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DECISION SUPPORT MODEL FOR CONSTRUCTION CREW REASSIGNMENTS
Title
DECISION SUPPORT MODEL FOR CONSTRUCTION CREW REASSIGNMENTS.
Creator
Sist, Angela, Pet-Armacost, Julia, University of Central Florida
Abstract / Description
The reassignment of crews on a construction project in response to changes occurs on a frequent basis. The factors that affect the crew reassignment decision can be myriad and most are not known with certainty. This research addresses the need for a decision support model to assist construction managers with the crew reassignment problem. The model design makes use of certainty factors in a decision tree structure. The research helped to determine the elements in the decision tree, the...
Show moreThe reassignment of crews on a construction project in response to changes occurs on a frequent basis. The factors that affect the crew reassignment decision can be myriad and most are not known with certainty. This research addresses the need for a decision support model to assist construction managers with the crew reassignment problem. The model design makes use of certainty factors in a decision tree structure. The research helped to determine the elements in the decision tree, the appropriate combination rules to use with certainty factors, and the method for combining the certainty factors and costs to develop a measure of cost for each decision option. The research employed surveys, group meetings, and individual interviews of experienced construction managers and superintendents to investigate the current methods used by decision makers to identify and evaluate the key elements of the construction crew reassignment decision. The initial research indicated that the use of certainty factors was preferred over probabilities for representing uncertainties. Since certainty factors have not been used in a traditional decision tree context, a contribution of the research is the development and testing of techniques for combining certainty factors, durations, and costs in order to represent the uncertainty and to emulate the decision process of the experts interviewed. The developed model provides the decision maker with an estimate of the upper and lower bounds of costs for each crew reassignment option. The model was applied contemporaneously to six changes on three ongoing construction projects to test the model and assess its usefulness. The model provides a previously unavailable tool for the prospective identification and estimation of productivity losses and potential costs that emanate from changes. The users indicated the model process resulted in concise and complete compilations of the elements of the crew reassignment decision and that the model outputs were consistent with the users' expectations.
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Date Issued
2004
Identifier
CFE0000297, ucf:46212
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000297
Development of the Strategy to Select optimum Reflective Cracking Mitigation Methods for the Hot-Mix Asphalt Overlays in Florida
Title
Development of the Strategy to Select optimum Reflective Cracking Mitigation Methods for the Hot-Mix Asphalt Overlays in Florida.
Creator
Maherinia, Hamid, Nam, Boo Hyun, Behzadan, Amir, Tatari, Mehmet, University of Central Florida
Abstract / Description
Hot Mix Asphalt (HMA) overlay is a major rehabilitation treatment for the existing deteriorated pavements (both flexible and rigid pavements). Reflective cracking (RC) is the most common distress type appearing in the HMA overlays which structurally and functionally degrades the whole pavement structure, especially under high traffic volume. Although many studies have been conducted to identify the best performing RC mitigation technique, the level of success varies from premature failure to...
Show moreHot Mix Asphalt (HMA) overlay is a major rehabilitation treatment for the existing deteriorated pavements (both flexible and rigid pavements). Reflective cracking (RC) is the most common distress type appearing in the HMA overlays which structurally and functionally degrades the whole pavement structure, especially under high traffic volume. Although many studies have been conducted to identify the best performing RC mitigation technique, the level of success varies from premature failure to good performance in the field. In Florida, Asphalt Rubber Membrane Interlayers (ARMIs) have been used as a RC mitigation technique but its field performance has not been successful. In this study, the best performing means to mitigate RC in the overlays considering Florida's special conditions have been investigated. The research methodology includes (1) extensive literature reviews regarding the RC mechanism and introduced mitigation options, (2) nationwide survey for understanding the current practice of RC management in the U.S., and (3) the development of decision trees for the selection of the best performing RC mitigation method. Extensive literature reviews have been conducted to identify current available RC mitigation techniques and the advantages and disadvantages of each technique were compared. Lesson learned from the collected case studies were used as input for the selection of the best performing RC mitigation techniques for Florida's roads. The key input parameters in selecting optimum mitigation techniques are: 1) overlay characterization, 2) existing pavement condition, 3) base and subgrade structural condition, 4) environmental condition and 5) traffic level. In addition, to understand the current practices how reflective cracking is managed in each state, a nationwide survey was conducted by distributing the survey questionnaire (with the emphasis on flexible pavement) to all other highway agencies. Based on the responses, the most successful method of treatment is to increase the thickness of HMA overlay. Crack arresting layer is considered to be in the second place among its users. Lack of cost analysis and low rate of successful practices raise the necessity of conducting more research on this subject.Considering Florida's special conditions (climate, materials, distress type, and geological conditions) and the RC mechanism, two RC mitigation techniques have been proposed: 1) overlay reinforcement (i.e. geosynthetic reinforcement) for the existing flexible pavements and 2) Stress Absorbing Membrane Interlayer (SAMI) for the existing rigid pavements. As the final products of this study, decision trees to select an optimum RC mitigation technique for both flexible and rigid pavements were developed. The decision trees can provide a detailed guideline to pavement engineer how to consider the affecting parameters in the selection of RC mitigation technique.
