Share:


Scientometric analysis of pavement maintenance: a twenty-year review

    Ankang Ji Affiliation
    ; Xiaolong Xue Affiliation
    ; Xiaowei Luo Affiliation
    ; Yuna Wang Affiliation
    ; Hengqin Wu Affiliation

Abstract

Pavement maintenance is widely thought to be critical for promoting sustainability, playing a pivotal role in sustainable and resilient transportation infrastructure for growth in economic development and improvements in social inclusion. It has attracted increasing attention from both academia and industry over the past 20 years. Although several literature reviews have been conducted, there is still a lack of systematic quantitative and visual investigation of the structure and evolution of knowledge in this field. To address this lack, reported here is a comprehensive and objective scientometric analysis to visualize the status quo of research areas regarding pavement maintenance. Focusing on 614 journal articles collected from the Web of Science for 2001–2020, key researchers within the field are identified, as are the key research institutions, key countries, and their interconnections, as well as keywords, evolution trends, key publications, and citation patterns, along with the extent to which these interact with each other in research networks. Based on the in-depth analysis, a knowledge roadmap is provided to inscribe how pavement maintenance-related research evolves over time, greatly contributing to the understanding of the underlying structure of pavement maintenance, and to highlight the identified current research challenges and future research trends, thus potentially benefiting the academic community and practice field on multiple themes of pavement maintenance. The results of this research are instructive, providing a broad overview and holistic thinking for researchers and practitioners with respect to pavement maintenance research, as well as facilitating further research and applications for both academia and industry in improving pavement maintenance for sustainability.

Keyword : pavement maintenance, literature review, scientometric analysis, CiteSpace, social network analysis

How to Cite
Ji, A., Xue, X., Luo, X., Wang, Y., & Wu, H. (2023). Scientometric analysis of pavement maintenance: a twenty-year review. Journal of Civil Engineering and Management, 29(5), 439–462. https://doi.org/10.3846/jcem.2023.19031
Published in Issue
Jul 10, 2023
Abstract Views
442
PDF Downloads
289
Creative Commons License

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

References

Abaza, K. A. (2005). Performance-based models for flexible pavement structural overlay design. Journal of Transportation Engineering, 131(2), 149–159. https://doi.org/10.1061/(ASCE)0733-947X(2005)131:2(149)

Abaza, K. A. (2016). Back-calculation of transition probabilities for markovian-based pavement performance prediction models. International Journal of Pavement Engineering, 17(3), 253–264. https://doi.org/10.1080/10298436.2014.993185

Abaza, K. A., Ashur, S. A., & Al-Khatib, I. A. (2004). Integrated pavement management system with a Markovian prediction model. Journal of Transportation Engineering, 130(1), 24–33. https://doi.org/10.1061/(ASCE)0733-947X(2004)130:1(24)

Abotaleb, I. S., & El-adaway, I. H. (2018). Managing construction projects through dynamic modeling: Reviewing the existing body of knowledge and deriving future research directions. Journal of Management in Engineering, 34(6), 04018033. https://doi.org/10.1061/(asce)me.1943-5479.0000633

Ahmed, K., Al-Khateeb, B., & Mahmood, M. (2019). Application of chaos discrete particle swarm optimization algorithm on pavement maintenance scheduling problem. Cluster Computing, 22(Suppl 2), 4647–4657. https://doi.org/10.1007/s10586-018-2239-3

Amin, S., Tamima, U., & Amador-Jiménez, L. E. (2019). Optimal pavement management: Resilient roads in support of emergency response of cyclone affected coastal areas. Transportation Research Part A: Policy and Practice, 119, 45–61. https://doi.org/10.1016/j.tra.2018.11.001

Bang, S., Park, S., Kim, H., & Kim, H. (2019). Encoder–decoder network for pixel-level road crack detection in black-box images. Computer-Aided Civil and Infrastructure Engineering, 34(8), 713–727. https://doi.org/10.1111/mice.12440

Bayer, A. E., Smart, J. C., & McLaughlin, G. W. (1990). Mapping intellectual structure of a scientific subfield through author cocitations. Journal of the American Society for Information Science, 41(6), 444–452. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<444::AID-ASI12>3.0.CO;2-J

Bosurgi, G., & Trifirò, F. (2005). A model based on artificial neural networks and genetic algorithms for pavement maintenance management. International Journal of Pavement Engineering, 6(3), 201–209. https://doi.org/10.1080/10298430500195432

