[ 1 ] Rollings RS, Pittman DW. Field instrumentation and performance monitoring of rigid pavements. J Transp Eng 1992;118(3):361–70. link1

[ 2 ] Sebaaly P, Tabatabaee N, Scullion T. Instrumentation for flexible pavementsfield performance of selected sensors. Final report. Washington, DC: Federal Highway Administration; 1992 Jun. Report No.: FHWA-RD-91-094. Contract No.: DTFH61-88-R-00052.

[ 3 ] Xue W, Weaver E. Pavement shear strain response to dual and wide-base tires. Transp Res Rec J Transport Res Board 2011;2225(1):155–64. link1

[ 4 ] Al-Qadi IL, Loulizi A, Elseifi M, Lahouar S. The virginia smart road: the impact of pavement instrumentation on understanding pavement performance. Asphalt Paving Technol 2004;73:427–65. link1

[ 5 ] Gonçalves P, Ceratti JAP, Bica AD. The use of embedded stress cells for monitoring pavement performance. Geotech Test J 2003;26(4):363–72. link1

[ 6 ] Timm DH, Priest AL. Dynamic pavement response data collection and processing at the NCAT test track. Final report. Auburn: National Center for Asphalt Technology; 2004. Report No.: NCAT Report 04–03. link1

[ 7 ] Scholz T. Instrumentation for mechanistic design implementation. Final report. Portland: Transportation Research and Education Center; 2010. Report No.: OTREC-RR-10-02.

[ 8 ] Hornyak NJ, Crovetti JA, Newman DE, Schabelski JP. Perpetual pavement instrumentation for the marquette interchange project-phase 1. Final report. Milwaukee: Transportation Research Center, Marquette University, Wisconsin Highway Research Program; 2007 Aug. Report No.:WHRP 07–11.

[ 9 ] Xue W, Wang L, Wang D, Druta C. Pavement health monitoring system based on an embedded sensing network. J Mater Civ Eng 2014;26(10):04014072. link1

[10] Xue W, Wang D, Wang L. Monitoring the speed, configurations, and weight of vehicles using an in-situ wireless sensing network. IEEE Trans Intell Transport Syst 2015;16(4):1667–75. link1

[11] Cho I, Lee JH, Park J, Yi DH, Cho D, Kim SW. A new method for accurately estimating the weight of moving vehicles using piezoelectric sensors and adaptive-footprint tire model. Veh Syst Dyn 2003;39(2):135–48. link1

[12] Zhang Z, Huang Y, Bridgelall R, Palek L, Strommen R. Sampling optimization for high-speed weigh-in-motion measurements using in-pavement strainbased sensors. Meas Sci Technol 2015;26(6):065003. link1

[13] Zhang W, Wang Q, Suo C. A novel vehicle classification using embedded strain gauge sensors. Sensors 2008;8(11):6952–71.

[14] Xue W, Wang L, Wang D. A prototype integrated monitoring system for pavement and traffic based on an embedded sensing network. IEEE Trans Intell Transp Syst 2015;16(3):1380–90. link1

[15] Yang H. Development of a piezoelectric energy harvesting system for applications in collecting pavement deformation energy [dissertation]. Beijing Chinese: University of Science and Technology Beijing; 2018. Chinese. link1

[16] Mazurek B, Janiczek T, Chmielowiec J. Assessment of vehicle weight measurement method using PVDF transducers. J Electrostat 2001;51– 52:76–81. link1

[17] Zhang J, Lu Y, Lu Z, Liu C, Sun G, Li Z. A new smart traffic monitoring method using embedded cement-based piezoelectric sensors. Smart Mater Struct 2015;24(2):025023. link1

[18] Wang L, Hu X, Huang Y, Xu H. Based on fiber-optic sensor and the light intensity changes vehicle dynamic weighing system. In: Proceedings of the 5th International Conference on Computational Intelligence and Communication Networks; 2013 Sep 27–29; Washington, DC, USA; 2013.

[19] Malla RB, Sen A, Garrick NW. A special fiber optic sensor for measuring wheel loads of vehicles on highways. Sensors 2008;8(4):2551–68.

