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Full-Text Articles in Engineering
Using Spatial Analysis And Machine Learning Techniques To Develop A Comprehensive Highway-Rail Grade Crossing Consolidation Model, Samira Soleimani
Using Spatial Analysis And Machine Learning Techniques To Develop A Comprehensive Highway-Rail Grade Crossing Consolidation Model, Samira Soleimani
LSU Doctoral Dissertations
The safety of highway-railroad grade crossings (HRGC) is still an issue in the United States of America (USA). The grade crossing is where a railroad crosses a road at the same level without any over or underpass. To improve the safety of crossings, the crossings’ condition should be explored from several aspects such as engineering design (speed limit, warning signs, etc.), road condition (number of lanes, surface markings, etc.), rail design (the type of track, ballast, etc.), temporal variables (weather, visibility, time of day, lightning, etc.), social variables (population, race, etc.), and last but not least, spatial variables (the type …
Designing Technology For Different Scales Of Irrigation Scheduling, Paolo Alexander Consalvo
Designing Technology For Different Scales Of Irrigation Scheduling, Paolo Alexander Consalvo
Undergraduate Honors Capstone Projects
Uncertainty in water availability is a significant challenge to the agriculture industry. Farmers and irrigators depend on novel uses of sensors and data to maximize water efficiency. Documented studies have demonstrated scheduling irrigation is a straightforward, deterministic means of achieving water efficiency. Irrigation scheduling uses several parameters to determine the moment of crop water stress due to available water in the soil. However, sensors and data for soil moisture and matric potential, a parameter describing water available to plants, have the potential to train machine learning algorithms to forecast water irrigation needs based on previous measurements. Satellite remote-sensing is another …
A Comparison Of Data-Driven And Process-Based Modeling For Nutrient Estimation In A Eutrophic Reservoir, Yohtaro Kobayashi
A Comparison Of Data-Driven And Process-Based Modeling For Nutrient Estimation In A Eutrophic Reservoir, Yohtaro Kobayashi
Open Access Theses & Dissertations
As land use around bodies of water changes, the need to model the body of water increases. Models help to educate, understand, and predict the state of water. Process-based models are commonly used in modelling bodies of water, but there are challenges with these kinds of models. They require data which can be difficult for certain communities to obtain due to logistics or cost, are computationally intensive, technically complicated, and require calibration. In contrast, a data-driven model simply connect relationships from the data, are not as computationally intensive nor technically complicated, and do not require calibration. This research compared a …
Using Statistical Learning Approaches To Understand Trends And Variability Of Tornadoes Across The Continental United States, Niloufar Nouri
Using Statistical Learning Approaches To Understand Trends And Variability Of Tornadoes Across The Continental United States, Niloufar Nouri
Dissertations and Theses
The annual frequency of tornadoes during 1950-2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other …
Studying The Effects Of Various Process Parameters On Early Age Hydration Of Single- And Multi-Phase Cementitious Systems, Rachel Cook
Doctoral Dissertations
”The hydration of multi-phase ordinary Portland cement (OPC) and its pure phase derivatives, such as tricalcium silicate (C3S) and belite (ß-C2S), are studied in the context varying process parameters -- for instance, variable water content, water activity, superplasticizer structure and dose, and mineral additive type and particle size. These parameters are studied by means of physical experiments and numerical/computational techniques, such as: thermodynamic estimations; numerical kinetic-based modelling; and artificial intelligence techniques like machine learning (ML) models. In the past decade, numerical kinetic modeling has greatly improved in terms of fitting experimental, isothermal calorimetry to kinetic-based modelling …