Bressi, S., Dumont, A. G., & Partl, M. N. (2016). A new laboratory methodology for optimization of mixture design of asphalt concrete containing reclaimed asphalt pavement material. Materials and Structures, 49(12), 4975–4990. https://doi.org/10.1617/s11527-016-0837-1

Busang, S., & Maina, J. (2022). Influence of aggregates properties on microstructural properties and mechanical performance of asphalt mixtures. Construction and Building Materials, 318, 126002. https://doi.org/10.1016/j.conbuildmat.2021.126002

Butler, L., & Visser, M. S. (2006). Extending citation analysis to non-source items. Scientometrics, 66(2), 327–343. https://doi.org/10.1007/s11192-006-0024-1

Cha, Y. J., Choi, W., & Büyüköztürk, O. (2017). Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378. https://doi.org/10.1111/mice.12263

Chang, J. R. (2013). Particle swarm optimization method for optimal prioritization of pavement sections for maintenance and rehabilitation activities. Applied Mechanics and Materials, 343, 43–49. https://doi.org/10.4028/www.scientific.net/amm.343.43

Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America, 101, 5303–5310. https://doi.org/10.1073/pnas.0307513100

Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317

Chen, C. (2017). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2(2), 1–40. https://doi.org/10.1515/jdis-2017-0006

Chen, C., Ibekwe‐SanJuan, F., & Hou, J. (2010). The structure and dynamics of cocitation clusters: A multiple‐perspective cocitation analysis. Journal of the American Society for Information Science and Technology, 61(7), 1386–1409. https://doi.org/10.1002/asi.21309

Chen, C., & Morris, S. (2003). Visualizing evolving networks: Minimum spanning trees versus Pathfinder networks. In Proceedings of IEEE Symposium on Information Visualization (INFO VIS) (pp. 67–74). https://doi.org/10.1109/INFVIS.2003.1249010

Chen, W., & Zheng, M. (2021). Multi-objective optimization for pavement maintenance and rehabilitation decision-making: A critical review and future directions. Automation in Construction, 130, 103840. https://doi.org/10.1016/j.autcon.2021.103840

Chootinan, P., Chen, A., Horrocks, M. R., & Bolling, D. (2006). A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach. Transportation Research Part A: Policy and Practice, 40(9), 725–743. https://doi.org/10.1016/j.tra.2005.12.003

Chou, J. S., & Le, T. S. (2011). Reliability-based performance simulation for optimized pavement maintenance. Reliability Engineering and System Safety, 96(10), 1402–1410. https://doi.org/10.1016/j.ress.2011.05.005

Chupin, O., Piau, J. M., & Chabot, A. (2013). Evaluation of the structure-induced rolling resistance (SRR) for pavements including viscoelastic material layers. Materials and Structures, 46(4), 683–696. https://doi.org/10.1617/s11527-012-9925-z

Cobo, M. J., López‐Herrera, A. G., Herrera‐Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402. https://doi.org/10.1002/asi.21525

Coenen, T. B. J., & Golroo, A. (2017). A review on automated pavement distress detection methods. Cogent Engineering, 4(1), 1374822. https://doi.org/10.1080/23311916.2017.1374822

de la Garza, J. M., Akyildiz, S., Bish, D. R., & Krueger, D. A. (2011). Network-level optimization of pavement maintenance renewal strategies. Advanced Engineering Informatics, 25(4), 699–712. https://doi.org/10.1016/j.aei.2011.08.002

de Souza, N. M., & de Almeida Filho, A. T. (2020). A systematic airport runway maintenance and inspection policy based on a delay time modeling approach. Automation in Construction, 110, 103039. https://doi.org/10.1016/j.autcon.2019.103039

Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of Informetrics, 5(1), 187–203. https://doi.org/10.1016/j.joi.2010.10.008

Dong, Q., Huang, B., Richards, S. H., & Yan, X. (2013). Cost-effectiveness analyses of maintenance treatments for low- and moderate-traffic asphalt pavements in Tennessee. Journal of Transportation Engineering, 139(8), 797–803. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000556

El-adaway, I. H., Ali, G., Assaad, R., Elsayegh, A., & Abotaleb, I. S. (2019). Analytic overview of citation metrics in the civil engineering domain with focus on construction engineering and management specialty area and its subdisciplines. Journal of Construction Engineering and Management, 145(10), 04019060. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001705