[20] Yuan S, Ansari F, Liu X, Zhao Y. Optic fiber-based dynamic pressure sensor for WIM system. Sens Actuators A 2005;120(1):53–8. link1

[21] Batenko A, Grakovski A, Kabashkin I, Petersons E, Sikerzhicki Y. Weight-inmotion (WIM) measurements by fiber optic sensor: problems and solutions. Transp Telecommun 2011;12(4):27–33. link1

[22] Zhang H, Wei Z, Fan L, Yang S, Wang P, Cui H. A high speed, portable, multifunction, weigh-in-motion (WIM) sensing system and a high performance optical fiber Bragg grating (FBG) demodulator. In: Proceedings of the SPIE 7647, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems; 2010 Mar 9; San Diego, CA, USA; 2010.

[23] Zhao H, Wu D, Zeng M, Zhong S. A vibration-based vehicle classification system using distributed optical sensing technology. Transp Res Rec J Transp Res Board 2018;2672(43):12–23. link1

[24] Dong Z, Ma X, Shao X. Airport pavement responses obtained from wireless sensing network upon digital signal processing. Int J Pavement Eng 2018;19 (5):381–90. link1

[25] Gubbi J, Buyya R, Marusic S, Palaniswami M. Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 2013;29(7):1645–60. link1

[26] Ho CM, Tai YC. Micro-electro-mechanical-systems (MEMS) and fluid flows. Annu Rev Fluid Mech 1998;30(1):579–612. link1

[27] Alavi AH, Hasni H, Lajnef N, Chatti K. Continuous health monitoring of pavement systems using smart sensing technology. Constr Build Mater 2016;114:719–36. link1

[28] Ong JB, You Z, Mills-Beale J, Tan EL, Pereles BD, Ong KG. A wireless, passive embedded sensor for real-time monitoring of water content in civil engineering materials. IEEE Sens J 2008;8(12):2053–8. link1

[29] Lian K. Developing embedded wireless strain/stress/temperature sensor platform for highway applications. Final report. Baton Rouge: Louisiana State University, Transportation Research Board of the National Academies; 2010 Mar. Report No.: NCHRP IDEA Project 129.

[30] Krüger M, Grosse CU, Marrón PJ. Wireless structural health monitoring using MEMS. Key Eng Mater 2005;293–294:625–34. link1

[31] Bennett R, Hayes-Gill B, Crowe JA, Armitage R, Rodgers D, Hendroff A. Wireless monitoring of highways. In: Proceedings of the SPIE 3671, Smart Structures and Materials 1999: Smart Systems for Bridges, Structures, and Highways; 1999 May 18; Newport Beach, CA, USA; 1999.

[32] Haoui A, Kavaler R, Varaiya P. Wireless magnetic sensors for traffic surveillance. Transp Res Part C Emerging Technol 2008;16(3):294–306. link1

[33] Pei JS, Ivey RA, Lin H, Landrum AR, Sandburg CJ, Ferzli NA, et al. An experimental investigation of applying Mica2 Motes in pavement condition monitoring. J Intell Mater Syst Struct 2009;20(1):63–85. link1

[34] Chua KM, Xu L. Simple procedure for identifying pavement damages from video images. J Transp Eng 1994;120(3):412–31. link1

[35] Zakeri H, Nejad FM, Fahimifar A. Image based techniques for crack detection, classification and quantification in asphalt pavement: a review. Arch Comput Methods Eng 2017;24(4):935–77. link1

[36] Hoang ND, Nguyen QL. Fast local Laplacian-based steerable and Sobel filters integrated with adaptive boosting classification tree for automatic recognition of asphalt pavement cracks. Adv Civ Eng 2018;2018:5989246. link1

[37] Sun B, Qiu Y, Liang S. Cracking recognition of pavement surface based on wavelet technology. J Chongqing Jiaotong University 2010;29(1):69–72. Chinese.

