| dc.description.abstract | The prediction of the time-dependent behavior of high-performance concrete (HPC) structures is essential to evaluating their service life. This prediction relies on the shrinkage and creep properties of HPC. However, unlike conventional concrete, the binary and ternary composite system of supplementary cementitious materials (SCMs) in HPC has demonstrated different shrinkage and creep properties. This difference makes it challenging to accurately predict these properties using existing material models in code and standard practices, i.e., ACI, fib, B4, and GL. These models exhibit significant deviations in prediction under standard statistical evaluation methods due to the influence of SCMs. To overcome this challenge, intelligent artificial neural network (ANN) models have been developed using a feed-forward backpropagation training algorithm. The ANN models consider a widely compiled indigenous database of shrinkage and creep and consist of the most realistic experimental relationship with the effecting extrinsic and intrinsic key parameters. These parameters include standard concrete mix design material proportions, mechanical and physical properties, environmental conditions, and aging factors to obtain shrinkage and creep properties of HPC. All concrete material properties influencing the behavior of shrinkage and creep have been related based on experimentally measured results and incorporated as input parameters in both intelligent developed ANN models. The accuracy of prediction of both ANN models has been substantiated by the experimentally measured database and existing material models as comparative appraisals using several statistical metrics. The developed ANN models to predict such complex nonlinear properties of HPC are more practical and beneficial than existing material models, which will help to fulfill sustainable development and improve the service life of HPC structures. | |
