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Machine Learning Prediction Of Shear Capacity Of Steel Fiber Reinforced Concrete, Wassim Ben Chaabene
Machine Learning Prediction Of Shear Capacity Of Steel Fiber Reinforced Concrete, Wassim Ben Chaabene
Electronic Thesis and Dissertation Repository
The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to …
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 …