[1] D. Berrar, N. Sato, A. Schuster, Quo vadis, artificial intelligence?, Advances in Artificial Intelligence, 2010(1) (2010) 629869.
[2] L. Morgenstern, and S.A. McIlraith, John McCarthy's legacy, Artificial Intelligence, 175(1) (2011) 1-24.
[3] Y. Xu, X. Liu, X. Cao, C. Huang, E. Liu, S. Qian, X. Liu, Y. Wu, F. Dong, C.-W. Qiu, J. Qiu, K. Hua, W. Su, J. Wu, H. Xu, Artificial intelligence: A powerful paradigm for scientific research, The Innovation, 2(4) (2021).
[4]- G. Tecuci, Artificial Intelligence, WIREs Computational Statistics, 4(2) (2012) 168-180.
[5] V. Dignum, Responsible Artificial Intelligence: Designing AI for human values, ITU Journal: ICT Discoveries, 1 (2017).
[6] P. Lu, S. Chen, Y. Zheng, Artificial intelligence in civil engineering, Mathematical Problems in Engineering, 1 (2012) 145974.
[7] Y. Zhang, S. Balochian, P. Agarwal, V. Bhatnagar, O.J. Housheya, Artificial intelligence and its applications, Mathematical problems in Engineering, 1 (2014) 840491.
[8] R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, F. De Felice, Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions, Sustainability, 12(2) (2020) 492.
[9] M. Abambres, A. Ferreira, Application of ANN in Pavement Engineering: State-of-Art, (2017).
[10] B. Ghorbania, A. Arulrajaha, G. Narsiliob, S. Horpibulsuk, Experimental and ANN analysis of temperature effects on the permanent deformation properties of demolition wastes, Transportation Geotechnics, 24 (2020) 100365.
[11] K. Kersting, Machine learning and artificial intelligence: two fellow travelers on the quest for intelligent behavior in machines, Frontiers in big Data, 1 (2018) 6-10.
[12] M.I. Jordan, T.M. Mitchell, Machine learning: trends, perspectives, and prospects, Science, 349 (2015) 255–260.
[13] T. Xu, F. Zhu, E.K. Wong, Y. Fang, Dual many-to-one-encoder-based transfer learning for cross-dataset human action recognition, Image and Vision Computing, 55(2) (2016) 127–137.
[14] E.P. Ijjina, K.M. Chalavadi, Human action recognition using genetic algorithms and convolutional neural networks, Pattern Recognition, 59 (2016) 199–212.
[15] Z. Tong, J. Gao, H. Zhang. Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks, Construction and Building Materials, 146 (2017) 775–787.
[16] Z. Du, J. Yuan, F. Xiao, C. Hettiarachchi, Application of image technology on pavement distress detection: A review, Measurement, 184 (2021) 109900.
[17] J. Bryce, S. Brodie, T. Parry, D.L. Presti, A systematic assessment of road pavement sustainability through a review of rating tools, Resources, Conservation and Recycling, 120 (2017) 108-118.
[18] X. Yang, J. Guan, L. Ding, Z. You, V.C. Lee, M.R.M. Hasan, X. Cheng, Research and applications of artificial neural network in pavement engineering: A state-of-the-art review, Journal of Traffic and Transportation Engineering (English Edition), 8(6) (2021) 1000-1021.
[19] N. Sholevar, A. Golroo, S.R. Esfahani, Machine learning techniques for pavement condition evaluation. Automation in Construction, 136 (2021) 104190.
[20] T.B.J. Coenen, A. Golroo, A review on automated pavement distress detection methods, Cogent Engineering, 4(1) (2017) 1374822.
[21] Y. Xu, Z. Zhang, Review of applications of artificial intelligence algorithms in pavement management. Journal of Transportation Engineering, Part B: Pavements, 148(3) (2022) 03122001.
[22] S. Cano-Ortiz, P. Pascual-Muñoz, D. Castro-Fresno, Machine learning algorithms for monitoring pavement performance. Automation in Construction, 139 (2022) 104309.
[23] J. Kang, P. Tavassoti, M.N.A.R. Chaudhry, H. Baaj, M. Ghafurian, Artificial intelligence techniques for pavement performance prediction: a systematic review, Road Materials and Pavement Design, 26(3) (2025) 497-522.
