Smart Structures and Systems

  • 제31권4호
  • /
  • Pages.311-323
  • /
  • 2023
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Computer vision and deep learning-based post-earthquake intelligent assessment of engineering structures: Technological status and challenges

  • T. Jin (Department of Civil Engineering, Zhejiang University) ;
  • X.W. Ye (Department of Civil Engineering, Zhejiang University) ;
  • W.M. Que (Department of Civil Engineering, Zhejiang University) ;
  • S.Y. Ma (Department of Civil Engineering, Zhejiang University)
  • 투고 : 2022.10.24
  • 심사 : 2023.03.03
  • 발행 : 2023.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

키워드

과제정보

The work described in this paper was jointly supported by the National Natural Science Foundation of China (Grant No. 52178306), and the China Postdoctoral Science Foundation (Grant No. 2022M712787).

참고문헌

  1. Achille, C., Adami, A., Chiarini, S., Cremonesi, S., Fassi, F., Fregonese, L. and Taffurelli, L. (2015), "UAV-based photogrammetry and integrated technologies for architectural applications-methodological strategies for the after-quake survey of vertical structures in Mantua (Italy)", Sensors, 15(7), 15520-15539. https://doi.org/10.3390/s150715520
  2. Alipour, M., Harris, D.K. and Miller, G.R. (2019), "Robust pixel-level crack detection using deep fully convolutional neural networks", J. Comput Civil Eng., 33(6), 04019040. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000854
  3. Amirkolaee, H.A. and Arefi, H. (2019), "CNN-based estimation of pre-and post-earthquake height models from single optical images for identification of collapsed buildings", Remote Sens. Lett., 10(7), 679-688. https://doi.org/10.1080/2150704X.2019.1601277
  4. Azimi, M., Eslamlou, A.D. and Pekcan, G. (2020), "Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review", Sensors, 20(10), 2778. https://doi.org/10.3390/s20102778
  5. Baiocchi, V., Dominici, D., Milone, M.V. and Mormile, M. (2014), "Development of a software to optimize and plan the acquisitions from UAV and a first application in a post-seismic environment", Eur. J. Remote Sens., 47(1), 477-496. https://doi.org/10.5721/EuJRS20144727
  6. Balz, T. and Liao, M. (2010), "Building-damage detection using post-seismic high-resolution SAR satellite data", Int. J. Remote Sens., 31(13), 3369-3391. https://doi.org/10.1080/01431161003727671
  7. Bao, Y., Tang, Z., Li, H. and Zhang, Y. (2019), "Computer vision and deep learning-based data anomaly detection method for structural health monitoring", Struct. Health Monitor., 18(2), 401-421. https://doi.org/10.1177/1475921718757405
  8. Bemis, S.P., Micklethwaite, S., Turner, D., James, M.R., Akciz, S., Thiele, S.T. and Bangash, H.A. (2014), "Ground-based and UAV-Based photogrammetry: A multi-scale, high-resolution mapping tool for structural geology and paleoseismology", J. Struct. Geol., 69, 163-178. https://doi.org/10.1016/j.jsg.2014.10.007
  9. Binda, L., Saisi, A. and Tiraboschi, C. (2000), "Investigation procedures for the diagnosis of historic masonries", Constr. Build. Mater., 14(4), 199-233. https://doi.org/10.1016/S0950-0618(00)00018-0
  10. Bonnefoy-Claudet, S., Cotton, F. and Bard, P.Y. (2006), "The nature of noise wavefield and its applications for site effects studies: A literature review", Earth-Sci. Rev., 79(3-4), 205-227. https://doi.org/10.1016/j.earscirev.2006.07.004
  11. Boore, D.M. and Atkinson, G.M. (2008), "Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5%-damped PSA at spectral periods between 0.01 s and 10.0 s", Earthq. Spectra, 24(1), 99-138. https://doi.org/10.1193/1.2830434
  12. Brunner, D., Schulz, K. and Brehm, T. (2011), "Building damage assessment in decimeter resolution SAR imagery: A future perspective", Proceedings of 2011 Joint Urban Remote Sensing Event, Munich, Germany (CD-ROM).