Elhadidy, A. A., Elbeltagi, E. E., & Ammar, M. A. (2015). Optimum analysis of pavement maintenance using multi-objective genetic algorithms. HBRC Journal, 11(1), 107–113. https://doi.org/10.1016/j.hbrcj.2014.02.008

Fay, L., & Shi, X. (2012). Environmental impacts of chemicals for snow and ice control: State of the knowledge. Water, Air, and Soil Pollution, 223(5), 2751–2770. https://doi.org/10.1007/s11270-011-1064-6

France-Mensah, J., & O’Brien, W. J. (2019). Developing a sustainable pavement management plan: tradeoffs in road condition, user costs, and greenhouse gas emissions. Journal of Management in Engineering, 35(3), 04019005. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000686

Fu, Z., Shen, W., Huang, Y., Hang, G., & Li, X. (2017). Laboratory evaluation of pavement performance using modified asphalt mixture with a new composite reinforcing material. International Journal of Pavement Research and Technology, 10(6), 507–516. https://doi.org/10.1016/j.ijprt.2017.04.001

Gao, H., & Zhang, X. (2013). A markov-based road maintenance optimization model considering user costs. Computer-Aided Civil and Infrastructure Engineering, 28(6), 451–464. https://doi.org/10.1111/mice.12009

Giustozzi, F., Crispino, M., & Flintsch, G. (2012). Multi-attribute life cycle assessment of preventive maintenance treatments on road pavements for achieving environmental sustainability. International Journal of Life Cycle Assessment, 17(4), 409–419. https://doi.org/10.1007/s11367-011-0375-6

Gopalakrishnan, K., Khaitan, S. K., Choudhary, A., & Agrawal, A. (2017). Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials, 157, 322–330. https://doi.org/10.1016/j.conbuildmat.2017.09.110

Gosse, C. A., Smith, B. L., & Clarens, A. F. (2013). Environmentally preferable pavement management systems. Journal of Infrastructure Systems, 19(3), 315–325. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000118

Hess, D. J. (1997). Science studies: An advanced introduction. NYU Press.

Hoang, N.-D., Nguyen, Q. L., & Tran, V. D. (2018). Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network. Automation in Construction, 94, 203–213. https://doi.org/10.1016/j.autcon.2018.07.008

Hoang, N. D. (2019). Image processing based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach. Advanced Engineering Informatics, 40, 110–120. https://doi.org/10.1016/j.aei.2019.04.004

Hosseini, M. R., Martek, I., Zavadskas, E. K., Aibinu, A. A., Arashpour, M., & Chileshe, N. (2018). Critical evaluation of off-site construction research: A Scientometric analysis. Automation in Construction, 87, 235–247. https://doi.org/10.1016/j.autcon.2017.12.002

Huang, J., Liu, W., & Sun, X. (2014). A pavement crack detection method combining 2D with 3D information based on dempster-shafer theory. Computer-Aided Civil and Infrastructure Engineering, 29(4), 299–313. https://doi.org/10.1111/mice.12041

Huang, Y., Bird, R., & Bell, M. (2009). A comparative study of the emissions by road maintenance works and the disrupted traffic using life cycle assessment and micro-simulation. Transportation Research Part D: Transport and Environment, 14(3), 197–204. https://doi.org/10.1016/j.trd.2008.12.003

Inzerillo, L., Di Mino, G., & Roberts, R. (2018). Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress. Automation in Construction, 96, 457–469. https://doi.org/10.1016/j.autcon.2018.10.010

Ji, A., Xue, X., Wang, Y., Luo, X., & Xue, W. (2020). An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement. Automation in Construction, 114, 103176. https://doi.org/10.1016/j.autcon.2020.103176

Jo, Y., & Ryu, S. (2015). Pothole detection system using a black-box camera. Sensors, 15(11), 29316–29331. https://doi.org/10.3390/s151129316

Johannesson, P., Podgórski, K., & Rychlik, I. (2016). Modelling roughness of road profiles on parallel tracks using roughness indicators. International Journal of Vehicle Design, 70(2), 183–210. https://doi.org/10.1504/IJVD.2016.074421

Jorge, D., & Ferreira, A. (2012). Road network pavement maintenance optimisation using the HDM-4 pavement performance prediction models. International Journal of Pavement Engineering, 13(1), 39–51. https://doi.org/10.1080/10298436.2011.563851

Kargah-Ostadi, N., & Stoffels, S. M. (2015). Framework for development and comprehensive comparison of empirical pavement performance models. Journal of Transportation Engineering, 141(8), 04015012. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000779

Khahro, S. H., Memon, Z. A., Nur, N. I., Gungat, L., & Yazid, M. R. M. (2020). Pavement management system research output: A scientometric assessment. Library Philosophy and Practice - Electronic Journal, 2020, 4145.