[24] M. Abambres, A. Ferreira, Application of ANN in pavement engineering: State-of-Art. Authorea Preprints, (2017).
[25] X. Yang, J. Guan, L. Ding, Z. You, V.C. Lee, M.R.M. Hasan, X. Cheng, Research and applications of artificial neural network in pavement engineering: a state-of-the-art review, Journal of Traffic and Transportation Engineering (English Edition), 8(6) (2021) 1000-1021.
[26] Y. Wang, J. Li, X. Zhang, Y. Yao, Y. Peng, Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning, Sensors, 24(9) (2024) 2777.
[27] W. Sha, Y. Guo, Q. Yuan, S. Tang, X. Zhang, S. Lu, X. Guo, Y.-C. Cao, and S. Cheng. Artificial Intelligence to Power the Future of Materials Science and Engineering. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
[28] Y. Duan, J.S. Edwards, Y.K. Dwivedi, Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda, International Journal of Information Management, 48 (2019) 63-71.
[29] S. Mouloodi, H. Rahmanpanah, S. Gohari, C. Burvill, K.M. Tse, H.M.S. Davies, What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research, Journal of the Mechanical Behavior of Biomedical Materials, 123 (2021) 104728.
[30] F. Yang, X. Zhang, Z. Yu, Human-machine collaborative learning for streaming data-driven scenarios. Sensors, 25(21) (2025) 6505.
[31] K. Razzaq, and M. Shah, Machine learning and deep learning paradigms: From techniques to practical applications and research frontiers, Computers, 14(3) (2025) 93.
[32] M.H.M., Noor, A.O. Ige, A survey on state-of-the-art deep learning applications and challenges, Engineering Applications of Artificial Intelligence, 159 (2025) 111225.
[33] I.H. Sarker, Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions, SN computer science, 2(6) (2021) 1-20.
[34] M. Soori, B. Arezoo, R. Dastres, Artificial intelligence, machine learning and deep learning in advanced robotics, a review, Cognitive Robotics, 3 (2023) 54-70.
[35] A.L. Fradkov, Early history of machine learning, IFAC-Papers OnLine, 53(2) (2020) 1385-1390.
[36] H.S. Greenwald, C.K. Oertel, Future directions in machine learning, Frontiers in Robotics and AI, 3 (2017) 79.
[37] K. Rajan, Materials informatics, Materials Today, 8(10) (2005) 38-45.
[38] D. Wang, H. He, D. Liu, Adaptive critic nonlinear robust control: a survey, IEEE transactions on cybernetics, 47(10) (2017) 3429-3451.
[39] M. Nikzad, K. Movagharnejad, F. Talebnia, Comparative study between neural network model and mathematical models for prediction of glucose concentration during enzymatic hydrolysis, International Journal of Computer Applications, 56(1) (2012).
[40] M. Lazarevska, M. Knezevic, M. Cvetkovska, A. Trombeva-Gavriloska, Application of artificial neural networks in civil engineering, Tehnički vjesnik, 21(6) (2014) 1353-1359.
[41] A.M. Mosa, R.A.O.K Rahmat, A. Ismail, M.R. Taha. Expert system to control construction problems in flexible pavements, Computer‐Aided Civil and Infrastructure Engineering, 28(4) (2013) 307-323.
[42] A. Kurian, E. Sunildutt, Artificial Neural Networks in Pavement Engineering: A Recent Review, AIJR Proceedings, (2021) 553-558.
[43] V.S. Dave, K. Dutta, Neural network-based models for software effort estimation: a review, Artificial Intelligence Review, 42(2) (2014) 295-307.
[44] R. Bala, D.D. Kumar, Classification using ANN: A review, International. Journal of Computational Intelligence Research, 13(7) (2017) 1811–1820.
[45] B.O. Antwi, B.O. Adelakun, A.O. Eziefule. Transforming financial reporting with AI: Enhancing accuracy and timeliness, International Journal of Advanced Economics, 6(6) (2024) 205-223.
[46] J. He, Z. Qi, W. Hang, C. Zhao, M. King, Predicting freeway pavement construction cost using a back-propagation neural network: A case study in Henan, China, The Baltic Journal of Road and Bridge Engineering, 9(1) (2014) 66-76.
