Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete

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 predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have been benchmarked against various soft-computing models and existing empirical equations. Sensitivity analysis has also been conducted to identify the most influential parameters to the SFRC shear strength.

Name thesis:degree_name

M Eng Sci

Discipline thesis:degree_discipline

Civil and Environmental Engineering

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2020

Author dc:creator

• Ben Chaabene, Wassim

Advisor dc:contributor.advisor

• Moncef L. Nehdi

• Machine learning
• artificial neural network
• genetic programming
• steel fibers
• concrete

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/30643

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/30643