Khan, M. U., Mesbah, M., Ferreira, L., & Williams, D. J. (2014). Developing a new road deterioration model incorporating flooding. Proceedings of the Institution of Civil Engineers – Transport, 167(5), 322–333. https://doi.org/10.1680/tran.13.00095

Kim, B., & Cho, S. (2019). Image-based concrete crack assessment using mask and region-based convolutional neural network. Structural Control and Health Monitoring, 26(8), e2381. https://doi.org/10.1002/stc.2381

Kobayashi, K., Do, M., & Han, D. (2010). Estimation of markovian transition probabilities for pavement deterioration forecasting. KSCE Journal of Civil Engineering, 14(3), 343–351. https://doi.org/10.1007/s12205-010-0343-x

Koch, C., Jog, G. M., & Brilakis, I. (2013). Automated pothole distress assessment using asphalt pavement video data. Journal of Computing in Civil Engineering, 27(4), 370–378. https://doi.org/10.1061/(asce)cp.1943-5487.0000232

Lee, J., & Madanat, S. (2017). Optimal policies for greenhouse gas emission minimization under multiple agency budget constraints in pavement management. Transportation Research Part D: Transport and Environment, 55, 39–50. https://doi.org/10.1016/j.trd.2017.06.009

Lee, J., Madanat, S., & Reger, D. (2016). Pavement systems reconstruction and resurfacing policies for minimization of life‐cycle costs under greenhouse gas emissions constraints. Transportation Research Part B: Methodological, 93, 618–630. https://doi.org/10.1016/j.trb.2016.08.016

Lethanh, N., & Adey, B. T. (2013). Use of exponential hidden markov models for modelling pavement deterioration. International Journal of Pavement Engineering, 14(7), 645–654. https://doi.org/10.1080/10298436.2012.715647

Li, H., Harvey, J., & Huang, X. (2015). Moving towards a sustainable transportation system: Focus issue on sustainable transportation technology and policy. International Journal of Transportation Science and Technology, 4(1), III–VIII. https://doi.org/10.1260/2046-0430.4.1.iii

Li, Z., Cheng, C., Kwan, M. P., Tong, X., & Tian, S. (2019). Identifying asphalt pavement distress using UAV LiDAR point cloud data and random forest classification. ISPRS International Journal of Geo-Information, 8(1), 39. https://doi.org/10.3390/ijgi8010039

Lidicker, J., Sathaye, N., Madanat, S., & Horvath, A. (2013). Pavement resurfacing policy for minimization of life-cycle costs and greenhouse gas emissions. Journal of Infrastructure Systems, 19(2), 129–137. https://doi.org/10.1061/(asce)is.1943-555x.0000114

Lu, G., Liu, P., Wang, Y., Faßbender, S., Wang, D., & Oeser, M. (2019). Development of a sustainable pervious pavement material using recycled ceramic aggregate and bio-based polyurethane binder. Journal of Cleaner Production, 220, 1052–1060. https://doi.org/10.1016/j.jclepro.2019.02.184

Lu, H., & Feng, Y. (2009). A measure of authors’ centrality in co-authorship networks based on the distribution of collaborative relationships. Scientometrics, 81(2), 499–511. https://doi.org/10.1007/s11192-008-2173-x

Luo, T., Tan, Y., Langston, C., & Xue, X. (2019). Mapping the knowledge roadmap of low carbon building: A scientometric analysis. Energy and Buildings, 194, 163–176. https://doi.org/10.1016/j.enbuild.2019.03.050

Luwel, M. (2004). The use of input data in the performance analysis of R&D systems. In Handbook of quantitative science and technology research (pp. 315–338). Springer. https://doi.org/10.1007/1-4020-2755-9_15