[47] M. Tiğdemir, Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling, PloS one, 9(11) (2014) e113226.
[48] A. Qadir, U. Gazder, K.U.N Choudhary, Artificial neural network models for performance design of asphalt pavements reinforced with geosynthetics, Transportation Research Record, 2674(8) (2020) 319-326.
[49] N. Tahaei, J.J. Yang, M.G. Chorzepa, S.S. Kim, S.A. Durham. Machine learning of Truck Traffic Classification groups from Weigh-in-Motion data, Machine Learning with Applications, 6 (2021) 100178.
[50] M. Tohidi, N. Khayat, A. Telvari, The use of intelligent search algorithms in the cost optimization of road pavement thickness design, Ain Shams Engineering Journal, 13 (2022) 101596.
[51] F. Xiao, B.J. Putman, S.N. Amirkhanian, Viscosity prediction of CRM binders using artificial neural network approach, International Journal of Pavement Engineering, 12(5) (2011) 485–495.
[52] A.S. Hosseini, P. Hajikarimi, M. Gandomi, F. Moghadas Nejad, A.H. Gandomi, Optimized machine learning approaches for the prediction of viscoelastic behavior of modified asphalt binders, Construction and Building Materials, 299 (2021) 124264.
[53] M. Arifuzzaman, M.A. Gul, K. Khan and S.M.Z. Hossain, Application of Artificial Intelligence (AI) for Sustainable Highway and Road System, Symmetry, 13(60) 2021.
[54] R.H. Riyad, R. Jaiswal, I.B. Muhit, J. Shen, Optimizing modified asphalt binder performance at high and intermediate temperatures using experimental and machine learning approaches, Construction and Building Materials, 449 (2024) 138350.
[55] S. Tapkın, A. Cevik, Ü. Usar. Accumulated strain prediction of polypropylene modified marshall specimens in repeated creep test using artificial neural networks, Expert Systems with Applications, 36 (2009) 11186–11197.
[56] M. Saffarzadeh, A. Heidaripanah. Effect of Asphalt Content on the Marshall Stability of Asphalt Concrete Using Artificial Neural Networks, Scientia Iranica, 16(1) (2009).
[57] A.R. Ghanizadeh and M. Fakhri, Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN, The Scientific World Journal, 1 (2014) 515467.
[58] S. Tapkin, Estimation of Fatigue Lives of Fly Ash Modified Dense Bituminous Mixtures Based on Artificial Neural Networks, Materials Research, 17(2) (2014) 316-325.
[59] S. Khuntia, A.K. Das, M. Mohanty, M. Panda, Prediction of Marshall Parameters of Modified Bituminous Mixtures Using Artificial Intelligence Techniques, Transportation Science and Technology, 3(3) (2014) 211-227.
[60] A.R. Ghanizadeh, Application of Support Vector Machine Regression for Predicting Critical Responses of Flexible Pavements, International Journal of Transportation Engineering, 4(4) (2017) 305-315.
[61] H. Majidifard, B. Jahangiri, W.G. Buttlar, A.H. Alavi, New machine learning-based prediction models for fracture energy of asphalt mixtures, Measurement, 135 (2019) 438–451.
[62] T.-H. Le, H.-L. Nguyen, B.T. Pham, M.H. Nguyen, C.-T. Pham, N.-L. Nguyen, T.-T. Le, H.-B. Ly, Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt, Applied Sciences, 10(15) (2020) 5242.
[63] A.R. Ghanizadeh, F.S. Jahanshahi, V. Khalifeh, F. Jalali, Predicting Flow Number of Asphalt Mixtures Based on the Marshall Mix design Parameters Using Multivariate Adaptive Regression Spline (MARS), International Journal of Transportation Engineering, 7(4) (2020) 433-448.
[64] N.T. Tran, O. Takahashi, Evaluating the rutting resistance of wearing course mixtures with different fine aggregate sources using the indirect tensile strength test, Journal of Testing and Evaluation, 48(4) (2020) 2865-2879.
[65] M.A. Abed, Z.N.M. Taki, A.H. Abed, Artificial neural network modeling of the modified hot mix asphalt stiffness using Bending Beam Rheometer. Materials Today: Proceedings, 42 (2021) 2584–258.
