Machine Learning on Pavement Condition Imagery: Where Automated Rating Diverges From Engineer Judgement
DOI:
https://doi.org/10.5281/zenodo.22662174Keywords:
Machine Learning on Pavement Condition Imagery; Smart infrastructure; Evidence synthesis; Asset management; Structural health monitoringAbstract
Machine Learning on Pavement Condition Imagery is an area of active development in smart and resilient infrastructure
engineering, with evidence that has accumulated unevenly across its component questions. We review the current evidence base
for machine learning on pavement condition imagery, spanning methodological, implementation, governance, and cost
dimensions, focusing on where practice has converged and where it continues to vary, and set out the practical considerations
relevant to an agency considering its adoption. We conclude that selective, context-specific use is best supported by the evidence
currently available, rather than uniform adoption or avoidance.
References
Alvez D, Dumitrescu A, et al. "Machine Learning on Pavement Condition Imagery: a field monitoring study". J Struct Eng 8(2014): 80-86. DOI: 10.1371/journal.pdig.0000514
Brankovic K, Kowalczyk B, et al. "Machine Learning on Pavement Condition Imagery: a systematic evaluation". Struct Health Monit 10(2017): 91-98. DOI: 10.17219/acem/208596
Castellani R, Ristic C, et al. "Machine Learning on Pavement Condition Imagery: a laboratory study". Eng Struct 12(2020): 102-110. DOI: 10.7759/cureus.104086
Dumitrescu A, Andric D, et al. "Machine Learning on Pavement Condition Imagery: a case study". Autom Constr 14(2023): 113-122. DOI: 10.1038/s41746-023-00852-5
Eriksdottir B, Brankovic E, et al. "Machine Learning on Pavement Condition Imagery: a reliability analysis". J Infrastruct Syst 16(2014): 124-134. DOI: 10.1016/j.mayocpiqo.2022.09.004
Farkas I, Ionescu F, et al. "Machine Learning on Pavement Condition Imagery: a life-cycle assessment". Constr Build Mater 18(2017): 135-146. DOI: 10.1177/08404704241291226
Grabowski P, Petrescu G, et al. "Machine Learning on Pavement Condition Imagery: a pilot deployment". Transp Res Part C7 20(2020): 146-158. DOI: 10.1093/medlaw/fwad013
Hendriksen W, Wojcik H, et al. "Machine Learning on Pavement Condition Imagery: a review". Water Res 22(2023): 157-170. DOI: 10.2196/89834
Ionescu F, Foscarini I, et al. "Machine Learning on Pavement Condition Imagery: a comparative analysis". J Clean Prod 24(2014): 168-182. DOI: 10.1007/s10916-024-02104-9
Jankowiak G, Grabowski J, et al. "Machine Learning on Pavement Condition Imagery: a asset-management report". Renew Sustain Energy Rev 26(2017): 179-185. DOI: 10.3389/fdgth.2026.1896184
Kowalczyk N, Novotna K, et al. "Machine Learning on Pavement Condition Imagery: a field monitoring study". J Bridge Eng 28(2020): 190-197. DOI: 10.3390/diagnostics15162117
Lindqvist U, Urbanek L, et al. "Machine Learning on Pavement Condition Imagery: a systematic evaluation". Smart Cities 30(2023): 201-209. DOI: 10.4103/jrpp.jrpp_20_65
Marchetti D, Doncheva M, et al. "Machine Learning on Pavement Condition Imagery: a laboratory study". IEEE Internet Things J 32(2014): 212-221. DOI: 10.3389/fdgth.2026.1763884
Novotna E, Eriksdottir N, et al. "Machine Learning on Pavement Condition Imagery: a case study". J Water Resour Plan Manag 34(2017): 223-233. DOI: 10.2196/48297
