ANN-Powered Models for Predicting Shrinkage and Creep Properties of High-Performance Concrete Using Supplementary Cementitious Materials
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 6
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.
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Data Availability Statement
Some or all data, models, or codes that support the research finding of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The author wishes to express their gratitude and sincere appreciation to the authority of the Science and Engineering Research Board, India, in Project No. EEQ/2023/000130 for funding this research work.
Author contributions: Banti A. Gedam: funding acquisition, investigation, methodology, project administration, supervision, conceptualization, data curation, formal analysis, and writing–review and editing.
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Published In
Journal of Computing in Civil Engineering
Volume 38 • Issue 6 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Mar 4, 2024
Accepted: May 21, 2024
Published online: Aug 6, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 6, 2025
ASCE Technical Topics:
- Artificial intelligence (AI)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Creep
- Engineering fundamentals
- Engineering mechanics
- Material mechanics
- Material properties
- Materials characterization
- Materials engineering
- Model accuracy
- Models (by type)
- Neural networks
- Rheology
- Shrinkage (material)
- Standards and codes
- Structural behavior
- Structural engineering
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