This research investigates the performance of an existing structural damage detection method (SDIM) when only experimentally-obtained measurement information can be used to calculate the frequency response functions used to detect damage. The development of a SDIM that can accurately identify damage while processing measurements containing realistic noise levels and overcoming experimental modeling errors would provide a robust method for identifying damage in the larger, more complex structures found in practice. The existing SDIM program, GaDamDet, uses an advanced genetic algorithm, along with a two-dimensional finite element model of the structure, to identify the location and the severity of damage using the linear vibration information contained in frequency response functions (FRF) as response signatures. Datagen is a Matlab program that simulates the three-dimensional dynamic response of the four-story, two-bay by two-bay UBC test structure built at the University of British Columbia. The dynamic response of the structure can be obtained for a range of preset damage cases or for any user-defined damage case. Datagen can be used to provide the FRF measurement information for the three-dimensional test structure. Therefore, using the FRF measurements obtained from the UBC test structure allows for a more realistic evaluation of the performance of the SDIM provided by GaDamDet as the impact on performance of more realistic noise and model errors can be investigated. Previous studies evaluated the performance of the SDIM using only simulated FRF measurements obtained from a two-dimensional structural model. In addition, the disparity between the two-dimensional model used by the SDIM used to identify damage and the measurements obtained from the three-dimensional test structure is analyzed. The research results indicate that the SDIM is able to accurately detect structural damage to individually damaged members or to within a damaged floor, with few false damages identified. The SDIM provides an easy to use, visual, and accurate algorithm and its performance compares favorably to performance of the various damage detection algorithms that have been proposed by researchers to detect damage in the three-dimensional structural benchmark problem.

This research investigates the performance of an existing structural damage detection method (SDIM) when only experimentally-obtained measurement information can be used to calculate the frequency response functions used to detect damage. The development of a SDIM that can accurately identify damage while processing measurements containing realistic noise levels and overcoming experimental modeling errors would provide a robust method for identifying damage in the larger, more complex structures found in practice. The existing SDIM program, GaDamDet, uses an advanced genetic algorithm, along with a two-dimensional finite element model of the structure, to identify the location and the severity of damage using the linear vibration information contained in frequency response functions (FRF) as response signatures. Datagen is a Matlab program that simulates the three-dimensional dynamic response of the four-story, two-bay by two-bay UBC test structure built at the University of British Columbia. The dynamic response of the structure can be obtained for a range of preset damage cases or for any user-defined damage case. Datagen can be used to provide the FRF measurement information for the three-dimensional test structure. Therefore, using the FRF measurements obtained from the UBC test structure allows for a more realistic evaluation of the performance of the SDIM provided by GaDamDet as the impact on performance of more realistic noise and model errors can be investigated. Previous studies evaluated the performance of the SDIM using only simulated FRF measurements obtained from a two-dimensional structural model. In addition, the disparity between the two-dimensional model used by the SDIM used to identify damage and the measurements obtained from the three-dimensional test structure is analyzed. The research results indicate that the SDIM is able to accurately detect structural damage to individually damaged members or to within a damaged floor, with few false damages identified. The SDIM provides an easy to use, visual, and accurate algorithm and its performance compares favorably to performance of the various damage detection algorithms that have been proposed by researchers to detect damage in the three-dimensional structural benchmark problem.

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Basal area and dominant height growth and survival models incorporating general response to silvicultural treatments for loblolly pine stands were developed using data from various silvicultural experiments across Southern United States. Growth models for treated stands were developed by multiplying base-line growth models with modifier response functions/multipliers accounting for effects of thinning, fertilization, and control of competing vegetation. Chapman-Richards functions were used to model the base-line growth. Separate response functions to mid-rotation thinning and fertilization effects were developed. The thinning response function was based on duration and rate parameters and is sensitive to stand age at the time of thinning, time since thinning, and intensity of thinning. The fertilization response functions were based on Weibull distribution and the magnitude of responses varies with time since application of fertilizers, type of fertilizer elements applied, and rate of application. Response functions were integrated as a multiplier to base-line models. Response to early control of competing vegetation was incorporated into base-line models through multiplier factors. Multiplier factors were calculated based on growth difference between treated and untreated stands. A difference function, derived from differential equation with age, initial stand density, and site index served as the base-line survival model. The survival model was adjusted for thinning treatment by including an additional independent variable that represents thinning intensity. No adjustment was required for survival model in response to fertilization and competing vegetation control. All growth models were unbiased and had adequate performance in predicting basal area and dominant height following treatments. Models were developed to represent general growth trends in response to treatments. The response functions developed here can be viewed as general response functions. / Ph. D.

