Machine-Learning Prediction of Marshall Mix-Design Properties and Mechanical Characterization of DCR-Treated Asphaltic Concrete
DOI:
https://doi.org/10.31436/iiumej.v27i3.4533Keywords:
Asphalt, Support Vector Machines (SVM), hyperparameter optimizationAbstract
Marshall mix design remains the dominant empirical framework for proportioning hot-mix asphalt, and coconut-derived by-products such as Desiccated Coconut Residue (DCR) have attracted interest as sustainable bitumen modifiers. Determining the optimum bitumen content (OBC) and characterizing mechanical performance is laboratory-intensive, which motivates data-driven prediction. This study evaluated whether regression models can predict six Marshall responses for DCR-modified asphalt concrete and characterized the mechanical performance of the modified mixtures at their respective OBC. Thirty averaged Marshall records covering bitumen contents of 4–6 %, two gradations (AC14, AC20), and three treatments (Control, untreated DCR, treated DCR) were modeled using eight algorithms and a quadratic response-surface baseline. Models were assessed by pooled out-of-fold prediction under random K-fold, leave-one-mixture-out, and leave-one-bitumen-level-out validation. For the AC14 mixtures, OBC values were 5.30% (Control), 4.95% (untreated), and 5.10% (treated); resilient modulus, indirect tensile strength (ITS), tensile strength ratio (TSR), and dynamic creep were measured for each mixture prepared at its own OBC. Pooled out-of-fold R² reached 0.89 for voids filled with bitumen, 0.88 for air voids, 0.83 for bulk density, 0.81 for voids in mineral aggregate, 0.53 for flow, and 0.49 for stability. The quadratic response surface matched or exceeded the machine-learning models on four of six responses. Under leave-one-mixture-out validation, R² became negative for most responses, whereas leave-one-bitumen-level-out validation retained R² of 0.47–0.91. The models are therefore interpolation tools within the tested design space rather than general predictors. The untreated mixture required the lowest binder content, and the treated mixture recorded the highest dry ITS (1,148.7 kPa) and TSR (50.3%), although all mixtures fell below common TSR acceptance limits despite the hydrated-lime filler.
ABSTRAK: Reka bentuk campuran Marshall, kekal sebagai kerangka empirik utama bagi menentukan kadar campuran asfalt panas, dan hasil sampingan berasaskan kelapa seperti Sisa Kelapa Kering (DCR) telah menarik minat pengubah suai bitumen yang mampan. Penentuan kandungan bitumen optimum (OBC) dan pencirian prestasi mekanikal memerlukan kerja makmal yang intensif, sekaligus mendorong penggunaan ramalan berasaskan data. Kajian ini menilai sama ada model regresi mampu meramal enam tindak balas Marshall bagi konkrit asfalt terubah suai DCR, serta mencirikan prestasi mekanikal campuran tersebut pada OBC masing-masing. Sebanyak 30 rekod Marshall purata merangkumi kandungan bitumen 4–6 %, dua gradasi (AC14, AC20) dan tiga rawatan (Kawalan, DCR tidak dirawat, dan DCR dirawat) dimodelkan menggunakan lapan algoritma serta model permukaan sambutan kuadratik sebagai asas perbandingan. Model dinilai melalui ramalan terkumpul luar-lipatan di bawah pengesahan K-lipatan rawak, tinggal-satu-campuran-keluar dan tinggal-satu-aras-bitumen-keluar. Bagi campuran AC14, nilai OBC ialah 5.30 % (Kawalan), 4.95 % (tidak dirawat) dan 5.10 % (dirawat). Modulus kenyal, kekuatan tegangan tak langsung (ITS), nisbah kekuatan tegangan (TSR) dan rayapan dinamik diukur dengan setiap campuran yang disediakan pada OBC tersendiri. R² terkumpul luar-lipatan mencapai 0.89 bagi lompang terisi bitumen, 0.88 bagi lompang udara, 0.83 bagi ketumpatan pukal, 0.81 bagi lompang dalam agregat mineral, 0.53 bagi aliran dan 0.49 bagi kestabilan. Model permukaan sambutan kuadratik menyamai atau mengatasi model pembelajaran mesin bagi empat daripada enam tindak balas. Di bawah pengesahan tinggal-satu-campuran-keluar, R² menjadi negatif bagi kebanyakan tindak balas, manakala pengesahan tinggal-satu-aras-bitumen-keluar mengekalkan R² 0.47–0.91. Justeru model ini merupakan alat interpolasi dalam ruang reka bentuk yang diuji dan bukan peramal umum. Campuran tidak dirawat memerlukan kandungan pengikat terendah, manakala campuran dirawat mencatat ITS kering tertinggi (1,148.7 kPa) dan TSR tertinggi (50.3 %), walaupun semua campuran berada di bawah had penerimaan TSR lazim.