Ma, J., Cheng, L., & Li, D. (2018). Road maintenance optimization model based on dynamic programming in Urban traffic network. Journal of Advanced Transportation, 2018, 4539324. https://doi.org/10.1155/2018/4539324

Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018). Road damage detection and classification using deep neural networks with smartphone images. Computer-Aided Civil and Infrastructure Engineering, 33(12), 1127–1141. https://doi.org/10.1111/mice.12387

Marcelino, P., de Lurdes Antunes, M., Fortunato, E., & Gomes, M. C. (2021). Machine learning approach for pavement performance prediction. International Journal of Pavement Engineering, 22(3), 341–354. https://doi.org/10.1080/10298436.2019.1609673

Martinez, P., Al-Hussein, M., & Ahmad, R. (2019). A scientometric analysis and critical review of computer vision applications for construction. Automation in Construction, 107, 102947. https://doi.org/10.1016/j.autcon.2019.102947

Mathew, B. S., & Isaac, K. P. (2014). Optimisation of maintenance strategy for rural road network using genetic algorithm. International Journal of Pavement Engineering, 15(4), 352–360. https://doi.org/10.1080/10298436.2013.806807

Morcous, G., & Lounis, Z. (2005). Maintenance optimization of infrastructure networks using genetic algorithms. Automation in Construction, 14(1), 129–142. https://doi.org/10.1016/j.autcon.2004.08.014

Múčka, P. (2013). Correlation among road unevenness indicators and vehicle vibration response. Journal of Transportation Engineering, 139(8), 771–786. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000558

Murthi, P., Poongodi, K., & Gobinath, R. (2020). Correlation between rebound hammer number and mechanical properties of steel fibre reinforced pavement quality concrete. Materials Today: Proceedings, 39, 142–147. https://doi.org/10.1016/j.matpr.2020.06.402

Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 103(23), 8577–8582. https://doi.org/10.1073/pnas.0601602103

Ng, M. W., Lin, D. Y., & Waller, S. T. (2009). Optimal long-term infrastructure maintenance planning accounting for traffic dynamics. Computer-Aided Civil and Infrastructure Engineering, 24(7), 459–469. https://doi.org/10.1111/j.1467-8667.2009.00606.x

Ni, F. T., Zhang, J., & Chen, Z. Q. (2019). Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning. Computer-Aided Civil and Infrastructure Engineering, 34(5), 367–384. https://doi.org/10.1111/mice.12421

O’Flaherty, C., & Hughes, D. (2015). Highways: The location, design, construction and maintenance of road pavements (15th ed.). https://doi.org/10.1680/h5e.59931

Olawumi, T. O., & Chan, D. W. M. (2018). A scientometric review of global research on sustainability and sustainable development. Journal of Cleaner Production, 183, 231–250. https://doi.org/10.1016/j.jclepro.2018.02.162

Ouma, Y. O., & Hahn, M. (2016). Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform. Advanced Engineering Informatics, 30(3), 481–499. https://doi.org/10.1016/j.aei.2016.06.003

Outay, F., Mengash, H. A., & Adnan, M. (2020). Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges. Transportation Research Part A: Policy and Practice, 141, 116–129. https://doi.org/10.1016/j.tra.2020.09.018

Ouyang, Y., & Madanat, S. (2004). Optimal scheduling of rehabilitation activities for multiple pavement facilities: Exact and approximate solutions. Transportation Research Part A: Policy and Practice, 38(5), 347–365. https://doi.org/10.1016/j.tra.2003.10.007

Ouyang, Y., & Madanat, S. (2006). An analytical solution for the finite-horizon pavement resurfacing planning problem. Transportation Research Part B: Methodological, 40(9), 767–778. https://doi.org/10.1016/j.trb.2005.11.001

Ozbek, M. E., de la Garza, J. M., & Triantis, K. (2010). Data and modeling issues faced during the efficiency measurement of road maintenance using data evelopment analysis. Journal of Infrastructure Systems, 16(1), 21–30. https://doi.org/10.1061/(asce)1076-0342(2010)16:1(21)

Pan, Y., & Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, 103517. https://doi.org/10.1016/j.autcon.2020.103517

Peraka, N. S. P., & Biligiri, K. P. (2020). Pavement asset management systems and technologies: A review. Automation in Construction, 119, 103336. https://doi.org/10.1016/j.autcon.2020.103336