Rapid increases in consumer food price beginning in 2007 generated interest in identifying the main factors influencing these increases. In subsequent years, food prices have fluctuated, but generally have continued their ascent. The effects of crude oil, gasoline, corn, and ethanol prices, as well as, the relative foreign exchange rate of the U.S. dollar and producer price indexes for food manufacturing and fuel products on domestic food prices are examined. Because the data series are non-stationary and cointegrated, a vector error correction model is estimated. Weak exogeneity and exclusion tests in the cointegration space are performed. Directed acyclical graphs are used to specify contemporaneous causal relationships. Dynamic interactions among the series are given by impulse response functions and forecast error variance decompositions. Weak exogeneity tests indicate all eight series work to bring the system back into equilibrium following a shock to the system. Further, exclusion tests suggest crude oil, gasoline, food CPI, ethanol, and food PPI variables are not in the long-run relationships. Dynamic analyses suggest the following relationships. Ethanol price is not a major factor in domestic food prices, suggesting that food prices are largely unaffected by the recent increased use of corn-based ethanol for fuel. Crude oil prices, corn prices, and the relative foreign exchange rate of the U.S. dollar, however, do influence domestic food prices with corn price contributing the most to food price variability. Innovation accounting inferences are robust to potential different contemporaneous causal specifications.

No / The relationship between exports and economic growth has been analysed by a number of recent empirical studies. This paper re-examines the sources of growth for the period 1971-2001 for India. It builds upon Feder's (1983) model to investigate empirically the relationship between export growth and GDP growth (the export led growth hypothesis), using recent data from the Reserve Bank of India, and by focusing on GDP growth and GDP growth net of exports. We investigate the following hypotheses: (i) whether exports, imports and GDP are cointegrated using the Johansen approach and Breitung's nonparametric cointegration test; (ii) whether export growth Granger causes GDP growth; (iii) and whether export growth Granger causes investment. Finally, a VAR is constructed and impulse response functions (IRFs) are employed to investigate the effects of macroeconomic shocks.