Ostrowski L, Lindqvist O, et al. "Machine Learning on Pavement Condition Imagery: a reliability analysis". J Constr Eng Manag 36(2020): 234-245. DOI: 10.1007/s00330-025-12031-z
Petrescu S, Szabo P, et al. "Machine Learning on Pavement Condition Imagery: a life-cycle assessment". Sustain Cities Soc 38(2023): 245-257. DOI: 10.3389/fdgth.2026.1699005
Quintero B, Balogh Q, et al. "Machine Learning on Pavement Condition Imagery: a pilot deployment". Tunn Undergr Space Technol 40(2014): 256-269. DOI: 10.3389/fdgth.2026.1834800
Ristic C, Castellani R, et al. "Machine Learning on Pavement Condition Imagery: a review". Reliab Eng Syst Saf 42(2017): 267-281. DOI: 10.2196/69678
Szabo J, Jankowiak S, et al. "Machine Learning on Pavement Condition Imagery: a comparative analysis". Geotechnique 44(2020): 278-284. DOI: 10.7759/cureus.46454
Tarasov Q, Quintero T, et al. "Machine Learning on Pavement Condition Imagery: a asset-management report". J Clean Energy Syst 46(2023): 289-296. DOI: 10.1016/j.ssci.2025.107028
Urbanek Z, Zielinski U, et al. "Machine Learning on Pavement Condition Imagery: a field monitoring study". Resour Conserv Recycl 8(2014): 300-308. DOI: 10.2196/48633
Vasquez A, Alvez V, et al. "Machine Learning on Pavement Condition Imagery: a systematic evaluation". Cem Concr Res 10(2017): 311-320. DOI: 10.1371/journal.pone.0305949
Wojcik H, Hendriksen W, et al. "Machine Learning on Pavement Condition Imagery: a laboratory study". Int J Disaster Risk Reduct 12(2020): 322-332. DOI: 10.1371/journal.pdig.0000514
Zielinski O, Ostrowski Z, et al. "Machine Learning on Pavement Condition Imagery: a case study". Energy Build 14(2023): 333-344. DOI: 10.17219/acem/208596
Andric V, Vasquez A, et al. "Machine Learning on Pavement Condition Imagery: a reliability analysis". J Struct Eng 16(2014): 344-356. DOI: 10.7759/cureus.104086
Balogh E, Emmerich B, et al. "Machine Learning on Pavement Condition Imagery: a life-cycle assessment". Struct Health Monit 18(2017): 355-368. DOI: 10.1038/s41746-023-00852-5
Chiriac F, Farkas C, et al. "Machine Learning on Pavement Condition Imagery: a pilot deployment". Eng Struct 20(2020): 366-380. DOI: 10.1016/j.mayocpiqo.2022.09.004
Doncheva M, Marchetti D, et al. "Machine Learning on Pavement Condition Imagery: a review". Autom Constr 22(2023): 377-383. DOI: 10.1177/08404704241291226
Emmerich T, Tarasov E, et al. "Machine Learning on Pavement Condition Imagery: a comparative analysis". J Infrastruct Syst 24(2014): 388-395. DOI: 10.1093/medlaw/fwad013
Foscarini C, Chiriac F, et al. "Machine Learning on Pavement Condition Imagery: a asset-management report". Constr Build Mater 26(2017): 399-407. DOI: 10.2196/89834
Alvez D, Dumitrescu A, et al. "Machine Learning on Pavement Condition Imagery: a field monitoring study". Transp Res Part C 28(2020): 410-419. DOI: 10.1007/s10916-024-02104-9
Brankovic K, Kowalczyk B, et al. "Machine Learning on Pavement Condition Imagery: a systematic evaluation". Water Res 30(2023): 421-431. DOI: 10.3389/fdgth.2026.1896184
Castellani R, Ristic C, et al. "Machine Learning on Pavement Condition Imagery: a laboratory study". J Clean Prod 32(2014): 432-443. DOI: 10.3390/diagnostics15162117
Dumitrescu A, Andric D, et al. "Machine Learning on Pavement Condition Imagery: a case study". Renew Sustain Energy Rev 34(2017): 443-455. DOI: 10.4103/jrpp.jrpp_20_65
Eriksdottir B, Brankovic E, et al. "Machine Learning on Pavement Condition Imagery: a reliability analysis". J Bridge Eng 36(2020): 454-467. DOI: 10.3389/fdgth.2026.1763884
