This Artificial neural network study presents the prediction model for a cellular foamed concrete. Foamed Concrete is a cementitious material that should consist of a minimum of 20% of foam, which is mechanically entrained using the mechanical generator of foam. Foamed Concrete possesses a cellular microstructure. By which they become a highly air-entrained system having unusual physical and mechanical properties. It is the perfect mixture of cement, water, sand (fine aggregate), and perforated foam. Published information related to the prediction of foamed concrete is limited, and rational guidelines to evaluate the compressive strength of the concrete are not widely available. This study aims to encourage the strength of foamed concrete economically and predict the strength in the compressive form of concrete. A dataset of 153 instances having an input parameter proportion of Density, W/C ratio, & S/C ratio have been taken to predict compressive strength to elevate and expand the precision and accuracy of the foamed concrete. The data has been trained with the help of ANN, in which we conduct a network analysis to forecast the compound’s performance and stability. The deficiency of strength of foamed concrete is to be sorted out with the help of ANN, and the prominent and reliable equation for the compression power is generated. ANN helps to optimize the compressive strength at the time of physical casting of the concrete.
The bond strength of grip b/w steel and concrete can be defined as the resistant to separating concrete or mortar from the reinforced bar. This bond strength is the most critical characteristic of reinforced-cement concrete. Structural performance depends upon this characteristic, especially in the failure phase. Bond strength is primarily dependable on many variables that affect this attribute. These variables include the diameter of the reinforced steel bar, bond extent, length to diameter ratio, cube compressive strength, concrete cover, cover to dimeter ratio, volume fraction and most importantly, different temperatures. Up to 150°C, there is no such change in bond strength of reinforcement concrete, but when the temperature rises beyond 150°C, it starts to decreasegradually. We have collected experimental data from the internationally published record. This study will see the change in bond strength at these temperature variations i.e., 200°C, 400°C, and 600°C. This observational study will represent a soft computing tool, i.e., an Artificial Neural network (ANN), to predict and measure the grip strength between concrete and steel bar at elevated temperatures. The bond strength of reinforced concrete has been predicted by using ANN Models. Data set based upon the different factor that affects the bond strength has been used as input for generating ANN model & ultimate bond strength of reinforced concrete has been used as output during the development of the ANN model. This model was then prepared to predict bond strength and affected by many input features and recorded a linear regression analysis. The predicted result then confirmed the accuracy and high estimation capability of the model.
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