To adequately evaluate risk associated hurricane flooding, numerous surge events must be considered, and the cost associated with high resolution numerical modeling for several storms is excessive. The Joint Probability Method with Optimal Sampling (JPM-OS) has been recently shown to be a reliable method in estimating extreme value probabilities of hurricane flooding – it relies heavily on a hurricane surge matrix comprised of surge values from several hurricane scenarios (with varying meteorological and climate change characteristics). Surge Response Functions (SRFs) are physics-based equations developed using scaling laws to adequately scale surge response in dimensionless space; they serve as surrogates to high resolution numerical models in estimating hurricane peak surge to populate the JPM-OS surge matrix. Research presented in this dissertation is primarily focused on the development of dimensionless formulations using physics-based scaling laws to account for the contribution of forward speed (v_f), approach angle (theta) and Sea Level Rise (SLR). These parameters are incorporated into pre-existing SRFs for open coast locations and bays. For the bays, in addition to accounting for the effects of v_f and theta in the SRFs, a new dimensionless formulation for the influence of storm size (R_p) is included in the SRFs.   To account for the influence of v_f in the SRFs, the dimensionless formulations primarily consist of the time it takes for surge to build up (over the shelf, for open coast SRFs and within the bays, for bay SRFs). The formulation for the influence of theta primarily accounts for the rotation of the hurricane wind field as the storm makes landfall. For the influence of R_p in the bays, the new formulation scales R_p with the farthest distance through which water mass will move inside the bay, from its center of gravity. A simple correction based on a linear model is derived to account for the influence of SLR on surge response at open coast locations and in bays. The developed dimensionless formulations for v_f and theta (and R_p for bay SRFs) are incorporated into the SRFs to obtain revised versions of the response functions. For open coast locations, the revised SRFs estimate peak surge with an increased accuracy (based on root-mean-square errors of modeled versus SRF-estimated peak surge) of up to 12.5% reduction in root-mean-square errors. In addition, the new formulations improve the predictions of 65% of surge events of 2 m or greater. For the bays, the revised SRFs reduce the root-mean-square errors (by up to 54% in Matagorda Bay), when compared to the previous formulation. These results indicate that the new formulations, which include v_f and tehta (and R_p for bay SRFs), significantly improve the accuracy of the SRFs. Application of the revised open coast SRFs to the JPM-OS framework shows only minor impacts of v_f and theta variation on surge versus return period curves (about 5.2% maximum increase in surge for theta varying from -80 degrees to +80 degrees, and a maximum of 6.7% for fvvarying from 1.54 m/s to 10.8 m/s). Climate change parameters however show a much more significant impact on the surge versus return period curves. SLR variation from 0.5 m to 2.0 m yields a maximum of 42.4% increase in surge, while hurricane intensification from 0.5 degrees C to 1.5 degrees C yields an increase of up to 11.3% in surge.

The use of organic scintillators is an established method for the measurement of neutron spectra above several hundred keV. Fast neutrons are detected largely by proton recoils in the scintillator resulting from neutron elastic scattering with hydrogen. This leads to a smeared rectangular pulse-height distribution for monoenergetic neutrons. The recoil proton distribution ranges in energy from zero to the incident neutron energy. In addition, the pulse-height distribution is further complicated by structure due to energy deposition from alpha particle recoils from interactions with carbon as well as carbon recoils themselves. In order to reconstruct the incident neutron spectrum, the pulse-height spectrum has to be deconvoluted (unfolded) using the computed or measured response of the scintillator to monoenergetic neutrons. In addition gamma rays, which are always present when neutrons are present, lead to Compton electron recoils in the scintillator. Fortunately, for certain organic scintillators, the electron recoil events can be separated from the heavier particle recoil events in turn to distinguish gamma-ray induced events from neutron-induced events. This is accomplished by using the risetime of the pulse from the organic scintillator seen in the photomultiplier tube as a decay of light. In this work, an organic scintillator detection system was assembled which includes neutron-gamma separation capabilities to store the neutron-induced and gamma-induced recoil spectra separately. An unfolding code was implemented to deconvolute the spectra into neutron and gamma energy spectra. In order to verify the performance of the system, a measurement of two reference neutron fields will be performed with the system, unmoderated Cf-252 and heavy-water moderated Cf-252. After the detection system has been verified, measurements will be made with an AmBe neutron source.

Trade deficit, financial deficit, and current account deficit of the United States have all been problems deeply concerned by economists and politicians in recent decades. Since the third season of 2000, a recession of the United States and the whole world has gradually started to appear. In addition, as a result of the 9/11 terrorist attacks and the war in Iraq the stock market has begun to decline significantly. In order to promote the recovery of its economy, the federal government determines to adopt the expanded financial policy which will most likely in the end cause its financial deficit more serious. The main purpose of this paper is to investigate the factors that influence the current account deficit of the United States. Because the study considers foreign variables that related researches ignore, we choose five variables as follows: regional output differential, regional interest rate differential, terms of trade, regional real effective exchange rate, and current account. Therefore, we adopt the Unit Root Test, the Granger Causality Test, the Co-integrating Test, and SVAR (Structural Vector Autoregressive) model to run RATS and E-views. It is the finding of empirical result that the United States government considers terms of trade and current account that can't be quantized of the first importance rather than the exchange rate factor that general research is thought. This is one of the contributions of the study.