[38] Balbin JR, Hortinela CC, Garcia RG, Baylon S, Ignacio AJ, Rivera MA, et al. Pattern recognition of concrete surface cracks and defects using integrated image processing algorithms. In: Proceedings of the 2nd International Workshop on Pattern Recognition; 2017 May 1–3; Singapore, Singapore; 2017.

[39] Wang B. A study on cement pavement crack detection based on Matlab image processing. J Shangluo Univ 2014;28(4):42–5. Chinese.

[40] Li M. Design and implementation of pavement inspection system based on image analysis [dissertation]. Shengyang: Liaoning University; 2018. Chinese. link1

[41] Gonzalez RC, Woods RE. Digital image processing. 3rd ed. Upper Saddle River: Prentice Hall; 2007. link1

[42] Ma RG, Xu K, Liu FF. Highway surface crack image identifying algorithm. J Transp Inf Saf 2014;32(2):90–3. Chinese.

[43] Wang PP. Research on pavement crack recognition based on improved gray scale segmentation algorithm [dissertation]. Xi’an: Chang’an University; 2014. Chinese.

[44] Liu N, Song W, Zhao Q. Morphology and maximum entropy image segmentation based urban pavement cracks detection. J Liaoning Tech Univ 2015;34(1):57–61. Chinese.

[45] Han K, Han HF. Pavement crack detection method based on region-level and pixel-level features. J Railw Sci Eng 2018;15(5):1178–86. Chinese.

[46] Luo YZ. Asphalt pavement crack detection based on image local mean standard deviation algorithm. Geomatics Spat Inf Technol 2017;40(12):167– 70. Chinese.

[47] Li YY, Huang QY, Hou ZX. Improve of OSTU based on hough transformation and applied in pavement crack detection. Electron Des Eng 2016;24(5):43–6. Chinese.

[48] Talab AMA, Huang Z, Xi F, Haiming L. Detection crack in image using Otsu method and multiple filtering in image processing techniques. Optik 2016;127(3):1030–3. link1

[49] Gao SB, Xie Z, Pan ZG, Qiu FZ, Li R. Novel automatic pavement crack detection algorithm. J Syst Simul 2017;29(9):2009–15. Chinese.

[50] Qiu LY. An algorithm of pavement crack detection based on edge detection. J Yancheng Inst Technol 2015;28(3):37–43. Chinese.

[51] Zhu Q. Pavement crack detection algorithm based on image processing analysis. In: Proceedings of the 8th International Conference on Intelligent Human-Machine Systems and Cybernetics; 2016 Sep 11–12; Hangzhou, China; 2016.

[52] Di Y. Research on recognition algorithm of pavement crack [dissertation]. Zhenzhou: Zhengzhou University; 2018. Chinese.

[53] Liu S, Wang W, Cao T, Yang N, Yang Y. Road crack extraction based on differential box dimension and maximum entropy threshold. J Chang’an Univ 2015;35(5):13–21. Chinese.

[54] Zhang H. Research on pavement crack detection system based on image processing [dissertation]. Shenyang: Shenyang Aerospace University; 2018. Chinese.

[55] Wen L. Improved gray correction algorithm for image preprocessing pavement cracks. Software Tech Algorithm 2015;24(2):220–3. Chinese.

[56] Gang W, Xu X, Liang X, He A. Algorithm based on the finite ridgelet transform for enhancing faint pavement cracks. Opt Eng 2008;47(1):017004. link1

[57] Li S, Hou DH, Gao J, Tong Z. Research on preprocessing method of pavement crack image via mathematical morphology. Highw Eng 2018;43(2):270–4. Chinese.

[58] Jia L. The extraction method of pavement crack edge based on automatic threshold detection and edge connection. Shanxi Sci Technol Commun 2015;5:21–4. Chinese.

[59] Qi HC, Xiao F, Bao XW. Improvement of edge detection discontinuity in the pavement crack recognition. Electron Des Eng 2014;22(1):32–4. Chinese.

[60] Zhang H, Meng H, Liu T. Analysis on the image edge detection algorithm of asphalt pavement crack. North Commun 2015;3:56–9. Chinese.

