Buildings 2025, 15(3), 408; https://doi.org/10.3390/buildings15030408 (registering DOI) - 28 Jan 2025
Abstract
This paper presents a comprehensive study of the mechanical properties of lime-based mortar in an acidic environment, employing both experimental analysis and machine learning to model techniques. Despite the extensive use of lime-based mortar in construction, particularly for the strengthening of structures as
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This paper presents a comprehensive study of the mechanical properties of lime-based mortar in an acidic environment, employing both experimental analysis and machine learning to model techniques. Despite the extensive use of lime-based mortar in construction, particularly for the strengthening of structures as externally bonded materials, its behavior under acidic conditions remains poorly understood in the literature. This study aims to address this gap by investigating the mechanical performance of lime-based mortar under prolonged exposure to acidic environments, laying the groundwork for further research in this critical area. In the experimental phase, a commercial hydraulic lime-based mortar was subjected to varying environmental conditions, including acidic solution immersion with a pH of 3.0, distilled water immersion, and dry storage. Subsequently, the specimens were tested under flexure following exposure durations of 1000, 3000, and 5000 h. In the modeling phase, the extreme gradient boosting (XGBoost) algorithm was deployed to predict the mechanical properties of the lime-based mortar by 1000, 3000, and 5000 h of exposure. Using the experimental data, the machine learning models were trained to capture the complex relationships between the stress-displacement curve (as the output) and various environmental and mechanical properties, including density, corrosion, moisture, and exposure duration (as input features). The predictive models demonstrated remarkable accuracy and generalization (using a 4-fold cross-validation approach) capabilities (R2 = 0.984 and RMSE = 0.116, for testing dataset), offering a reliable tool for estimating the mortar’s behavior over extended periods in an acidic environment. The comparative analysis demonstrated that mortar samples exposed to an acidic environment reached peak values at 3000 h of exposure, followed by a decrease in the mechanical properties with prolonged acidic exposure. In contrast, specimens exposed to distilled water and dry conditions exhibited an earlier onset of strength increase, indicating different material responses under varying environmental conditions.
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(This article belongs to the Special Issue Transformation in the Building Design and Construction Industry: Smart Technologies, Sustainable Practices, and Artificial Intelligence Applications)
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