Development of Prediction models for Bond Strength of Steel Fiber Reinforced Concrete by Computational Machine Learning

Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of normalized bond strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphases on using analytical model for the prediction of bond strength of the green concrete, so that there is a reduction in the cost of construction, con-serve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper machine learning approach has been used to predict the normalized bond strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the Machine learning (ML) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a ML network model for prediction of normalized bond strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken from technical literature and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.

Investigation of bond between lap-spliced steel bar and self-compacting concrete: The role of silica fume

In this study, normal concrete (NC) and four types of self-compacting concrete (SCC), in which cement was partially replaced by 5%, 10%, 15%, and 20% of silica fume (SF), were used to evaluate the effect of SF content on the bond strength of tension lap-spliced bars embedded in NC and SCC specimens. Therefore, 15 full-scale beam specimens (2000 × 300 × 200 mm3) were tested and 20 mm reinforcing bars were used with a 300-mm splice length as tension reinforcement. Each beam was designed with bars spliced in a constant moment region at midspan. It was found that the bond strength of the reinforcement embedded in SCC beams was higher than that of the reinforcement in NC beams, whilst the bond strength increased with increase in the replacement of cement by SF from 5% to 10%. Moreover, the beam specimens produced from SCC containing 5% SF had the highest normalized bond strength of 1.07 followed by SCC beams with 10% SF, 15% SF, NC beams, and 20% SF.