Prediction on Flexural strength of High Strength Hybrid Fiber Self Compacting Concrete by using Artificial Intelligence

Abstract

The primitive focus of this research work is about the artificial intelligence methods engaged for creating an outlook for flexural strength of High Strength Hybrid Fiber Self Compacted Concrete (HSHFSCC), which is considered to be a special concrete in order to tackle both workability and durability without disturbing the strength of the concrete. It possesses not only the good deformability during fresh state but also put forward high aversion to segregation resulting in superior homogeneity and increase in productivity by altering the period of construction. While incorporating various fibers like glass, steel, carbon, synthetic, and quartz powder in plain concrete, directs in the enhancement of post-cracking, toughness, ductility and limits the detrimental effect of shrinkage. The current work is classified into two stages. 1) Development of HSHFSCC and High Strength Self Compacting Concrete (HSSCC). 2) Engaging different Machine Learning (ML) models to divide the obtained information into Train, Test and Validation followed by 19 types of different ML regression models accompanied with Artificial Neural Network for engaging the function to appropriate the flexural strength of HSHFSCC and HSSCC. The boundary conditions discussed as input includes Setting time, percentage of quartz and river sand. Total 25 number of datasets are used for 5-fold cross validations by adopting MATLAB ML and Deep learning toolkit and Python is adopted to validate the optimized models. The evaluation factors like R-square and Root mean square show a great level of accuracy and reliability of the model.

An Investigation on Eco Friendly Self-Compacting Concrete Using Spent Catalyst and Development of Structural Elements

“…In order to address both workability and durability without compromising the concrete's strength, artificial intelligence techniques were used to provide a perspective on the flexural strength of High Strength Hybrid Fiber Self Compacted Concrete (HSHFSCC), which is regarded as a specific type of concrete. It has excellent deformability in the fresh state and a strong reluctance to segregation, which improves homogeneity and increases production by adjusting the construction duration [35]. The advantages of this new composited SCC fiber composition with appropriate rheological, physical, and mechanical qualities compared to those of conventional SCC concrete.…”

Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete

“…Keeping this in mind, an inclination has recently been made towards employing ML approaches for concrete strength prediction (Aiyer et al, 2014;Asteris et al, 2016;Sonebi et al, 2016;Dutta et al, 2017;Bayrami, 2022;Sarkhani Benemaran et al, 2022;Shah et al, 2022;Wang and Wu, 2022). These approaches may be used for numerous applications such as classification, regression, clustering, and correlation (Nehdi et al, 2001;Nguyen et al, 2020;Asri et al, 2022;De-Prado-Gil et al, 2022;Kumar and Kumar, 2022). Due to the development in ML techniques, it is thus easier to determine the concrete strength (Balf et al, 2021;Kovačević et al, 2021;Zhang Z et al, 2022).…”

Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete

“…Considering this, a trend has gained a surge in recent years by using ML techniques to anticipate the CS of concrete material [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. These methods can be utilized for a number of applications, including regression, classification, correlation, and clustering [ 36 , 37 , 38 , 39 , 40 ]. With the advancement of ML approaches, it is consequently uncomplicated to investigate the CS of SCC along with the concrete’s other properties [ 41 , 42 ].…”