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Date Issued
2013
Identifier
CFE0005108, ucf:50753
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005108
AN ANALYSIS OF MISCLASSIFICATION RATES FOR DECISION TREES
Title
AN ANALYSIS OF MISCLASSIFICATION RATES FOR DECISION TREES.
Creator
Zhong, Mingyu, Georgiopoulos, Michael, University of Central Florida
Abstract / Description
The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class...
Show moreThe decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by standard deviations). We also provide an explicit expression of the k-norm estimation for the tree's misclassification rate that combines both the expected value and the reliability. Furthermore, our proposed and proven theorem on k-norm estimation suggests an efficient pruning algorithm that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly that compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Finally, our work provides a deeper understanding of decision trees.
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Date Issued
2007
Identifier
CFE0001774, ucf:47271
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001774
Predicting Students' Academic Performance with Decision Tree and Neural Network
Title
Predicting Students' Academic Performance with Decision Tree and Neural Network.
Creator
Feng, Junshuai, Jha, Sumit Kumar, Zhang, Wei, Zhang, Shaojie, University of Central Florida
Abstract / Description
Educational Data Mining (EDM) is a developing research field that involves many techniques to explore data relating to educational background. EDM can analyze and resolve educational data with computational methods to address educational questions. Similar to EDM, neural networks have been utilized in widespread and successful data mining applications. In this paper, synthetic datasets are employed since this paper aims to explore the methodologies such as decision tree classifiers and neural...
Show moreEducational Data Mining (EDM) is a developing research field that involves many techniques to explore data relating to educational background. EDM can analyze and resolve educational data with computational methods to address educational questions. Similar to EDM, neural networks have been utilized in widespread and successful data mining applications. In this paper, synthetic datasets are employed since this paper aims to explore the methodologies such as decision tree classifiers and neural networks to predict student performance in the context of EDM. Firstly, it introduces EDM and some relative works that have been accomplished previously in this field along with their datasets and computational results. Then, it demonstrates how the synthetic student dataset is generated, analyzes some input attributes from the dataset such as gender and high school GPA, and delivers with some visualization results to determine which classification methods approaches are the most efficient. After testing the data with decision tree classifiers and neural networks methodologies, it concludes the effectiveness of both approaches in terms of the model evaluation performance as well as discussing some of the most promising future work of this research.
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Date Issued
2019
Identifier
CFE0007455, ucf:52680
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007455
Modeling Mass Care Resource Provision Post Hurricane
Title
Modeling Mass Care Resource Provision Post Hurricane.
Creator
Muhs, Tammy, Kincaid, John, Rollins, David, Dorman, Teresa, Taylor, Gregory, University of Central Florida
Abstract / Description
Determining the amount of resources needed, specifically food and water, following a hurricane is not a straightforward task. Through this research effort, an estimating tool was developed that takes into account key demographic and evacuation behavioral effects, as well as hurricane storm specifics to estimate the number of meals required for the first fourteen days following a hurricane making landfall in the State of Florida. The Excel based estimating tool was created using data collected...
Show moreDetermining the amount of resources needed, specifically food and water, following a hurricane is not a straightforward task. Through this research effort, an estimating tool was developed that takes into account key demographic and evacuation behavioral effects, as well as hurricane storm specifics to estimate the number of meals required for the first fourteen days following a hurricane making landfall in the State of Florida. The Excel based estimating tool was created using data collected from four hurricanes making landfall in Florida during 2004-2005. The underlying model used in the tool is a Regression Decision Tree with predictor variables including direct impact, poverty level, and hurricane impact score. The hurricane impact score is a hurricane classification system resulting from this research that includes hurricane category, intensity, wind field size, and landfall location. The direct path of a hurricane, a higher than average proportion of residents below the poverty level, and the hurricane impact score were all found to have an effect on the number of meals required during the first fourteen days following a hurricane making landfall in the State of Florida.
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Date Issued
2011
Identifier
CFE0004143, ucf:49053
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004143
Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level
Title
Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level.
Creator
Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
Abstract / Description
This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based...
Show moreThis thesis presents different data mining/machine learning techniques to analyze the vulnerable road users' (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model's estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model.
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Date Issued
2018
Identifier
CFE0007358, ucf:52103
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007358
Factors Influencing Hypoglycemia Care Utilization and Outcomes Among Adult Diabetic Patients Admitted to Hospitals: A Predictive Model
Title
Factors Influencing Hypoglycemia Care Utilization and Outcomes Among Adult Diabetic Patients Admitted to Hospitals: A Predictive Model.
Creator
Kattan, Waleed, Wan, Thomas, Ramirez, Bernardo, Gurupur, Varadraj, Stevenson, Robyne, Pratley, Richard, University of Central Florida
Abstract / Description
Diabetes Miletus (DM) is one of the major health problems in the United States. Despite all efforts made to combat this disease, its incidence and prevalence are steadily increasing. One of the common and serious side effects of treatment among people with diabetes is hypoglycemia (HG), where the level of blood glucose falls below the optimum level. Episodes of HG vary in their severity. Nevertheless, many require medical assistance and are usually associated with higher utilization of...
