Performance of FRP-concrete bridges under blast loading

Catastrophic failure from intentional terrorist attacks on surface transportation infrastructure could he detrimental to the society. In order to minimize the vulnerabilities and to ensure a safe transportation system, the issue of security for transportation structures, primarily bridges, which are subjected to man-made hazards is investigated in this study. A procedure for identifying and prioritizing "critical bridges" using a screening and prioritization processes is established. For each of the "critical" bridges, a systematic risk-based assessment approach is proposed that takes into account the combination of threat occurrence likelihood, its consequences, and the socioeconomic importance of the bridge. A series of effective security countermeasures are compiled in the four categories of deterrence, detection, defense and mitigation to help reduce the vulnerability of critical bridges. The concepts of simplified equivalent I-shape cross section and virtual materials are proposed for integration into a nonlinear finite element model, which helps assess the performance of reinforced concrete structures with and without composite retrofit or hardening measures under blast loading. A series of parametric studies are conducted for single column and two-column pier frame systems as well as for an entire bridge. The parameters considered include column height, column type, concrete strength, longitudinal steel reinforcement ratio, thickness, fiber angle and tensile strength of the fiber reinforced polymer (FRP) tube, shape of the cross section, damping ratio and different bomb sizes. The study shows the benefits of hardening with composites against blast loading. The effect of steel reinforcement on blast resistance of the structure is more significant than the effect of concrete compressive strength. Moreover, multiple blasts do not necessarily lead to a more severe destruction than a single detonation at a strategically vulnerable location on the bridges.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Application of intervention analysis to evaluate the impacts of special events on freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

The effects of the use of technology in mathematics instruction on student achievement

The purpose of this study was to examine the effects of the use of technology on students’ mathematics achievement, particularly the Florida Comprehensive Assessment Test (FCAT) mathematics results. Eleven schools within the Miami-Dade County Public School System participated in a pilot program on the use of Geometers Sketchpad (GSP). Three of these schools were randomly selected for this study. Each school sent a teacher to a summer in-service training program on how to use GSP to teach geometry. In each school, the GSP class and a traditional geometry class taught by the same teacher were the study participants. Students’ mathematics FCAT results were examined to determine if the GSP produced any effects. Students’ scores were compared based on assignment to the control or experimental group as well as gender and SES. SES measurements were based on whether students qualified for free lunch. The findings of the study revealed a significant difference in the FCAT mathematics scores of students who were taught geometry using GSP compared to those who used the traditional method. No significant differences existed between the FCAT mathematics scores of the students based on SES. Similarly, no significant differences existed between the FCAT scores based on gender. In conclusion, the use of technology (particularly GSP) is likely to boost students’ FCAT mathematics test scores. The findings also show that the use of GSP may be able to close known gender and SES related achievement gaps. The results of this study promote policy changes in the way geometry is taught to 10th grade students in Florida’s public schools.

Hybrid approaches to estimating freeway travel times using point traffic detector data

The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.

Swimming against the tide: A study of a youth enrichment program seeking to empower inner-city Black youth

Serious inequities in our K-12 public education system, particularly in regard to the quality of education in predominately Black inner-city schools, are well-documented in the literature (Freeman, 1998; Ross, 1998). Moreover, there is general agreement that the most effective means of ameliorating that situation is through well-thought-out after-school programs and partnership initiatives (Beck, 1993; Gardner et al., 2001). ^ The purpose of this qualitative study was to examine the programmatic interventions of a youth enrichment program for inner-city Black youth currently in place at the Overtown Youth Center (OYC) in Miami, Florida, in order to: (a) discern those factors that support its claim that it is making a difference in students’ lives, (b) explore how any such factors are implemented, and (c) determine whether its interventions have served to equalize the playing field for these youth. ^ Two primary methods of data collection were used for this study. The first was participant observation conducted over the course of two years through a partnership initiative established and led by this author. The second was through in-depth interviews of the Center’s founder, staff, and students. Secondary methods used were the recording of informal conversations and the analysis of written documents. ^ Analysis of the data yielded four features of the Center that are indispensible to the students’ growth. The center provides the youth with (a) physical and psychological safety, (b) supportive relationships, (c) exposure to cultural and educational opportunities, and (d) assistance in building self-esteem. ^ The most significant finding of the study was that OYC has been successful at making a difference in students’ lives and at increasing their aspirations to attend college. By addressing the full spectrum of their needs, the Center has given them many of the necessary tools with which to compete and thereby helped equalize their opportunities to succeed in school and in life. ^ The study also noted a number of challenges for the Center to examine. The main issues that need to be addressed more seriously are staff turnover, staff indifference, nepotism, inconsistent student attendance, and insufficient focus on racial issues and African-American-centered education. Meeting those challenges would engender even greater positive outcomes.^

