Optimizing coconut fiber-modified hot mix asphalt for enhanced mechanical performance using response surface methodology

Coconut products such as oil, milk powder, activated carbon, and desiccated coconut are increasingly in demand, leading to higher coconut production and a surplus of coconut fibers. Despite their excellent physical and mechanical properties, these fibers are often discarded or burned due to limited research into alternative uses, contributing to environmental pollution. This study evaluates the potential of coconut fibers in hot mix asphalt (HMA) to reduce waste and enhance their mechanical performance. Central composite design (CCD) was adopted to optimize fiber-modified HMA mixes using response surface methodology (RSM) based on Marshall testing. Sixty Marshall samples with varying fiber content, bitumen content, and fiber length were prepared to develop the RSM model based on 20 runs. Fiber content (%), fiber length (mm), and bitumen content (%) were considered as factors, while marshall stability (KN) and flow (mm) were taken as responses. The optimized mix, containing 0.28% coconut fibers (approximately 13 mm in length) and 4. 16% bitumen, achieved a marshall stability of 18. 02 kN and a flow of 3.12 mm. Validation of the optimized solution with the experimental trials showed an error of 7.05% for marshall stability and 6. 11% for marshall flow. Indirect tensile strength testing showed a 5% higher tensile strength for the optimized dry mix compared to the 1.29 KN observed for control samples. Furthermore, the tensile strength ratio between dry and wet samples was recorded to be higher than the threshold of 80% for both control and optimized HMA mixes. Moreover, the indirect tensile stiffness modulus (ITSM) for control samples recorded at 5 °C was higher than the optimized mix. However, the optimized HMA mixes resulted in around 13%, 6%, and 2.16% higher ITSM at 15 °C, 20 °C, and 25 °C, respectively, in reference to the control mix. Furthermore, the indirect tensile fatigue testing revealed that the control mix performed better than the optimized mix. Nonetheless, the optimized mix showed steady behavior against stress variation as compared to the control mix. Overall, this study demonstrates the effective use of RSM to optimize the Marshall mix design, reducing laboratory testing. Additionally, it was observed that optimized fiber-modified HMA mixes exhibit superior mechanical properties compared to control samples, paving the roads for sustainable and efficient asphalt technologies.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-04-29
DOI
https://doi.org/10.1038/s41598-025-98234-2
Citations
6
Primary Topic
Asphalt Pavement Performance Evaluation
Type
article
Field-Weighted Citation Impact
5.72

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing coconut fiber-modified hot mix asphalt for enhanced mechanical performance using response surface methodology

Shabir Hussain Khahro, Muslich Hartadi Sutanto, Inamullah Khan, Mohammad Abdul Malik et al.
6 citations
Scientific Reports
Asphalt Pavement Performance Evaluation
5.72
article

Optimizing coconut fiber-modified hot mix asphalt for enhanced mechanical performance using response surface methodology

Shabir Hussain Khahro, Muslich Hartadi Sutanto, Inamullah Khan, Mohammad Abdul Malik, Nasir Khan
article en
6 citations

Abstract

Coconut products such as oil, milk powder, activated carbon, and desiccated coconut are increasingly in demand, leading to higher coconut production and a surplus of coconut fibers. Despite their excellent physical and mechanical properties, these fibers are often discarded or burned due to limited research into alternative uses, contributing to environmental pollution. This study evaluates the potential of coconut fibers in hot mix asphalt (HMA) to reduce waste and enhance their mechanical performance. Central composite design (CCD) was adopted to optimize fiber-modified HMA mixes using response surface methodology (RSM) based on Marshall testing. Sixty Marshall samples with varying fiber content, bitumen content, and fiber length were prepared to develop the RSM model based on 20 runs. Fiber content (%), fiber length (mm), and bitumen content (%) were considered as factors, while marshall stability (KN) and flow (mm) were taken as responses. The optimized mix, containing 0.28% coconut fibers (approximately 13 mm in length) and 4. 16% bitumen, achieved a marshall stability of 18. 02 kN and a flow of 3.12 mm. Validation of the optimized solution with the experimental trials showed an error of 7.05% for marshall stability and 6. 11% for marshall flow. Indirect tensile strength testing showed a 5% higher tensile strength for the optimized dry mix compared to the 1.29 KN observed for control samples. Furthermore, the tensile strength ratio between dry and wet samples was recorded to be higher than the threshold of 80% for both control and optimized HMA mixes. Moreover, the indirect tensile stiffness modulus (ITSM) for control samples recorded at 5 °C was higher than the optimized mix. However, the optimized HMA mixes resulted in around 13%, 6%, and 2.16% higher ITSM at 15 °C, 20 °C, and 25 °C, respectively, in reference to the control mix. Furthermore, the indirect tensile fatigue testing revealed that the control mix performed better than the optimized mix. Nonetheless, the optimized mix showed steady behavior against stress variation as compared to the control mix. Overall, this study demonstrates the effective use of RSM to optimize the Marshall mix design, reducing laboratory testing. Additionally, it was observed that optimized fiber-modified HMA mixes exhibit superior mechanical properties compared to control samples, paving the roads for sustainable and efficient asphalt technologies.

Scientific ReportsVol. 15(1)
Prince Sultan University (SA), National University of Technology (PK), National Institute of Health (PK)
Universiti Teknologi Petronas, Prince Sultan University
Zero hunger
Openalex Percentile: Top 4%
Asphalt Pavement Performance Evaluation
5.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.