Show moreDiabetes Miletus (DM) is one of the major health problems in the United States. Despite all efforts made to combat this disease, its incidence and prevalence are steadily increasing. One of the common and serious side effects of treatment among people with diabetes is hypoglycemia (HG), where the level of blood glucose falls below the optimum level. Episodes of HG vary in their severity. Nevertheless, many require medical assistance and are usually associated with higher utilization of healthcare resources such as frequent emergency department visits and physician visits. Additionally, patients who experience HG frequently have poor outcomes such as higher rates for morbidities and mortality.Although many studies have been conducted to explore the risk factors associated with HG as well as others that looked into the level of healthcare utilization and outcomes among patients with HG, most of these studies failed to establish a theoretical foundation and integrate a comprehensive list of personal risk factors. Therefore, this study aimed to employ Andersen's health Behavior Model of health care utilization (BM) as a framework to examine the problems of HG. This holistic approach facilitates enumerating predictors and examining differential risks of the predisposing (P), enabling (E) and need-for-care (N) factors influencing HG and their effects on utilization (U) and outcomes (O). The population derived from the national inpatient sample of the Healthcare Cost and Utilization Project (HCUP) database and included all non-pregnant adult diabetic patients admitted to hospitals' Emergency Departments (EDs) with a diagnosis of HG from 2012-2014. Based on the BM framework, different factors influencing HG utilization and outcome were grouped under the P, E, or N component. Utilization was measured by patients' length of stay (LoS) in the hospital and the total charges incurred for the stay. Outcome was assessed based on the severity ranging from mortality (the worst), severe complications, mild complications, to no complications (the best). Structural Equation Modeling (SEM) followed by Decision Tree Regression (DTREG) were performed. SEM helped in testing multiple hypotheses developed in the study as well as exploring the direct and indirect impact of different risk factors on utilization and outcome. The results of the analysis show that N is the most influential component of predictors of U and O. This is parallel to what was repeatedly found in different studies that employed the BM. Regarding the other two components, P was found to have some effect on O, while E influences the total charge. Interaction effects of predictors were noted between some components, which indicate the indirect effect of these components on U and O. Subsequently, DTREG analysis was conducted to further explore the probability of the different predictor variables on LoS, total charge, and outcome. Results of this study revealed that the presence of renal disease and DM complications among HG patients play a key role in predicting U and O. Furthermore, age, socio-economic status (SES), and the geographical location of the patients were also found to be vital factors in determining the variability in U and O among HG patients.In conclusion, findings of this study lend support to the use of the BM approach to health services use and outcomes and provide some practical applications for healthcare providers in terms of using the predictive model for targeting patient subgroups (HG patients) for interventions among diabetic patients. Moreover, policy implications, particularly related to the Central Florida area, for decision makers regarding how to approach the growing problem of DM can be drawn from the study results.
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Date Issued
2017
Identifier
CFE0006611, ucf:51304
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006611
Learning to Grasp Unknown Objects using Weighted Random Forest Algorithm from Selective Image and Point Cloud Feature
Title
Learning to Grasp Unknown Objects using Weighted Random Forest Algorithm from Selective Image and Point Cloud Feature.
Creator
Iqbal, Md Shahriar, Behal, Aman, Boloni, Ladislau, Haralambous, Michael, University of Central Florida
Abstract / Description
This method demonstrates an approach to determine the best grasping location on an unknown object using Weighted Random Forest Algorithm. It used RGB-D value of an object as input to find a suitable rectangular grasping region as the output. To accomplish this task, it uses a subspace of most important features from a very high dimensional extensive feature space that contains both image and point cloud features. Usage of most important features in the grasping algorithm has enabled the...
Show moreThis method demonstrates an approach to determine the best grasping location on an unknown object using Weighted Random Forest Algorithm. It used RGB-D value of an object as input to find a suitable rectangular grasping region as the output. To accomplish this task, it uses a subspace of most important features from a very high dimensional extensive feature space that contains both image and point cloud features. Usage of most important features in the grasping algorithm has enabled the system to be computationally very fast while preserving maximum information gain. In this approach, the Random Forest operates using optimum parameters e.g. Number of Trees, Number of Features at each node, Information Gain Criteria etc. ensures optimization in learning, with highest possible accuracy in minimum time in an advanced practical setting. The Weighted Random Forest chosen over Support Vector Machine (SVM), Decision Tree and Adaboost for implementation of the grasping system outperforms the stated machine learning algorithms both in training and testing accuracy and other performance estimates. The Grasping System utilizing learning from a score function detects the rectangular grasping region after selecting the top rectangle that has the largest score. The system is implemented and tested in a Baxter Research Robot with Parallel Plate Gripper in action.
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Date Issued
2014
Identifier
CFE0005509, ucf:50358
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005509