Pérez-Acebo, H., Linares-Unamunzaga, A., Abejón, R., & Rojí, E. (2018). Research trends in pavement management during the first years of the 21st century: A bibliometric analysis during the 2000-2013 Period. Applied Sciences, 8(7), 1041. https://doi.org/10.3390/app8071041

Prozzi, J. A., & Madanat, S. M. (2003). Incremental nonlinear model for predicting pavement serviceability. Journal of Transportation Engineering, 129(6), 635–641. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(635)

Ruiz, A., & Guevara, J. (2020). Environmental and economic impacts of road infrastructure development: Dynamic considerations and policies. Journal of Management in Engineering, 36(3), 04020006. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000755

Saberian, M., Li, J., & Setunge, S. (2019). Evaluation of permanent deformation of a new pavement base and subbase containing unbound granular materials, crumb rubber and crushed glass. Journal of Cleaner Production, 230, 38–45. https://doi.org/10.1016/j.jclepro.2019.05.100

Saha, P., Liu, R., Melson, C., & Boyles, S. D. (2014). Network model for rural roadway tolling with pavement deterioration and repair. Computer-Aided Civil and Infrastructure Engineering, 29(5), 315–329. https://doi.org/10.1111/mice.12057

Sánchez-Silva, M., Frangopol, D. M., Padgett, J., & Soliman, M. (2016). Maintenance and operation of infrastructure systems: Review. Journal of Structural Engineering, 142(9), F4016004. https://doi.org/10.1061/(asce)st.1943-541x.0001543

Santero, N. J. (2009). Pavements and the environment: a life-cycle assessment approach. University of California, Berkeley.

Santero, N. J., Masanet, E., & Horvath, A. (2011a). Life-cycle assessment of pavements. Part I: Critical review. Resources, Conservation and Recycling, 55(9–10), 801–809. https://doi.org/10.1016/j.resconrec.2011.03.010

Santero, N. J., Masanet, E., & Horvath, A. (2011b). Life-cycle assessment of pavements Part II: Filling the research gaps. Resources, Conservation and Recycling, 55(9–10), 810–818. https://doi.org/10.1016/j.resconrec.2011.03.009

Santos, J., Ferreira, A., & Flintsch, G. (2015). A life cycle assessment model for pavement management: Methodology and computational framework. International Journal of Pavement Engineering, 16(3), 268–286. https://doi.org/10.1080/10298436.2014.942861

Santos, J., Ferreira, A., & Flintsch, G. (2017). A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability. Journal of Cleaner Production, 164, 1380–1393. https://doi.org/10.1016/j.jclepro.2017.07.027

Sarsam, S. I. (2016). Pavement maintenance management system: A review. Trends in Transport Engineering and Applications, 3(2), 19–30.

Settari, C., Debieb, F., Kadri, E. H., & Boukendakdji, O. (2015). Assessing the effects of recycled asphalt pavement materials on the performance of roller compacted concrete. Construction and Building Materials, 101, 617–621. https://doi.org/10.1016/j.conbuildmat.2015.10.039

Shi, X., Veneziano, D., Xie, N., & Gong, J. (2013). Use of chloride-based ice control products for sustainable winter maintenance: A balanced perspective. Cold Regions Science and Technology, 86, 104–112. https://doi.org/10.1016/j.coldregions.2012.11.001

Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60. https://doi.org/10.1186/s40537-019-0197-0

Sivilevičius, H., & Vansauskas, V. (2013). Research and evaluation of ruts in the asphalt pavement on Lithuanian highways. Journal of Civil Engineering and Management, 19(5), 609–621. https://doi.org/10.3846/13923730.2013.817481

Statista. (2021). Total length of public roads in China from 2008 to 2020. https://www.statista.com/statistics/276051/total-length-of-public-roads-in-china/

Sultana, M., Rahman, A., & Chowdhury, S. (2013). A review of performance based maintenance of road infrastructure by contracting. International Journal of Productivity and Performance Management, 62(3), 276–292. https://doi.org/10.1108/17410401311309186

Sun, X., Gao, Z., Cao, P., & Zhou, C. (2019). Mechanical properties tests and multiscale numerical simulations for basalt fiber reinforced concrete. Construction and Building Materials, 202, 58–72. https://doi.org/10.1016/j.conbuildmat.2019.01.018

Suresh, K., & Kumarappan, N. (2013). Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem. Swarm and Evolutionary Computation, 9, 69–89. https://doi.org/10.1016/j.swevo.2012.11.003

Taylor, L., & Nitschke, G. (2019). Improving deep learning with generic data augmentation. In Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018) (pp. 1542–1547). https://doi.org/10.1109/SSCI.2018.8628742

Tehrani, S. S., Cowe Falls, L., & Mesher, D. (2015). Road users’ perception of roughness and the corresponding IRI threshold values. Canadian Journal of Civil Engineering, 42(4), 233–240. https://doi.org/10.1139/cjce-2014-0344

Thomas, O., & Sobanjo, J. (2013). Comparison of markov chain and semi-markov models for crack deterioration on flexible pavements. Journal of Infrastructure Systems, 19(2), 186–195. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000112

Thube, D. T. (2013). Highway development and management model (HDM-4): Calibration and adoption for low-volume roads in local conditions. International Journal of Pavement Engineering, 14(1), 50–59. https://doi.org/10.1080/10298436.2011.606320

TRIP. (2022). America’s surface transportation system and federal funding key facts. https://tripnet.org/wp-content/uploads/2019/05/Fact_Sheet_National.pdf

Tuttle, R. S. (2005). Condition analysis of concrete bridge decks in Utah. https://scholarsarchive.byu.edu/etd/567

Van Noortwijk, J. M., & Frangopol, D. M. (2004). Two probabilistic life-cycle maintenance models for deteriorating civil infrastructures. Probabilistic Engineering Mechanics, 19(4), 345–359. https://doi.org/10.1016/j.probengmech.2004.03.002

Vaneman, W. K., & Triantis, K. (2007). Evaluating the productive efficiency of dynamical systems. IEEE Transactions on Engineering Management, 54(3), 600–612. https://doi.org/10.1109/TEM.2007.900798

Vázquez-Méndez, M. E., Casal, G., Santamarina, D., & Castro, A. (2018). A 3D model for optimizing infrastructure costs in road design. Computer-Aided Civil and Infrastructure Engineering, 33(5), 423–439. https://doi.org/10.1111/mice.12350

Vyas, V., Singh, A. P., & Srivastava, A. (2021). Entropy-based fuzzy SWOT decision-making for condition assessment of airfield pavements. International Journal of Pavement Engineering, 22(10), 1226–1237. https://doi.org/10.1080/10298436.2019.1671590

Wang, J. (2013). Citation time window choice for research impact evaluation. Scientometrics, 94(3), 851–872. https://doi.org/10.1007/s11192-012-0775-9

Wang, H., & Wang, Z. (2013). Evaluation of pavement surface friction subject to various pavement preservation treatments. Construction and Building Materials, 48, 194–202. https://doi.org/10.1016/j.conbuildmat.2013.06.048

Wang, H., Al-Saadi, I., Lu, P., & Jasim, A. (2020). Quantifying greenhouse gas emission of asphalt pavement preservation at construction and use stages using life-cycle assessment. International Journal of Sustainable Transportation, 14(1), 25–34. https://doi.org/10.1080/15568318.2018.1519086

Waseem, A., & Yuan, X. X. (2013). Longitudinal local calibration of MEPDG permanent deformation models for reconstructed flexible pavements using PMS data. International Journal of Pavement Research and Technology, 6(4), 304–312.

Wu, W., Qurishee, M. A., Owino, J., Fomunung, I., Onyango, M., & Atolagbe, B. (2019). Coupling deep learning and UAV for infrastructure condition assessment automation. In 2018 IEEE International Smart Cities Conference (ISC2 2018), Kansas City, MO, USA. https://doi.org/10.1109/ISC2.2018.8656971

Wu, Z., Flintsch, G., Ferreira, A., & de Picado-Santos, L. (2012). Framework for multiobjective optimization of physical highway assets investments. Journal of Transportation Engineering, 138(12), 1411–1421. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000458

Wu, Z., & Flintsch, G. W. (2009). Pavement preservation optimization considering multiple objectives and budget variability. Journal of Transportation Engineering, 135(5), 305–315. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000006

Xinhua. (2013). China issues plan for national road network. Chinadaily. http://www.chinadaily.com.cn/china/2013-06/20/content_16641094.htm

Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., & Yang, X. (2018). Automatic pixel-level crack detection and measurement using fully convolutional network. Computer-Aided Civil and Infrastructure Engineering, 33(12), 1090–1109. https://doi.org/10.1111/mice.12412

Yao, L., Dong, Q., Jiang, J., & Ni, F. (2020). Deep reinforcement learning for long-term pavement maintenance planning. Computer-Aided Civil and Infrastructure Engineering, 35(11), 1230–1245. https://doi.org/10.1111/mice.12558

Yepes, V., Torres-Machi, C., Chamorro, A., & Pellicer, E. (2016). Optimal pavement maintenance programs based on a hybrid Greedy Randomized Adaptive Search Procedure Algorithm. Journal of Civil Engineering and Management, 22(4), 540–550. https://doi.org/10.3846/13923730.2015.1120770

Yousaf, M. H., Azhar, K., Murtaza, F., & Hussain, F. (2018). Visual analysis of asphalt pavement for detection and localization of potholes. Advanced Engineering Informatics, 38, 527–537. https://doi.org/10.1016/j.aei.2018.09.002

Yu, B., Gu, X., Ni, F., & Guo, R. (2015). Multi-objective optimization for asphalt pavement maintenance plans at project level: Integrating performance, cost and environment. Transportation Research Part D: Transport and Environment, 41, 64–74. https://doi.org/10.1016/j.trd.2015.09.016

Yu, B., Guo, Z., Peng, Z., Wang, H., Ma, X., & Wang, Y. (2019). Agent-based simulation optimization model for road surface maintenance scheme. Journal of Transportation Engineering, Part B: Pavements, 145(1), 04018065. https://doi.org/10.1061/jpeodx.0000097

Yu, B., & Lu, Q. (2012). Life cycle assessment of pavement: Methodology and case study. Transportation Research Part D: Transport and Environment, 17(5), 380–388. https://doi.org/10.1016/j.trd.2012.03.004

Yu, B., Wang, S., & Gu, X. (2018). Estimation and uncertainty analysis of energy consumption and CO2 emission of asphalt pavement maintenance. Journal of Cleaner Production, 189, 326–333. https://doi.org/10.1016/j.jclepro.2018.04.068

Zalama, E., Gómez-García-Bermejo, J., Medina, R., & Llamas, J. (2014). Road crack detection using visual features extracted by gabor filters. Computer-Aided Civil and Infrastructure Engineering, 29(5), 342–358. https://doi.org/10.1111/mice.12042

Zhang, A., Wang, K. C. P., Li, B., Yang, E., Dai, X., Peng, Y., Fei, Y., Liu, Y., Li, J. Q., & Chen, C. (2017). Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Computer-Aided Civil and Infrastructure Engineering, 32(10), 805–819. https://doi.org/10.1111/mice.12297

Zhang, A., Wang, K. C. P., Fei, Y., Liu, Y., Tao, S., Chen, C., Li, J. Q., & Li, B. (2018a). Deep learning–based fully automated pavement crack detection on 3D asphalt surfaces with an improved crackNet. Journal of Computing in Civil Engineering, 32(5), 04018041. https://doi.org/10.1061/(asce)cp.1943-5487.0000775

Zhang, D., Zou, Q., Lin, H., Xu, X., He, L., Gui, R., & Li, Q. (2018b). Automatic pavement defect detection using 3D laser profiling technology. Automation in Construction, 96, 350–365. https://doi.org/10.1016/j.autcon.2018.09.019

Zhang, H., Jin, R., Li, H., & Skibniewski, M. J. (2018c). Pavement maintenance–focused decision analysis on concession periods of PPP highway projects. Journal of Management in Engineering, 34(1), 04017047. https://doi.org/10.1061/(asce)me.1943-5479.0000568

Zhang, A., Wang, K. C. P., Fei, Y., Liu, Y., Chen, C., Yang, G., Li, J. Q., Yang, E., & Qiu, S. (2019). Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network. Computer-Aided Civil and Infrastructure Engineering, 34(3), 213–229. https://doi.org/10.1111/mice.12409

Zhong, B., Wu, H., Ding, L., Love, P. E. D., Li, H., Luo, H., & Jiao, L. (2019). Mapping computer vision research in construction: Developments, knowledge gaps and implications for research. Automation in Construction, 107, 102919. https://doi.org/10.1016/j.autcon.2019.102919