Browsing by Subject "Statistical analysis"

Now showing 1 - 4 of 4
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    Determining Economic Strategies for Repair and Replacement of Low Slump Overlays of Bridge Decks
    (Minnesota Department of Transportation, Research Services Section, 2007-05) Zimmerman, Justin; Olson, Steven; Schultz, Arturo
    In the interest of providing tools for the cost-effective maintenance of an aging inventory of bridges, a method for comparing feasible repair/replacement sequences for low-slump concrete overlays for bridge decks is developed. The method relies on a technique for computing deterioration curves using inspection data from the National Bridge Inventory. Over twenty years of inspection data for bridge decks in Minnesota, which were overlaid with low-slump concrete overlays placed between 1974 and 1981, was used. The deterioration curves were assumed dependent on several material and geometric variables identified by means of a literature review, and the statistical significance of these parameters on deterioration rates was examined. These variables include span length, average daily traffic, and superstructure material type, and piecewise linear deterioration curves were constructed for various subgroups with similar deterioration characteristics. Present value cost analysis was used to price the available options by identifying the sequence of repairs that has the least cost while maintaining a specified performance measure. The present value analysis considers the costs and timing of repair/replacement sequences, inflation, and the discount rate.
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    Pedestrian and Bicycle Crash Risk and Equity: Implications for Street Improvement Projects
    (2019-06) Lindsey, Greg; Tao, Tao; Wang, Jueyu; Cao, Jason
    Transportation managers need information about crash risk and equity to prioritize investments in street networks. This case study uses data from Minneapolis, Minnesota, to illustrate how estimates of pedestrian and bicycle crash risk and assessments of inequities in the distribution of that risk can inform prioritization of street improvement projects. Crash numbers and frequencies for pedestrian and bicycle crashes at intersections and mid-blocks in Minneapolis are determined for the 2005-2017 period. New models of pedestrian and bicycle crash risk at both intersections and mid-blocks that control for exposure are introduced and used to predict crashes at all intersections and mid-blocks in the city. Statistical tests are used to assess the equity of distribution of estimated crash risk between areas of concentrated poverty with majority-minority populations and other areas in the city. Crash indexes based on predicted crashes are used to illustrate how increased emphases can be placed on pedestrian and bicycle safety in street improvement rankings. Results show that pedestrian and bicycle crash risk is correlated with exposure, that different factors affect crash risk at intersections and mid-blocks, and that these factors differ for pedestrian and bicycle crashes. Results also show that mean crash risk is higher in neighborhoods with lower incomes and majority-minority populations. For street improvement projects in the city, different rankings result when segments are ranked according to modeled pedestrian and bicycle crash risk in addition to total crash rates based on historical numbers of crashes at particular locations. Results generally affirm efforts by the Minneapolis Department of Public Works to increase emphases on pedestrian and bicycle safety and equity in its prioritization of street improvements.
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    Statistical Analysis of Moose Habitat Behaviors Using Bayesian Hierarchical Model with Spatially Varying Coefficients
    (2017-06) Kroc, Matej
    In the past few years interest in statistical modeling has rapidly increased for scientists in many different fields. With new technologies and the ability to collect larger amounts of data they sought a tool which would help them to get a better understanding, and eventually, prediction of behavior of subjects in their range of study. For biologists and ecologists habitat data is necessary to develop effective conservation and management strategies, and help determine what is behind the change in the population of different species. Our research is focused on the moose habitat behavior statistics. Moose, Alces alces, are the largest of all deer species. Male moose are recognizable by their huge antlers, which can spread up to 6 feet wide. Because of their tall body, they prefer to browse higher shrubs and their typical habitat is a dense mixed boreal forest in North America, including the northern United States, Canada, Alaska, and in Scandinavia and Russia. Despite their large bodies, moose are good swimmers and are often seen in lakes and rivers feeding on aquatic plants both at and below the surface. One of the reasons why moose habitat behavior is the subject of study by many biologists is recent changes in population in North America. Since the 1990's, the moose population in northern Minnesota has decline significantly. Based on a moose population survey from 2017, the population in northeastern Minnesota has dropped from about 8; 000 moose to a stable population of just under 4; 000 moose over the last 4 years. Meanwhile, the northwestern Minnesotan population practically disappeared after declining from 4; 000 to fewer than 100. The reason behind this steep drop is unknown. Many scientists believe that it could be caused by climate change. Shorter winters and longer falls give more time for parasites, especially winter ticks, to find a host. For purposes of research, moose wore GPS collars, which allow biologists to track their location and collect essential data for future work. In some cases, moose received a tiny transmitter which monitored their heart rate and temperature and notified biologists when the moose died. This work intends to utilize the Bayesian hierarchic model with spatially varying coefficients to obtain better insights into moose habitat behavior in Northern Minnesota.
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    Statistical analysis techniques for logic and memory circuits.
    (2010-07) Liu, Qunzeng
    Process variations have become increasingly important as feature sizes enter the sub- 100nm regime and continue to shrink. Both logic and memory circuits have seen their performance impacted due to these variations. It is increasingly difficult to ensure that the circuit manufactured is in accordance with the expectation of designers through sim- ulation. For logic circuits, general statistical static timing analysis (SSTA) techniques have emerged to calculate the probability density function (PDF) of the circuit delay. However, in many situations post-silicon tuning is needed to further improve the yield. For memory circuits, embedded DRAM (eDRAM) is beginning to replace SRAM as the on-die cache choice in order to keep the scaling trend. Although techniques exist for statistical analysis of SRAM, detailed analysis of eDRAM has not been developed prior to this thesis. In this thesis, we provide techniques to aid statistical analysis for both logic and memory circuits. Our contribution in the logic circuits area is to provide robust and reliable, yet efficient post-silicon statistical delay prediction techniques for estimating the circuit delay, to replace the traditional critical path replica method that can generate large errors due to process variations during the manufacturing process. We solve this problem from both the analysis perspective and the synthesis perspective. For the analysis problem, we assume that we are given a set of test structures built on chip, and try to get the delay information of the original circuit through measurement of these test structures. For the synthesis problem, we automatically build a representative critical path which maximally correlate with the original circuit delay. Both of these approaches are derived using variation aware formula and use SSTA as sub-steps. They capture the delay variation of the original circuit better than the traditional critical path replica approach and eliminates the need to perform full chip testing for the post-silicon tuning purpose. In response to the growing interest in using eDRAM-based memories as on-die cache, in the memory analysis area we provide the first statistical analysis approach for the cell voltage of eDRAM. We not only calculate the main body of the PDF for the cell voltage, but also specifically look at the tail of this PDF which is more important to ensure quality design due to the highly repetitive nature of the memory systems. We demonstrate the accuracy and efficiency of our methods by comparing them with Monte Carlo simulations.