Journal
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
Volume 82, Issue 6, Pages -Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10064-023-03226-z
Keywords
Marl; Lignosulfonate; Cement; UCS; Freeze-thaw cycles; Machine learning
Ask authors/readers for more resources
The mechanical strength of calcium carbonate-enriched marl soil under the effect of freeze-thaw cycles was improved by introducing a combination of ordinary Portland cement and lignosulfonate. Microstructural investigations demonstrated the development of new calcium-aluminate-silicate-hydrate products and a denser structure with lower porosity. Various machine learning algorithms were employed for cost-effective and accurate prediction of the soil's strength.
Calcium carbonate-enriched marl is supposed to lose the bearing capacity while subjected to an increase/decrease in the moisture content and under the effect of multiple freeze-thaw (F-T) cycles. Investigating different weight percentages and curing periods, combination of ordinary Portland cement (OPC) and lignosulfonate is introduced as an efficient method to improve the mechanical strength of the marl soil under the effect of F-T cycles. In this regard, compaction, unconfined compressive strength, and direct shear tests were conducted. It was observed that although the addition of OPC (as the sole binder) can enhance the shear strength, the performance of samples under the action of F-T was not significantly improved. However, samples containing lignosulfonate showed a rectified behavior. Microstructural investigations exhibited the development of new intensity peaks for the calcium-aluminate-silicate-hydrate (C-A-S-H) products and elaborated a denser structure with a lower porosity, keeping the soil particles closer. Next, different intelligent approaches of machine learning (ML) were employed to provide cost-effective and accurate speedy tools. Among eight machine learning algorithms and advanced ensemble models, comparative study revealed the efficiency of gradient boosting model with coefficients of determinations of up to 98.5% for the prediction of UCS. Feature importance analysis suggested the duration of treatment and the cement content as the main contributing factors to the UCS. Highly accurate and efficient EPR-based models with coefficients of determination of higher than 99% were also proposed for the prediction of shear strength and shear stress parameters.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available