Probabilistic Comparative Assessment of Structural Cement Composite Compressive Strength–Porosity Models
Publication: Journal of Structural Engineering
Volume 150, Issue 11
Abstract
Compressive strength models for ordinary portland cement composites are typically deterministic and rely on parameters such as water-to-cement ratio, porosity, and chemical composition. While these models can estimate compressive strength, they often lack the ability to quantify uncertainty in their predictions. Various factors contribute to this uncertainty, including diverse testing protocols, limited data, overly complex mathematical formulations, and constraints in capturing the variability of the phenomenon through model parameters. This study introduces a comparative evaluation of representative compressive strength–porosity models for cement paste, adopting a probabilistic perspective and using data from various testing protocols found in the literature. Experimental data encompassing compressive strength and porosity, spanning porosity values from 1% to 30%, were gathered from hot-pressed and conventionally cast specimens. Results were obtained following the Bayesian Analysis Reporting Guidelines (BARG) with combined decision criteria using a 95% High Posterior Density (HPD) interval and the Range of Practical Equivalence (ROPE). A probabilistic version of Ryshkewitch’s model emerges as the most plausible based on porosity data. These results hold promise for integration into risk analysis and decision-making tailored for structural analysis and design. Ultimately, this research contributes insights into enhancing the reliability of compressive strength predictions for cement composites by accounting for the inherent uncertainty of the underlying models.
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Data Availability Statement
The data, models, Python scripts, and MCMC chains that support the findings of this study are accessible from the OSF repository (MejiaCruz et al. 2024).
Acknowledgments
This material is based on work supported by the US Department of Energy’s Office of Science, Office of Basic Energy Sciences, and Office of Biological and Environmental Research under Award No. DE-SC-00012530.
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Published In
Journal of Structural Engineering
Volume 150 • Issue 11 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 3, 2024
Accepted: May 9, 2024
Published online: Sep 4, 2024
Published in print: Nov 1, 2024
Discussion open until: Feb 4, 2025
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