of urban infrastructure systems. Wastewater collection networks ??? an important and critical component of urban infrastructure ??? have been neglected, and as a result, municipalities in North America and other parts of the world have accrued significant liabilities and infrastructure deficits. To reduce cost of ownership, to cope with heighten accountability, and to provide reliable and sustainable service, these systems need to be managed in an effective and intelligent manner. The overall objective of this research is to present a new strategic management framework and related tools to support multi-perspective maintenance, rehabilitation and replacement (M, R&R) planning for wastewater collection networks. The principal objectives of this research include: (1) Developing a comprehensive wastewater collection network asset database consisting of high quality condition assessment data to support the work presented in this thesis, as well as, the future research in this area. (2) Proposing a framework and related system to aggregate heterogeneous data from municipal wastewater collection networks to develop better understanding of their historical and future performance. (3) Developing statistical models to understand the deterioration of wastewater pipelines. (4) To investigate how strategic management principles and theories can be applied to effectively manage wastewater collection networks, and propose a new management framework and related system. (5) Demonstrating the application of strategic management framework and economic principles along with the proposed deterioration model to develop long-term financial sustainability plans for wastewater collection networks. A relational database application, WatBAMS (Waterloo Buried Asset Management System), consisting of high quality data from the City of Niagara Falls wastewater collection system is developed. The wastewater pipelines??? inspections were completed using a relatively new Side Scanner and Evaluation Technology camera that has advantages over the traditional Closed Circuit Television cameras. Appropriate quality assurance and quality control procedures were developed and adopted to capture, store and analyze the condition assessment data. To aggregate heterogeneous data from municipal wastewater collection systems, a data integration framework based on data warehousing approach is proposed. A prototype application, BAMS (Buried Asset Management System), based on XML technologies and specifications shows implementation of the proposed framework. Using wastewater pipelines condition assessment data from the City of Niagara Falls wastewater collection network, the limitations of ordinary and binary logistic regression methodologies for deterioration modeling of wastewater pipelines are demonstrated. Two new empirical models based on ordinal regression modeling technique are proposed. A new multi-perspective ??? that is, operational/technical, social/political, regulatory, and finance ??? strategic management framework based on modified balanced-scorecard model is developed. The proposed framework is based on the findings of the first Canadian National Asset Management workshop held in Hamilton, Ontario in 2007. The application of balanced-scorecard model along with additional management tools, such as strategy maps, dashboard reports and business intelligence applications, is presented using data from the City of Niagara Falls. Using economic principles and example management scenarios, application of Monte Carlo simulation technique along with the proposed deterioration model is presented to forecast financial requirements for long-term M, R&R plans for wastewater collection networks. A myriad of asset management systems and frameworks were found for transportation infrastructure. However, to date few efforts have been concentrated on understanding the performance behaviour of wastewater collection systems, and developing effective and intelligent M, R&R strategies. Incomplete inventories, and scarcity and poor quality of existing datasets on wastewater collection systems were found to be critical and limiting issues in conducting research in this field. It was found that the existing deterioration models either violated model assumptions or assumptions could not be verified due to limited and questionable quality data. The degradation of Reinforced Concrete pipes was found to be affected by age, whereas, for Vitrified Clay pipes, the degradation was not age dependent. The results of financial simulation model show that the City of Niagara Falls can save millions of dollars, in the long-term, by following a pro-active M, R&R strategy. The work presented in this thesis provides an insight into how an effective and intelligent management system can be developed for wastewater collection networks. The proposed framework and related system will lead to the sustainability of wastewater collection networks and assist municipal public works departments to proactively manage their wastewater collection networks.

The dynamics around managing urban infrastructure are changing dramatically. Today’s infrastructure management challenges – in the wake of shrinking coffers and stricter stakeholders’ requirements – include finding better condition assessment tools and prediction models, and effective and intelligent use of hard-earn data to ensure the sustainability of urban infrastructure systems. Wastewater collection networks – an important and critical component of urban infrastructure – have been neglected, and as a result, municipalities in North America and other parts of the world have accrued significant liabilities and infrastructure deficits. To reduce cost of ownership, to cope with heighten accountability, and to provide reliable and sustainable service, these systems need to be managed in an effective and intelligent manner. The overall objective of this research is to present a new strategic management framework and related tools to support multi-perspective maintenance, rehabilitation and replacement (M, R&R) planning for wastewater collection networks. The principal objectives of this research include: (1) Developing a comprehensive wastewater collection network asset database consisting of high quality condition assessment data to support the work presented in this thesis, as well as, the future research in this area. (2) Proposing a framework and related system to aggregate heterogeneous data from municipal wastewater collection networks to develop better understanding of their historical and future performance. (3) Developing statistical models to understand the deterioration of wastewater pipelines. (4) To investigate how strategic management principles and theories can be applied to effectively manage wastewater collection networks, and propose a new management framework and related system. (5) Demonstrating the application of strategic management framework and economic principles along with the proposed deterioration model to develop long-term financial sustainability plans for wastewater collection networks. A relational database application, WatBAMS (Waterloo Buried Asset Management System), consisting of high quality data from the City of Niagara Falls wastewater collection system is developed. The wastewater pipelines’ inspections were completed using a relatively new Side Scanner and Evaluation Technology camera that has advantages over the traditional Closed Circuit Television cameras. Appropriate quality assurance and quality control procedures were developed and adopted to capture, store and analyze the condition assessment data. To aggregate heterogeneous data from municipal wastewater collection systems, a data integration framework based on data warehousing approach is proposed. A prototype application, BAMS (Buried Asset Management System), based on XML technologies and specifications shows implementation of the proposed framework. Using wastewater pipelines condition assessment data from the City of Niagara Falls wastewater collection network, the limitations of ordinary and binary logistic regression methodologies for deterioration modeling of wastewater pipelines are demonstrated. Two new empirical models based on ordinal regression modeling technique are proposed. A new multi-perspective – that is, operational/technical, social/political, regulatory, and finance – strategic management framework based on modified balanced-scorecard model is developed. The proposed framework is based on the findings of the first Canadian National Asset Management workshop held in Hamilton, Ontario in 2007. The application of balanced-scorecard model along with additional management tools, such as strategy maps, dashboard reports and business intelligence applications, is presented using data from the City of Niagara Falls. Using economic principles and example management scenarios, application of Monte Carlo simulation technique along with the proposed deterioration model is presented to forecast financial requirements for long-term M, R&R plans for wastewater collection networks. A myriad of asset management systems and frameworks were found for transportation infrastructure. However, to date few efforts have been concentrated on understanding the performance behaviour of wastewater collection systems, and developing effective and intelligent M, R&R strategies. Incomplete inventories, and scarcity and poor quality of existing datasets on wastewater collection systems were found to be critical and limiting issues in conducting research in this field. It was found that the existing deterioration models either violated model assumptions or assumptions could not be verified due to limited and questionable quality data. The degradation of Reinforced Concrete pipes was found to be affected by age, whereas, for Vitrified Clay pipes, the degradation was not age dependent. The results of financial simulation model show that the City of Niagara Falls can save millions of dollars, in the long-term, by following a pro-active M, R&R strategy. The work presented in this thesis provides an insight into how an effective and intelligent management system can be developed for wastewater collection networks. The proposed framework and related system will lead to the sustainability of wastewater collection networks and assist municipal public works departments to proactively manage their wastewater collection networks.