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Full-Text Articles in Engineering
Application Of Artificial Neural Network To Predict The Properties Of Permeable Concrete, Hatem H. Almasaeid, Donia G. Salman
Application Of Artificial Neural Network To Predict The Properties Of Permeable Concrete, Hatem H. Almasaeid, Donia G. Salman
Faculty and Student Publications
The structure of permeable concrete has been the primary reason for its use in construction. Permeable concrete is composed of water, cement, aggregate, and little-to no-fines resulting in the presence of a significant number of voids. This makes permeable concrete an ideal solution to water accumulation issues as it acts as a drainage system. This study employs a feedforward backpropagation artificial neural network model that combines experimental laboratory data from previous studies with appropriate network architectures and training techniques. The purpose of the analysis is to develop a reliable functional relationship, based on water-cement ratio, aggregate-cement ratio, and density parameters, …
Computational Modeling Of Climate Attributes And Condition Deterioration Of Concrete Highway Pavements, Salma Sultana
Computational Modeling Of Climate Attributes And Condition Deterioration Of Concrete Highway Pavements, Salma Sultana
Electronic Theses and Dissertations
An efficient and safe road network secures the nation’s economy and prosperity by providing public mobility and freight transport. Maintenance and rehabilitation of the road network cost billions of dollars annually. Road and highway infrastructures performance in any country is impacted by load repetitions and it is further compromised by climate attributes and extreme weather events. Damages to roads and bridges are among the infrastructure failures that have occurred during these extreme events. If maintenance and rehabilitation are not done promptly, the damages to the road caused by heavy traffic and extreme climate may lead to life-threatening conditions for road …
Forecasting Geotechnical Parameters From Electrical Resistivity And Seismic Wave Velocities Using Artificial Neural Network Models, Fatema Tuz Johora
Forecasting Geotechnical Parameters From Electrical Resistivity And Seismic Wave Velocities Using Artificial Neural Network Models, Fatema Tuz Johora
Electronic Theses and Dissertations
Geotechnical measurements of soil parameters used in the design of infrastructure provide information at a specific point of the ground. The use of limited point data may result in greater uncertainty and less reliability in design. Geophysical methods are non-invasive, less time-consuming, and provide continuous spatial information about the soil. However, geophysical information is not in terms of engineering parameters. Correlations between geotechnical parameters and geophysical parameters are needed to facilitate the use of geophysical information in geotechnical designs. The current research is focused on two geophysical methods; electrical resistivity (ER) and seismic wave velocity (S-wave and P-wave). Artificial neural …