An Assessment of Tree-Based Models for Predicting Indirect Tensile Stiffness Modulus in Fiber-Enhanced Cold Mix Asphalt
Received: 20 June 2025 | Revised: 17 July 2025 and 20 July 2025 | Accepted: 23 July 2025 | Online: 6 October 2025
Corresponding author: Mohammed Hazim Mohammed
Abstract
This study focuses on forecasts of the Indirect Tensile Stiffness Modulus (ITSM) of fiber-reinforced Cold Mix Asphalt (CMA), using a CHAID (Chi-Squared Automatic Interaction Detector) model of tree-based machine learning methods. One of the most important advantages of the CHAID model is its simplicity when it comes to little training. This study employed 123 laboratory samples, taken on predetermined essential elements, fibre content and type, fibre length, curing time, and air voids. The CHAID model was evaluated through several performance indicators, including R² (coefficient of determination) and RMSE (Root Mean Square Error). The proposed model had an R² of 0.913, an Average Accuracy Percentage (AA%) of 16.16%, and an RMSE of 170.2 MPa. Feature importance showed both curing time and air voids as the main factors that affect ITSM. This study demonstrates the practicality of the CHAID model as a transparent and interpretable model to forecast stiffness, offering a wise choice in designing heavy-duty sustainable fiber-enhanced CMA mixtures for pavements.
Keywords:
gene expression programming, machine learning, fiber-reinforced cold mix asphalt, indirect tensile stiffness modulus, modelingDownloads
References
Z. Sun, Y. Ma, S. Liu, Y. Li, X. Qiu, and Z. Luo, "Evaluation of Engineering Properties of Fiber-reinforced Usual-temperature Synthetic Pitch (USP) modified cold mix patching asphalt," Case Studies in Construction Materials, vol. 16, Jun. 2022, Art. no. e00997.
G. Ferrotti, E. Pasquini, and F. Canestrari, "Experimental characterization of high-performance fiber-reinforced cold mix asphalt mixtures," Construction and Building Materials, vol. 57, pp. 117–125, Apr. 2014.
A. Dulaimi, H. Al Nageim, F. Ruddock, and L. Seton, "New developments with cold asphalt concrete binder course mixtures containing binary blended cementitious filler (BBCF)," Construction and Building Materials, vol. 124, pp. 414–423, Oct. 2016.
H. K. Shanbara, F. Ruddock, and W. Atherton, "A laboratory study of high-performance cold mix asphalt mixtures reinforced with natural and synthetic fibres," Construction and Building Materials, vol. 172, pp. 166–175, May 2018.
H. Chen, Q. Xu, S. Chen, and Z. Zhang, "Evaluation and design of fiber-reinforced asphalt mixtures," Materials & Design, vol. 30, no. 7, pp. 2595–2603, Aug. 2009.
H. K. Shanbara, F. Ruddock, and W. Atherton, "Predicting the rutting behaviour of natural fibre-reinforced cold mix asphalt using the finite element method," Construction and Building Materials, vol. 167, pp. 907–917, Apr. 2018.
S. M. Abtahi, M. Sheikhzadeh, and S. M. Hejazi, "Fiber-reinforced asphalt-concrete – A review," Construction and Building Materials, vol. 24, no. 6, pp. 871–877, Jun. 2010.
W. Ben Chaabene, M. Flah, and M. L. Nehdi, "Machine learning prediction of mechanical properties of concrete: Critical review," Construction and Building Materials, vol. 260, Nov. 2020, Art. no. 119889.
A. M. Ahmed, M. I. Ali, and A. H. Abbas, "Recycling of Disposal Syringe Needles to Produce Fiber Reinforced Concrete," International Review of Civil Engineering (IRECE), vol. 9, no. 4, pp. 148–153, Jul. 2018.
M. I. Ali, A. A. J. Jamel, and S. I. Ali, "The hardened characteristics of self-compacting mortar including carbon fibers and estimation results by artificial neural networks," AIP Conference Proceedings, vol. 2213, no. 1, Mar. 2020, Art. no. 020159.
S. Rahman, A. Bhasin, and A. Smit, "Exploring the use of machine learning to predict metrics related to asphalt mixture performance," Construction and Building Materials, vol. 295, Aug. 2021, Art. no. 123585.
J. Zhao and H. Wang, "Machine learning based pavement performance prediction for data-driven decision of asphalt pavement overlay," Structure and Infrastructure Engineering, vol. 21, no. 6, pp. 940–955, Jun. 2025.
A. Eleyedath and A. K. Swamy, "Prediction of dynamic modulus of asphalt concrete using hybrid machine learning technique," International Journal of Pavement Engineering, vol. 23, no. 6, pp. 2083–2098, May 2022.
R. Y. Liang and J. Zhou, "Prediction of fatigue life of asphalt concrete beams," International Journal of Fatigue, vol. 19, no. 2, pp. 117–124, Jan. 1997.
A. M. Ahmed, S. I. Ali, M. I. Ali, and A. A. J. Jamel, "Analyzing Self-Compacted Mortar Improved by Carbon Fiber Using Artificial Neural Networks," Annales de Chimie - Science des Matériaux, vol. 47, no. 6, pp. 363–369, Dec. 2023.