Metaphor: The Hidden Link between Visual Literacy and Learning

Visual Thinking Strategies (VTS) have been proven to enhance learning abilities in children. This study will examine how the use of metaphor might affect the development of fourth graders’ verbal and written abilities at three schools in the Miami-Dade County Public School system.

Exploring the Relationship Between Hotel Characteristics and Crime

Knowledge of crimes that have occurred in hotels has been scares. The authors explore the nature and causes of hotel crimes in a U.S. metropolitan area. Levels of crimes were directly related to size of the hotel, target market of business travelers, access to public transportation, and an unsafe image of the environment surrounding the hotel. Crime prevention programs based on the findings can be developed to protect the safety of guests and property.

An investigation of computer anxiety among vocational -technical teachers in Dade County, Florida

The purpose of this research was to investigate the relationship of computer anxiety to selected demographic variables: learning styles, age, gender, ethnicity, teaching/professional areas, educational level, and school types among vocational-technical educators.^ The subjects (n = 202) were randomly selected vocational-technical educators from Dade County Public School System, Florida, stratified across teaching/professional areas. All subjects received the same survey package in the spring of 1996. Subjects self-reported their learning style and level of computer anxiety by completing Kolb's Learning Style Inventory (LSI) and Oetting's Computer Anxiety Scale (COMPAS, Short Form). Subjects' general demographic information and their experience with computers were collected through a self-reported Participant Inventory Form.^ The distribution of scores suggested that some educators (25%) experienced some overall computer anxiety. There were significant correlations between computer related experience as indicated by self-ranked computer competence and computer based training and computer anxiety. One-way analyses of variance (ANOVA) indicated no significant differences between computer anxiety and/or computer related experiences, and learning style, age, and ethnicity. There were significant differences between educational level, teaching area, school type, and computer anxiety and/or computer related experiences. T-tests indicated significant differences between gender and computer related experiences. However, there was no difference between gender and computer anxiety.^ Analyses of covariance (ANCOVA) were performed for each independent variable on computer anxiety, with computer related experiences (self-ranked computer competence and computer based training) as the respective covariates. There were significant main effects for the educational level and school type on computer anxiety. All other variables were insignificant on computer anxiety. ANCOVA also revealed an effect for learning style varied notably on computer anxiety. All analyses were conducted at the.05 level of significance. ^

Investigating group processes in a positive youth development program for multi -problem youth

This study investigated group processes as potential mediators or moderators of positive development outcome and negative reduction intervention response by evaluating the utility of a group measure modified from a widely known measure of group impact found in the group therapy research literature. Four group processes were of primary interest, (1) Group Impact; (2) Facilitator Impact; (3) Skills Impact; and (4) Exploration Impact as assessed by the Session Evaluation Form (SEF). Outcome measures included the Personally Expressive Activities Questionnaire (PEAQ), Erikson Psycho-Social Index (EPSI) and the Zill Behavior Items, Behavior Problem Index (ZBI (BPI)). The sample consisted of 121 multi-ethnic participants drawn from four alternative high schools from the Miami-Dade County Public School system. Utilizing a Latent Growth Curve Modeling approach with Structural Equation Modeling (SEM) statistics, preliminary analyses were conducted to evaluate the psychometric properties of the SEF and its role in the mediation or moderation of intervention outcome. Preliminary results revealed evidence of a single higher order factor representing a "General" global reaction, which was hypothesized to be a "Positive Group Climate" construct to the program as opposed to the four distinct group processes that were initially hypothesized to affect outcomes. The results of the evaluation of the mediation or moderation role of intervention outcome of the single "General" global latent factor ("Positive Group Climate" construct) did not significantly predict treatment response on any of the outcome variables. Nevertheless, the evidence of an underlying "General" global latent factor ("Positive Group Climate" construct) has important future directions for research on positive youth development programs as well as in group therapy research.

