Models for Quantitative Assessment of Self-Healing in Bacteria-Incorporated Fiber-Reinforced Mortar
Publication: Journal of Materials in Civil Engineering
Volume 35, Issue 7
Abstract
Statistical modeling and the design of experiment methodology (DOE) have been successfully used in the past in various civil engineering applications. An attempt has been made in this paper to incorporate the principles of DOE to statistically model the self-healing characteristics of bacteria-incorporated fiber-reinforced (FR) mortar. DOE eliminated a great deal of redundancy and provided characteristic equations for properties of cementitious composites to quantify self-healing because it allowed manipulation of multiple input factors to determine their effect on a desired response. Characteristic model equations were developed with the help of statistical tools such as regression analysis and ANOVA. Statistical models were developed to predict the self-healing characteristics in terms of rapid chloride permeability, sorptivity, and ultrasonic pulse velocity. The performance of the models was validated through experimental results. The models were found to predict reasonably well the properties that are indicators of the self-healing capability of FR mortar. The developed statistical models can be used as a valuable tool for quantifying the self-healing capability of bacteria-incorporated FR mortar in terms of illustrated properties.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This research is being carried out in the research labs of the Civil Engineering and Environmental Microbiology at Toronto Metropolitan University, Toronto, Canada. Financial support for this research from NSERC, Canada, is gratefully acknowledged.
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Published In
Journal of Materials in Civil Engineering
Volume 35 • Issue 7 • July 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 12, 2020
Accepted: Nov 18, 2022
Published online: Apr 29, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 29, 2023
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