Exploring the Potential of Lateritic Aggregates in Pervious Concrete: A Study on Mechanical Properties and Predictive Techniques
This study investigates the mechanical properties of pervious concrete incorporating river lateritic and quarry lateritic aggregates as sustainable alternatives to conventional aggregates. The research aims to evaluate the compressive strength, split tensile strength, and permeability of pervious co...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | CivilEng |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4109/6/2/30 |
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| Summary: | This study investigates the mechanical properties of pervious concrete incorporating river lateritic and quarry lateritic aggregates as sustainable alternatives to conventional aggregates. The research aims to evaluate the compressive strength, split tensile strength, and permeability of pervious concrete mixes with varying void ratios (20% and 24%) and aggregate sizes. The results indicate that pervious concrete containing quarry lateritic aggregates exhibits superior permeability due to its inherent porosity, while river lateritic aggregates provide relatively better compressive strength than quarry aggregates. However, both lateritic aggregates show lower mechanical strength than conventional pervious concrete. Additionally, Python-based predictive models employing multi-linear regression were developed to estimate compressive strength based on independent variables such as binder quantity, coarse aggregate content, water-to-cement ratio, and curing duration. The predictive models achieved R<sup>2</sup> values of 0.69 for 7-day compressive strength and 0.82 for 28-day compressive strength, indicating strong predictive capabilities. This research highlights the potential of locally sourced materials in enhancing the sustainability of construction practices while offering valuable insights into the mechanical performance of pervious concrete and the utility of computational modeling for predicting concrete properties. |
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| ISSN: | 2673-4109 |