A comparison study of the English III/American Literature success of African American high school students and that of students whose home language is Haitian Creole

The purpose of the study was to compare the English III success of students whose home language is Haitian Creole (SWHLIHC) with that of the more visible African American high school students in the Miami Dade County Public Schools System, in an effort to offer insight that might assist educators in facilitating the educational success of SWHLIHC in American Literature class.^ The study was guided by two important theories on how students interact with and learn from literature. They are Reader Response Theory which advocates giving students the opportunity to become involved in the literature experience (Rosenblatt, 1995), and Critical Literacy, a theory developed by Paolo Freire and Henry Giroux, which espouses a critical approach to analysis of society that enables people to analyze social problems through lenses that would reveal social inequities and assist in transforming society into a more equitable entity.^ Data for the study: 10th grade reading FCAT scores, English III/American Literature grades, and Promotion to English IV records for the school year 2010-2011 were retrieved from the records division of the Miami Dade County Public Schools System. The study used a quantitative methods approach, the central feature of which was an ex post facto design with hypotheses (Newman, Newman, Brown, & McNeely, 2006). The ex post facto design with hypotheses was chosen because the researcher postulated hypotheses about the relationships that might exist between the performances of SWHLIHC and those of African American students on the three above mentioned variables. This type of design supported the researcher's purpose of comparing these performances.^ One way analysis of variance (ANOVA), two way ANOVAs, and chi square tests were used to examine the two groups' performances on the 10th grade reading FCAT, their English III grades, and their promotion to English IV. ^ The study findings show that there was a significant difference in the performance of SWHLIHC and African American high school students on all three independent variables. SWHLIHC performed significantly higher on English III success and promotion to English IV. African American high school students performed significantly higher on the reading FCAT.^

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

The Effects of the Use of Technology In Mathematics Instruction on Student Achievement

The purpose of this study was to examine the effects of the use of technology on students’ mathematics achievement, particularly the Florida Comprehensive Assessment Test (FCAT) mathematics results. Eleven schools within the Miami-Dade County Public School System participated in a pilot program on the use of Geometers Sketchpad (GSP). Three of these schools were randomly selected for this study. Each school sent a teacher to a summer in-service training program on how to use GSP to teach geometry. In each school, the GSP class and a traditional geometry class taught by the same teacher were the study participants. Students’ mathematics FCAT results were examined to determine if the GSP produced any effects. Students’ scores were compared based on assignment to the control or experimental group as well as gender and SES. SES measurements were based on whether students qualified for free lunch. The findings of the study revealed a significant difference in the FCAT mathematics scores of students who were taught geometry using GSP compared to those who used the traditional method. No significant differences existed between the FCAT mathematics scores of the students based on SES. Similarly, no significant differences existed between the FCAT scores based on gender. In conclusion, the use of technology (particularly GSP) is likely to boost students’ FCAT mathematics test scores. The findings also show that the use of GSP may be able to close known gender and SES related achievement gaps. The results of this study promote policy changes in the way geometry is taught to 10th grade students in Florida’s public schools.

Evaluating the effectiveness of signal timing optimization based on microscopic simulation evaluation

The optimization of the timing parameters of traffic signals provides for efficient operation of traffic along a signalized transportation system. Optimization tools with macroscopic simulation models have been used to determine optimal timing plans. These plans have been, in some cases, evaluated and fine tuned using microscopic simulation tools. A number of studies show inconsistencies between optimization tool results based on macroscopic simulation and the results obtained from microscopic simulation. No attempts have been made to determine the reason behind these inconsistencies. This research investigates whether adjusting the parameters of macroscopic simulation models to correspond to the calibrated microscopic simulation model parameters can reduce said inconsistencies. The adjusted parameters include platoon dispersion model parameters, saturation flow rates, and cruise speeds. The results from this work show that adjusting cruise speeds and saturation flow rates can have significant impacts on improving the optimization/macroscopic simulation results as assessed by microscopic simulation models.