M. I. Ali and A. A. Allawi, "An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18277–18282, Dec. 2024.
A. Nazari and F. Pacheco Torgal, "Predicting compressive strength of different geopolymers by artificial neural networks," Ceramics International, vol. 39, no. 3, pp. 2247–2257, Apr. 2013.
M. Rathnayaka, D. Karunasinghe, C. Gunasekara, K. Wijesundara, W. Lokuge, and D. W. Law, "Machine learning approaches to predict compressive strength of fly ash-based geopolymer concrete: A comprehensive review," Construction and Building Materials, vol. 419, Mar. 2024, Art. no. 135519.
M. Abdel-Mongy, M. Iqbal, M. Farag, Ahmed. M. Yosri, F. Alsharari, and S. E. A. S. Yousef, "Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete," Computer Modeling in Engineering & Sciences, vol. 141, no. 1, pp. 525–543, 2024.
A. H. Gandomi and A. H. Alavi, "A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems," Neural Computing and Applications, vol. 21, no. 1, pp. 171–187, Feb. 2012.
C. Ferreira, Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Springer, 2006.
C. Ma, S. Wang, J. Zhao, X. Xiao, C. Xie, and X. Feng, "Prediction of shear strength of RC deep beams based on interpretable machine learning," Construction and Building Materials, vol. 387, Jul. 2023, Art. no. 131640.
A. F. Ashour, L. F. Alvarez, and V. V. Toropov, "Empirical modelling of shear strength of RC deep beams by genetic programming," Computers & Structures, vol. 81, no. 5, pp. 331–338, Mar. 2003.
M. A. Ali Khan, A. Zafar, A. Akbar, M. F. Javed, and A. Mosavi, "Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete," Materials, vol. 14, no. 5, Feb. 2021, Art. no. 1106.
G. Abdollahzadeh, E. Jahani, and Z. Kashir, "Predicting of compressive strength of recycled aggregate concrete by genetic programming," Computers and Concrete, vol. 18, no. 2, pp. 155–163, Aug. 2016.
V. Kaushik and M. Kumar, "Sustainable gene expression programming model for shear stress prediction in nonprismatic compound channels," Sustainable Energy Technologies and Assessments, vol. 57, Jun. 2023, Art. no. 103229.
S. Jahanmiri, M. Noorian-Bidgoli, and A. Dallaki, "Optimizing tunnel boring machine penetration rate prediction using gene expression programming and whale optimization algorithms," Modeling Earth Systems and Environment, vol. 11, no. 2, Apr. 2025, Art. no. 133, https://doi.org/10.1007/s40808-025-02323-0.
A. Mollahasani, A. H. Alavi, and A. H. Gandomi, "Empirical modeling of plate load test moduli of soil via gene expression programming," Computers and Geotechnics, vol. 38, no. 2, pp. 281–286, Mar. 2011, https://doi.org/10.1016/j.compgeo.2010.11.008.
N. A. M. Shukry et al., "Influence of diatomite filler on rheological properties of porous asphalt mastic," International Journal of Pavement Engineering, vol. 21, no. 4, pp. 428–436, Mar. 2020.
A. Alfalah, D. Offenbacker, A. Ali, Y. Mehta, M. Elshaer, and C. Decarlo, "Evaluating the impact of fiber type and dosage rate on laboratory performance of Fiber-Reinforced asphalt mixtures," Construction and Building Materials, vol. 310, Dec. 2021, Art. no. 125217.
Q. Ye, S. Wu, and N. Li, "Investigation of the dynamic and fatigue properties of fiber-modified asphalt mixtures," International Journal of Fatigue, vol. 31, no. 10, pp. 1598–1602, Oct. 2009.
J. S. Chou, T. P. T. Pham, T. K. Nguyen, A. D. Pham, and N. T. Ngo, "Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models," Soft Computing, vol. 24, no. 5, pp. 3393–3411, Mar. 2020.
F. Aslam et al., "Applications of Gene Expression Programming for Estimating Compressive Strength of High‐Strength Concrete," Advances in Civil Engineering, vol. 2020, no. 1, Jan. 2020, Art. no. 8850535.
V. Bherde and U. Balunaini, "Application of Tree-Based Ensemble Techniques to Predict Subgrade Resilient Modulus of Soils," in Proceedings of 9IYGEC 2023, Volume 1, vol. 605, N. Satyam, A. P. Singh, and M. S. Dixit, Eds. Springer Nature Singapore, 2025, pp. 199–208.
T. Wen, H. Lang, S. Ding, J. J. Lu, and Y. Xing, "PCDNet: Seed Operation–Based Deep Learning Model for Pavement Crack Detection on 3D Asphalt Surface," Journal of Transportation Engineering, Part B: Pavements, vol. 148, no. 2, Jun. 2022, Art. no. 04022023.
M. I. Ali, A. A. J. Jamel, and S. I. Ali, "The hardened characteristics of self-compacting mortar including carbon fibers and estimation results by artificial neural networks," presented at the 2nd International Conference on Materials Engineering and Science (IConMEAS 2019), Baghdad, Iraq, 2020, Art. no. 020159.
C. McCue, Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis. Butterworth-Heinemann, 2014.
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