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Alternative Fillers in Asphalt Concrete Mixtures: Laboratory Investigation and Machine Learning Modeling towards
Nitin Tiwari1,2, Fabio Rondinella3, Neelima Satyam2
1Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA.
Materials (Basel, Switzerland)
|January 21, 2023
Summary
Researchers explored using industrial waste like rice husk ash and fly ash as alternative fillers in asphalt for road construction. Limestone dust and silica fume showed promising mechanical strength and durability, meeting Indian standards.
Area of Science:
- Civil Engineering
- Materials Science
- Environmental Engineering
Background:
- Growing scarcity of natural resources drives research into reusing industrial waste.
- Recycled materials offer sustainable alternatives in civil engineering applications, particularly for road construction.
Purpose of the Study:
- To assess the feasibility of using seven industrial by-products as alternative fillers in asphalt mixtures for road pavement base layers.
- To evaluate the impact of these fillers on the mechanical properties and durability of asphalt.
Main Methods:
- Marshall tests, indirect tensile strength, moisture susceptibility, and Cantabro abrasion loss tests were conducted.
- Seven alternative fillers were tested: rice husk ash (RHA), brick dust (BD), marble dust (MD), stone dust (SD), fly ash (FA), limestone dust (LD), and silica fume (SF).
- A Machine Learning model (Categorical Boosting) was developed to predict mechanical properties based on composition.
Main Results:
- Most mixtures met Indian standards for air voids, Marshall stability, and quotient, except for those with 4% RHA or MD.
- Limestone dust (LD) and silica fume (SF) mixtures exhibited superior mechanical strength and durability compared to ordinary Portland cement (OPC).
- The Machine Learning model achieved high prediction accuracy (mean R² of 0.9374).
Conclusions:
- Industrial by-products like LD and SF can be effectively recycled as fillers in asphalt mixtures, enhancing mechanical performance.
- The study validates the use of sustainable materials in road construction, reducing reliance on virgin resources.
- Machine Learning provides a reliable tool for predicting asphalt mixture performance based on filler composition.
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