[61] Xu AH, Gao J. Crack identification method for cement pavement based on image enhancement and mathematical morphology. Highway 2015;25 (10):55–8. Chinese.

[62] Liu S. Method of shadow pavement crack extraction based on improved local threshold segmentation. Wireless Internet Technol 2018;15(20):112–3. Chinese.

[63] Xu H, Li Z, Jiang Y, Huang J. Pavement crack detection based on OpenCv and improved Canny operator. Comput Eng Des 2014;35(12):4254–8. Chinese.

[64] Wang X, Feng D, Li W. Research and implementation of pavement crack detection algorithm. J North China Inst Aerosp Eng 2017;27(5):9–13. Chinese.

[65] Koza JR, Bennett FH, Andre D, Keane MA. Automated design of both the topology and sizing of analog electrical circuits using genetic programming. In: Artificial Intelligence in Design. Boston: Kluwer Academic Publishers; 1996. p. 151–70. link1

[66] Cortes C, Vapnik VN. Support vector networks. Mach Learn 1995;20 (3):273–97. link1

[67] Bishop CM. Pattern recognition and machine learning (information science and statistics). New York: Springer; 2006. link1

[68] Hoang ND, Nguyen QL. Automatic recognition of asphalt pavement cracks based on image processing and machine learning approaches: a comparative study on classifier performance. Math Probl Eng 2018;2018:6290498. link1

[69] Hoang ND, Nguyen QL, Tien BD. Image processing-based classification of asphalt pavement cracks using support vector machine optimized by artificial bee colony. J Comput Civ Eng 2018;32(5):04018037. link1

[70] Schlotjes MR, Burrow MPN, Evdorides HT, Henning TFP. Using support vector machines to predict the probability of pavement failure. Proc Inst Civ Eng Transp 2015;168(3):212–22. link1

[71] Pan YF, Zhang XF, Cervone G, Yang LP. Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 2018;11(10):3701–12. link1

[72] Fujita Y, Shimada K, Ichihara M, Hamamoto Y. A method based on machine learning using hand-crafted features for crack detection from asphalt pavement surface images. In: Proceedings of the International Conference on Quality Control by Artificial Vision; 2017 May 14; Tokyo, Japan; 2017.

[73] Sapatinas T. The elements of statistical learning. J R Stat Soc Ser A 2004;167 (1):183–98. link1

[74] Wang H, Xie P, Ji R, Gagnon J. Prediction of airfield pavement responses from surface deflections: comparison between the traditional backcalculation approach and the ANN model. Road Mater Pavement Des. Forthcoming 2020.

[75] Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning; 2010 Jun 21–25; Haifa, Israel; 2010.

[76] Maas AL, Hannun AY, Ng AY. Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th International Conference on Machine Learning; 2013 Jun 16–21; Atlanta, GA, USA; 2013.

[77] Schmidhuber J. Deep learning in neural networks: an overview. Neural Networks 2015;61:85–117. link1

[78] Banharnsakun A. Hybrid ABC-ANN for pavement surface distress detection and classification. Int J Mach Learn Cybern 2017;8(2):699–710. link1

[79] Elbagalati O, Elseifi MA, Gaspard K, Zhang Z. Development of an enhanced decision-making tool for pavement management using a neural network pattern-recognition algorithm. J Transp Eng Part B Pavements 2018;144 (2):04018018. link1

[80] Ng A. Deep learning [Internet]. deeplearning.ai; c2020. [cited 2019 Mar 1]. Available from: www.deeplearning.ai/ deep-learning-specialization/. link1

[81] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. arXiv:1409.1556.

[82] Gopalakrishnan K, Khaitan SK, Choudhary A, Agrawal A. Deep convolutional neural networks with transfer learning for computer vision-based datadriven pavement distress detection. Constr Build Mater 2017;157:322–30. link1

[83] Zhang A, Wang KCP, Li BX, Yang EH, Dai X, Peng Y, et al. Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput-Aided Civ Infrastruct Eng 2017;32(10):805–19. link1

[84] Cha YJ, Choi W, Suh G, Mahmoudkhani S, Buyukozturk O. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput-Aided Civ Infrastruct Eng 2018;33(9):731–47. link1

[85] Wu W, Fan Q, Zurada JM, Wang J, Yang D, Liu Y. Batch gradient method with smoothing L1/2 regularization for training of feedforward neural networks. Neural Networks 2014;50:72–8. link1

[86] Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R. Improving neural networks by preventing co-adaptation of feature detectors. 2012. arXiv:1207.0580v1.

