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Full-Text Articles in Other Civil and Environmental Engineering
Machine Learning Prediction Of Mechanical And Durability Properties Of Recycled Aggregates Concrete, Itzel Rosalia Nunez Vargas
Machine Learning Prediction Of Mechanical And Durability Properties Of Recycled Aggregates Concrete, Itzel Rosalia Nunez Vargas
Electronic Thesis and Dissertation Repository
Whilst recycled aggregate (RA) can alleviate the environmental footprint of concrete production and the landfilling of colossal amounts of demolition waste, there need for robust predictive tools for its effects on mechanical and durability properties. In this thesis, state-of-the-art machine learning (ML) models were deployed to predict properties of recycled aggregate concrete (RAC). A systematic review was performed to analyze pertinent ML techniques previously applied in the concrete technology field. Accordingly, three different ML methods were selected to determine the compressive strength of RAC and perform mixture proportioning optimization. Furthermore, a gradient boosting regression tree was used to study the …
Exploiting Earth Observation Data To Impute Groundwater Level Measurements With An Extreme Learning Machine, Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames, E. James Nelson
Exploiting Earth Observation Data To Impute Groundwater Level Measurements With An Extreme Learning Machine, Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames, E. James Nelson
Faculty Publications
Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture …
Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen
Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen
Department of Biological Systems Engineering: Papers and Publications
Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the USMeat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means …
Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen
Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen
Department of Biological Systems Engineering: Papers and Publications
Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the US Meat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy …