Machine-learning prediction of Marshall mix-design properties and mechanical characterization of DCR-treated asphaltic concrete

Kamaruzaman, Nur Atikah and Wan Azahar, Wan Nur Aifa and Nadimalla, Altamashuddin Khan and Kasim, Norhidayu and Masjuki, Siti Aliyyah and Nor Hairin, Assayidatul Laila and Ismail, Norfarah Nadia and Bujang, Mastura (2026) Machine-learning prediction of Marshall mix-design properties and mechanical characterization of DCR-treated asphaltic concrete. IIUM Engineering Journal, 27 (3). pp. 70-88. ISSN 1511-788X E-ISSN 2289-7860

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Abstract

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

Item Type:	Article (Journal)
Uncontrolled Keywords:	Asphalt, Support Vector Machines (SVM), hyperparameter optimization
Subjects:	T Technology > TE Highway engineering. Roads and pavements T Technology > TE Highway engineering. Roads and pavements > TE250 Pavement and paved roads
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Civil Engineering
Depositing User:	Dr. WAN NUR AIFA WAN AZAHAR
Date Deposited:	21 Sep 2026 14:47
Last Update:	21 Sep 2026 14:47
Queue Number:	2026-09-Q5161
URI:	http://irep.iium.edu.my/id/eprint/131342
Indexed In:	SCOPUS, ERA, Google Scholar, MyCite

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