[87] Fei Y, Wang KCP, Zhang A, Chen C, Li B, Liu Y, et al. Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based CrackNet-V. IEEE Trans Intell Transp Syst 2020;21(1):273–84. link1

[88] Cha YJ, Choi W, Buyukozturk O. Deep learning-based crack damage detection using convolutional neural networks. Comput-Aided Civ Infrastruct Eng 2017;32(5):361–78. link1

[89] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning; 2015 Jul 6–11; Lille, France; 2015.

[90] Wu Y, He K. Group normalization. Int J Comput Vision 2020;128:742–55. link1

[91] Cotter A, Shamir O, Srebro N, Sridharan K. Better mini-batch algorithms via accelerated gradient methods. In: Shawe-Taylor J, Zemel R, Bartlett P, Pereira F, Weinberger KQ, editors. Advances in Neural Information Processing Systems 24. Red Hook: Curran Associates Inc.; 2011. p. 1647–55.

[92] Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International Conference on Machine Learning; 2013 Jun 16–21; Atlanta, GA, USA; 2013.

[93] Kingma D, Ba J. Adam: a method for stochastic optimization. 2014. arXiv:1412.6980.

[94] Dorafshan S, Thomas RJ, Maguire M. Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr Build Mater 2018;186:1031–45. link1

[95] Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Commun ACM 2012;60(6):1–9. link1

[96] Sobel I. Neighborhood coding of binary images for fast contour following and general binary array processing. Comput Graphics Image Process 1978;8 (1):127–35. link1

[97] Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986;8(6):679–98. link1

[98] Wang X, Hu Z. Grid-based pavement crack analysis using deep learning. In: Proceedings of the 4th International Conference on Transportation Information and Safety; 2017 Aug 8–10; Banff, Canada; 2017.

[99] Zhang A, Wang KCP, Fei Y, Liu Y, Tao SY, Chen C, et al. Deep learning-based fully automated pavement crack detection on 3D asphalt surfaces with an improved CrackNet. J Comput Civ Eng 2018;32(5):04018041. link1

[100] Sha AM, Tong Z, Gao J. Recognition and measurement of pavement disasters based on convolutional neural networks. China J Highway Transp 2018;31 (1):1–10. Chinese.

[101] Tong Z, Gao J, Sha A, Hu L, Li S. Convolutional neural network for asphalt pavement surface texture analysis. Comput-Aided Civ Infrastruct Eng 2018;33(12):1056–72. link1

[102] Yang G, Li Q, Zhan Y, Fei Y, Zhang A. Convolutional neural network–based friction model using pavement texture data. J Comput Civ Eng 2018;32 (6):04018052. link1

[103] Tong Z, Gao J, Zhang HT. Innovative method for recognizing subgrade defects based on a convolutional neural network. Constr Build Mater 2018;169:69–82. link1

[104] Cao X, Wang P, Meng C, Bai X, Gong G, Liu M, et al. Region based CNN for foreign object debris detection on airfield pavement. Sensors 2018;18 (3):737. link1

[105] Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 2017;39(6):1137–49. link1

[106] Majidifard H, Jahangiri B, Buttlar W, Alavi A. New machine learning-based prediction models for fracture energy of asphalt mixtures. Measurement 2019;135:438–51. link1

[107] Gong H, Sun Y, Mei Z, Huang B. Improving accuracy of rutting prediction for mechanistic-empirical pavement design guide with deep neural networks. Constr Build Mater 2018;190:710–8. link1

[108] Majidifard H, Adu-Gyamfi Y, Buttlar WG. Deep machine learning approach to develop a new asphalt pavement condition index. Constr Build Mater 2020;247:118513. link1