Downloads
Metrics
References
Hunter RN (Ed.) (2015). The Shell Bitumen Handbook, 6th Ed. ICE Publishing, London.
JKR Malaysia (2008). Standard Specification for Road Works, Section 4: Flexible Pavement (JKR/SPJ/2008-S4). Jabatan Kerja Raya Malaysia, Kuala Lumpur.
Hussan S, Kamal MA, Hafeez I, Ahmad N (2019). Evaluation and modelling of permanent deformation behaviour of asphalt mixtures using dynamic creep test in uniaxial mode. International Journal of Pavement Engineering, 20(9):1026–1043. https://doi.org/10.1080/10298436.2017.1380805
Mashaan NS, Karim MR (2013). Evaluation of permanent deformation of CRM-reinforced SMA and its correlation with dynamic stiffness and dynamic creep. The Scientific World Journal, 2013:981637. https://doi.org/10.1155/2013/981637
Solaimanian M, Harvey J, Tahmoressi M, Tandon V (2003). Test methods to predict moisture sensitivity of hot-mix asphalt pavements. In Moisture Sensitivity of Asphalt Pavements: A National Seminar, pp. 77–110. Transportation Research Board, Washington, DC.
Do TC (2024). Engineering properties-based parameters used for moisture damage evaluation of asphalt mixtures: A review. Canadian Journal of Civil Engineering, 51(4):390–398. https://doi.org/10.1139/cjce-2023-0243
Mashaan NS, Ali AH, Karim MR, Abdelaziz M (2014). A review on using crumb rubber in reinforcement of asphalt pavement. The Scientific World Journal, 2014:214612. https://doi.org/10.1155/2014/214612
Kumar B, Alyaseen A, Kumar N (2025). Utilizing conventional and state-of-the-art machine learning algorithms to predict Marshall stability of modified asphalt mixes incorporating PET, HDPE, and PVC plastic waste: Performance evaluation and mix optimization. International Journal of Pavement Research and Technology. https://doi.org/10.1007/s42947-025-00591-8
Asphalt Institute (2014). Mix Design Methods for Asphalt Concrete and Other Hot-Mix Types (MS-2), 7th Ed. Asphalt Institute, Lexington, KY.
Asi I, Alhadidi YI, Alhadidi TI (2024). Predicting Marshall stability and flow parameters in asphalt pavements using explainable machine-learning models. Transportation Engineering, 18:100282. https://doi.org/10.1016/j.treng.2024.100282
Awan HH, Hussain A, Javed MF, Qiu Y, Alrowais R, Mohamed AM, Fathi D, Alzahrani AM (2022). Predicting Marshall flow and Marshall stability of asphalt pavements using Multi Expression Programming. Buildings, 12(3):314. https://doi.org/10.3390/buildings12030314
Al-Ammari M, Dong R, Nasser M, Al-Maswari A (2025). Innovative machine learning approaches for predicting the asphalt content during Marshall design of asphalt mixtures. Materials, 18(7):1474. https://doi.org/10.3390/ma18071474
Al Mamun A, Gazder U, Islam MK, Arifuzzaman M, Wahhab HA-A, Rahman MM (2025). Predicting indirect tensile strength of rejuvenated asphalt mixes using machine learning with high reclaimed asphalt pavement content. Processes, 13(5):1489. https://doi.org/10.3390/pr13051489
Jamil MH, Jagirdar R, Kashem A, Ali MN, Deb D (2025). Modeling of Marshall stability of plastic-reinforced asphalt concrete using machine learning algorithms and SHAP. Hybrid Advances, 10:100483. https://doi.org/10.1016/j.hybadv.2025.100483
Cawley GC, Talbot NLC (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research, 11:2079–2107.