[66] L.T. Glover, J. Mallela, Guidelines for Implementing NCHRP 1-37A ME Design Procedures in Ohio: Volume 4—MEPDG Models Validation & Recalibration, No. FHWA/OH-2009/9D, Ohio Department of Transportation, 2009.
[67] Q. Li, D.X. Xiao, K.C. Wang, K.D. Hall, Y. Qiu, Mechanistic-empirical pavement design guide (mepdg): A bird’s-eye view, Journal of Modern Transportation, 19(2) (2011) 114-133.
[68] J. Huang, G.S. Kumar, Y. Sun, Evaluation of workability and mechanical properties of asphalt binder and mixture modified with waste toner, Construction and Building Materials, 276 (2021) 122230.
[69] J. Huang, G.S. Kumar, J. Ren, J. Zhang, Y. Sun, Accurately predicting dynamic modulus of asphalt mixtures in low temperature regions using hybrid artificial intelligence model, Construction and Building Materials, 297 (2021) 123655.
[70] X. Zhang, F. Otto, M. Oeser, Pavement moduli back-calculation using artificial neural network and genetic algorithms, Construction and Building Materials, 287 (2021) 123026.
[71] A. Behnood, E.M. Golafshani, Predicting the dynamic modulus of asphalt mixture using machine learning techniques: An application of multi biogeography-based programming, Construction and Building Materials, 266 (2021) 120983.
[72] S. Rahman, A. Bhasin, A. Smit. Exploring the use of machine learning to predict metrics related to asphalt mixture performance, Construction and Building Materials, 295 (2021) 123585.
[73] S. Ghafari, M. Ehsani, F. Moghadas Nejad, Prediction of low-temperature fracture resistance curves of unmodified and crumb rubber modified hot mix asphalt mixtures using a machine learning approach, Construction and Building Materials, 314 (2022) 125332.
[74] X. Wang, P. Pan, J. Li, Real-time measurement on dynamic temperature variation of asphalt pavement using machine learning, Measurement, 207 112413.
[75] Z. Tong, D. Yuan, J. Gao, Y. Wei, H. Dou, Pavement-distress detection using ground-penetrating radar and network in networks, Construction and Building Materials, 233 (2020) 117352.
[76] Q. Mei, M. Gül. A cost-effective solution for pavement crack inspection using cameras and deep neural networks, Construction and Building Materials, 256 (2020) 119397.
[77] J. Gao, D. Yuan, Z. Tong, J. Yang, D. Yu, Autonomous pavement distress detection using ground penetrating radar and region-based deep learning, Measurement 164 (2020) 108077.
[78] H.-C., Dan, G.-W. Bai, Z.-H. Zhu, Application of deep learning-based image recognition technology to asphalt–aggregate mixtures: Methodology, Construction and Building Materials, 297 (2021) 123770.
[79] D. Arya, H. Maeda, S.K. Ghosh, D. Toshniwal, Y. Sekimoto, An annotated image dataset for automatic road damage detection using deep learning, Data in Brief, 36 (2021) 107133.
[80] C. Zhang, E. Nateghinia, L.F. Miranda-Moreno, L. Sun, Pavement distress detection using convolutional neural network (CNN): A case study in Montreal, Canada, International Journal of Transportation Science and Technology, 11(2) (2022) 298-309.
[81] J. Guan, X. Yang, L. Ding, X. Cheng, V.C.S. Lee, C. Jin, Automated pixel-level pavement distress detection based on stereo vision and deep learning, Automation in Construction, 129 (2021) 103788.
[82] M. Ehsani, F. Moghadas Nejad, P. Hajikarimi, Developing an optimized faulting prediction model in Jointed Plain Concrete Pavement using artificial neural networks and random forest methods, International journal of pavement engineering, 24(2) (2023) 2057975.
[83] B. Li, K.C.P. Wang, A. Zhang, Y. Fei, Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images. Journal of Advanced Transportation, 1 (2019) 1813763.
[84] M. Fakhri, R.S. Dezfoulian, Pavement structural evaluation based on roughness and surface distress survey using neural network model, Construction and Building Materials, 204 (2019) 768–780.
