Predicting the compressive strength and slump of high strength concrete using neural network

Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models

Machine learning prediction of mechanical properties of concrete: Critical review

Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks

Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model's performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R² = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R² = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance.

https://www.mdpi.com/1996-1944/12/6/983/pdf

J. Zhang

Papers

Predicting the shear strength of reinforced concrete beams using artificial neural networks

Numerical Simulation of Casing Strength in Salt-Gypsum Stratum Deep Well

Factors Determining the Maximum Recovery Stress of the Ni47Ti44Nb9 Alloy During Constrained Heating