Tibshirani R (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Zou H, Hastie T (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x
Cortes C, Vapnik V (1995). Support-vector networks. Machine Learning, 20(3):273–297. https://doi.org/10.1007/BF00994018
Breiman L (2001). Random forests. Machine Learning, 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Friedman JH (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189–1232. https://doi.org/10.1214/aos/1013203451
Geurts P, Ernst D, Wehenkel L (2006). Extremely randomized trees. Machine Learning, 63(1):3–42. https://doi.org/10.1007/s10994-006-6226-1
Hastie T, Tibshirani R, Friedman J (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Ed. Springer-Verlag, New York.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Upadhya A, Thakur MS, Al Ansari MS, Malik MA, Alahmadi AA, Alwetaishi M, Alzaed AN (2022). Marshall stability prediction with glass and carbon fiber modified asphalt mix using machine learning techniques. Materials, 15(24):8944. https://doi.org/10.3390/ma15248944
ASTM (2020). Standard test method for determining the resilient modulus of asphalt mixtures by indirect tension test (ASTM D7369-20). ASTM International, West Conshohocken, PA. https://doi.org/10.1520/D7369-20
Shafabakhsh G, Tanakizadeh A (2015). Investigation of loading features effects on resilient modulus of asphalt mixtures using Adaptive Neuro-Fuzzy Inference System. Construction and Building Materials, 76:256–263. https://doi.org/10.1016/j.conbuildmat.2014.11.069
Lottman RP (1982). Predicting moisture-induced damage to asphaltic concrete: Field evaluation. NCHRP Report 246. Transportation Research Board, Washington, DC.
AASHTO (2014). Standard method of test for resistance of compacted asphalt mixtures to moisture-induced damage (AASHTO T283-14). American Association of State Highway and Transportation Officials, Washington, DC.
Yang Q, Lin J, Wang X, Wang D, Xie N, Shi X (2024). A review of polymer-modified asphalt binder: Modification mechanisms and mechanical properties. Cleaner Materials, 12:100255. https://doi.org/10.1016/j.clema.2024.100255
Lundberg SM, Lee S-I (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30:4765–4774.
Yousif RA, Tayh SA, Jasim AF (2023). The effect of coconut powder on asphalt binder performance under laboratory conditions. Journal of Engineering and Technological Sciences, 55(5):577–586. https://doi.org/10.5614/j.eng.technol.sci.2023.55.5.7
Ting TL, Jaya RP, Hassan NA, Yaacob H, Jayanti DS (2015). A review of utilization of coconut shell and coconut fiber in road construction. Jurnal Teknologi, 76(14):121–125. https://doi.org/10.11113/jt.v76.5851
Mior Sani WNH, Allias NF, Yaacob H, Al-Saffar ZH, Hashim MH (2024). Utilisation of sawdust and charcoal ash as sustainable modified bitumen. Smart and Green Materials, 1(1):13–32. https://doi.org/10.70028/sgm.v1i1.1
ASTM (2019). Standard test method for penetration of bituminous materials (ASTM D5/D5M-20). ASTM International, West Conshohocken, PA. https://doi.org/10.1520/D0005_D0005M-20
ASTM (2014). Standard test method for softening point of bitumen (ring-and-ball apparatus) (ASTM D36/D36M-14). ASTM International, West Conshohocken, PA. https://doi.org/10.1520/D0036_D0036M-14
ASTM (2015). Standard test method for viscosity determination of asphalt at elevated temperatures using a rotational viscometer (ASTM D4402/D4402M-15). ASTM International, West Conshohocken, PA. https://doi.org/10.1520/D4402_D4402M-15
CEN (2016). Bituminous mixtures — Test methods — Part 25: Cyclic compression test (EN 12697-25:2016). European Committee for Standardization, Brussels.
ASTM (2019). Standard test method for indirect tensile (IDT) strength of bituminous mixtures (ASTM D6931-17). ASTM International, West Conshohocken, PA. https://doi.org/10.1520/D6931-17
Smola AJ, Schölkopf B (2004). A tutorial on support vector regression. Statistics and Computing, 14(3):199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 IIUM Press

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.