[85] O. Kaya, H. Ceylan, S. Kim, D. Waid, and B.P. Moore, Statistics and Artificial Intelligence-Based Pavement Performance and Remaining Service Life Prediction Models for Flexible and Composite Pavement Systems, Transportation Research Record, 2674(10) (2020) 448-460.
[86] H. Gong, Y. Sun, X. Shu, B. Huang, Use of random forests regression for predicting IRI of asphalt pavements, Construction and Building Materials, 189 (2018) 890–897.
[87] N. Solatifar, S.M. Lavasani, Development of An Artificial Neural Network Model for Asphalt Pavement Deterioration Using LTPP Data, Journal of Rehabilitation in Civil Engineering, 8(1) (2020) 121-132.
[88] M.Z. Bashar, C. Torres-Machi, Performance of Machine Learning Algorithms in Predicting the Pavement International Roughness Index, Transportation Research Record, 2675(5) (2021) 226-237.
[89] A. Issa, H. Samaneh, M. Ghanim, Predicting pavement condition index using artificial neural networks approach, Ain Shams Engineering Journal, 13(1) (2022) 101490.
[90] M. Ghodratabadi, A. Golroo, M.S. Entezari, Machine learning for predicting pavement roughness and optimising maintenance, Road Materials and Pavement Design, (2025) 1-20.
[91] K. Chen, M.E. Torbaghan, N. Thom, A. Faramarzi, Physics-guided neural network for predicting international roughness index on flexible pavements considering accuracy, uncertainty and stability, Engineering Applications of Artificial Intelligence, 142 (2025) 109922.
[92] A. Yazdi, M.H. Dehnad, A Case Study on Predicting Asphalt Pavement Distress Using Advanced Machine Learning Techniques and Road Surface Inspection Data, Case Studies in Construction Materials, (2025) e05291.
[93] M. Shaheen, R.A. Elsayed, H. Ghazoly, W.A. Bekheet, Explainable and economical AI-based approach for PCI assessment, International Journal of Pavement Engineering, 26(1) (2025) 2531195.
[94] Y. Wu, B. Sicard, S.A. Gadsden, Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring, Expert Systems with Applications, 255 (2024) 124678.
[95] A.A. Elhadidy, E.E. Elbeltagi, M.A. Ammar, Optimum analysis of pavement maintenance using multi-objective genetic algorithms, HBRC Journal, 11(1) (2015) 107–113.
[96] L. Barua, B. Zou, Planning maintenance and rehabilitation activities for airport pavements: A combined supervised machine learning and reinforcement learning approach, International Journal of Transportation Science and Technology, 11(2) (2022) 423-435.
[97] C. Han, T. Ma, S. Chen, Asphalt pavement maintenance plans intelligent decision model based on reinforcement learning algorithm, Construction and Building Materials, 299 (2021) 124278.
[98] M.S. Entezari, R. Tanzadeh, F. Moghadas Nejad, Economic, Environmental, and Social Assessment of Concrete Pavement Life Cycle: A Literature Review, Sharif Journal of Civil Engineering, 41(1) (2025) 110-129.
[99] M.M. Dadaei, P. Hajikarimi, F. Moghadas Nejad, Sustainable prospective proposals for utilizing modifiers in bitumen industry to address global warming, Scientific Reports, 15(1) (2025) 17042.
[100] H. Hosseinzadeh, A. Hasani, S. Arman, A.S. Hejazi, Predicting Marshall Asphalt Stability Using Supervised Machine Learning Algorithms, support vector machine and random forest, Journal of Transportation Research, 20(3) (2023) 249-262 (in Persian).
[101] A. Amini, Presenting Predictive Models of Rheological Characteristics of Modified Bitumens Containing SBS and Carbon Nanotubes Using Nonlinear Regression and Neural Network, Journal of Transportation Research, 22(3) (2025) 373-384 (in Persian).
[102] H. Behbahani, N. Nadimi, M. Khaleghi, Introducing a New Method for the Pavements’ Maintenance and Rehabilitation Planning, Amirkabir Journal of Civil Engineering, 53(7) (2021) 2801-2820 (in Persian).
[103] P. Ghaderi, A. Abdolmaaleki, A novel unsupervised deep neural network-based method for damage detection in civil structures, Modarres Civil Engineering Journal, 22(1) (2022) 143-159 (in Persian).