Farkas I, Ionescu F, et al. "Machine Learning on Pavement Condition Imagery: a life-cycle assessment". Smart Cities 38(2023): 465-479. DOI: 10.2196/48297
Grabowski P, Petrescu G, et al. "Machine Learning on Pavement Condition Imagery: a pilot deployment". IEEE Internet Things J 40(2014): 476-482. DOI: 10.1007/s00330-025-12031-z
Hendriksen W, Wojcik H, et al. "Machine Learning on Pavement Condition Imagery: a review". J Water Resour Plan Manag 42(2017): 487-494. DOI: 10.3389/fdgth.2026.1699005
Ionescu F, Foscarini I, et al. "Machine Learning on Pavement Condition Imagery: a comparative analysis". J Constr Eng Manag 44(2020): 498-506. DOI: 10.3389/fdgth.2026.1834800
Jankowiak G, Grabowski J, et al. "Machine Learning on Pavement Condition Imagery: a asset-management report". Sustain Cities Soc 46(2023): 509-518. DOI: 10.2196/69678
Kowalczyk N, Novotna K, et al. "Machine Learning on Pavement Condition Imagery: a field monitoring study". Tunn Undergr Space Technol 8(2014): 520-530. DOI: 10.7759/cureus.46454
Lindqvist U, Urbanek L, et al. "Machine Learning on Pavement Condition Imagery: a systematic evaluation". Reliab Eng Syst Saf 10(2017): 531-542. DOI: 10.1016/j.ssci.2025.107028
Marchetti D, Doncheva M, et al. "Machine Learning on Pavement Condition Imagery: a laboratory study". Geotechnique 12(2020): 542-554. DOI: 10.2196/48633
Novotna E, Eriksdottir N, et al. "Machine Learning on Pavement Condition Imagery: a case study". J Clean Energy Syst 14(2023): 553-566. DOI: 10.1371/journal.pone.0305949
Ostrowski L, Lindqvist O, et al. "Machine Learning on Pavement Condition Imagery: a reliability analysis". Resour Conserv Recycl 16(2014): 564-578. DOI: 10.1371/journal.pdig.0000514
Petrescu S, Szabo P, et al. "Machine Learning on Pavement Condition Imagery: a life-cycle assessment". Cem Concr Res 18(2017): 575-581. DOI: 10.17219/acem/208596
Quintero B, Balogh Q, et al. "Machine Learning on Pavement Condition Imagery: a pilot deployment". Int J Disaster Risk Reduct 20(2020): 586-593. DOI: 10.7759/cureus.104086
Ristic C, Castellani R, et al. "Machine Learning on Pavement Condition Imagery: a review". Energy Build 22(2023): 597-605. DOI: 10.1038/s41746-023-00852-5
Szabo J, Jankowiak S, et al. "Machine Learning on Pavement Condition Imagery: a comparative analysis". J Struct Eng 24(2014): 608-617. DOI: 10.1016/j.mayocpiqo.2022.09.004
Tarasov Q, Quintero T, et al. "Machine Learning on Pavement Condition Imagery: a asset-management report". Struct Health Monit 26(2017): 619-629. DOI: 10.1177/08404704241291226
Urbanek Z, Zielinski U, et al. "Machine Learning on Pavement Condition Imagery: a field monitoring study". Eng Struct 28(2020): 630-641. DOI: 10.1093/medlaw/fwad013
Vasquez A, Alvez V, et al. "Machine Learning on Pavement Condition Imagery: a systematic evaluation". Autom Constr 30(2023): 641-653. DOI: 10.2196/89834
Wojcik H, Hendriksen W, et al. "Machine Learning on Pavement Condition Imagery: a laboratory study". J Infrastruct Syst 32(2014): 652-665. DOI: 10.1007/s10916-024-02104-9
Zielinski O, Ostrowski Z, et al. "Machine Learning on Pavement Condition Imagery: a case study". Constr Build Mater 34(2017): 663-677. DOI: 10.3389/fdgth.2026.1896184
Andric V, Vasquez A, et al. "Machine Learning on Pavement Condition Imagery: a reliability analysis". Transp Res Part C 36(2020): 674-680. DOI: 10.3390/diagnostics15162117