  13. Cao, C., Liu, D., Singh, R.P., Zheng, S., Tian, R. and Tian, H. (2016), "Integrated detection and analysis of earthquake disaster information using airborne data", Geomat. Nat. Haz. Risk, 7(3), 1099-1128. https://doi.org/10.1080/19475705.2015.1020887
  14. Carreno, M.L., Cardona, O.D. and Barbat, A.H. (2010), "Computational tool for post-earthquake evaluation of damage in buildings", Earthq. Spectra, 26(1), 63-86. https://doi.org/10.1193/1.3282885
  15. Chang, C., Bo, J., Qi, W., Qiao, F. and Peng, D. (2022), "Distribution of large-and medium-scale loess landslides induced by the Haiyuan Earthquake in 1920 based on field investigation and interpretation of satellite images", Open Geosci., 14(1), 995-1019. https://doi.org/10.1515/geo-2022-0403
  16. Chini, M., Pierdicca, N. and Emery, W.J. (2008), "Exploiting SAR and VHR optical images to quantify damage caused by the 2003 Bam earthquake", IEEE T. Geosci. Remote, 47(1), 145-152. https://doi.org/10.1109/TGRS.2008.2002695
  17. Dai, K., Deng, J., Xu, Q., Li, Z.H., Shi, X.L., Hancock, C., Wen, N.L., Zhang, L.L. and Zhuo, G.C. (2022), "Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements", GISci. Remote Sens., 59(1), 1226-1242. https://doi.org/10.1080/15481603.2022.2100054
  18. Dizaji, M.S. and Harris, D.K. (2019), "3D InspectionNet: A deep 3D convolutional neural networks based approach for 3D defect detection on concrete columns", In: Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII, Volk. 10971, pp. 67-77. https://doi.org/10.1117/12.2514387
  19. Dominici, D., Alicandro, M. and Massimi, V. (2017), "UAV photogrammetry in the post-earthquake scenario: case studies in L'Aquila", Geomat. Nat. Haz. Risk, 8(1), 87-103. https://doi.org/10.1080/19475705.2016.1176605
  20. Dong, Y., Li, Q., Dou, A. and Wang, X. (2011), "Extracting damages caused by the 2008 Ms 8.0 Wenchuan earthquake from SAR remote sensing data", J. Asian Earth Sci., 40(4), 907-914. https://doi.org/10.1016/j.jseaes.2010.07.009
  21. Dong, L., Shan, J. and Ye, Y. (2014), "An attempt of using straight-line information for building damage detection based only on post-earthquake optical imagery", Proceedings of IOP Conference Series: Earth and Environmental Science, Wu Han, China (CD-ROM).
  22. Dramsch, J.S. (2020), "70 years of machine learning in geoscience in review", Adv. Geophys., 61, 1-55. https://doi.org/10.1016/bs.agph.2020.08.002
  23. Duarte, D., Nex, F., Kerle, N. and Vosselman, G. (2020), "Detection of seismic facade damages with multi-temporal oblique aerial imagery", Gisci. Remote. Sens., 57(5), 670-686. https://doi.org/10.1080/15481603.2020.1768768
  24. Duffy, J.P. and Anderson, K. (2016), "A 21st-century renaissance of kites as platforms for proximal sensing", Prog. Phys. Geog., 40(2), 352-361. https://doi.org/10.1177/0309133316641810
  25. Ebrahimkhanlou, A., Farhidzadeh, A. and Salamone, S. (2015), "Multifractal analysis of two-dimensional images for damage assessment of reinforced concrete structures", Proceedings of Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015, CA, USA (CD-ROM).
  26. Fayaz, J. and Galasso, C. (2022), "A deep neural network framework for real-time on-site estimation of acceleration response spectra of seismic ground motions", Comput. Aided Civil Infrastruct. Eng., 1-17. https://doi.org/10.1111/mice.12830
  27. Freeman, M., Vernon, C., Berrett, B., Hastings, N., Derricott, J., Pace, J., Horne, B., Hammond, J., Janson, J., Chiabrando, F., Hedengren, J. and Franke, K. (2019), "Sequential earthquake damage assessment incorporating optimized sUAV remote sensing at Pescara del Tronto", Geosciences, 9(8), 332. https://doi.org/10.3390/geosciences9080332
  28. Fu, B. and Lin, A. (2003), "Spatial distribution of the surface rupture zone associated with the 2001 Ms 8.1 Central Kunlun earthquake, northern Tibet, revealed by satellite remote sensing data", Int. J. Remote Sens., 24(10), 2191-2198. https://doi.org/10.1080/0143116031000075918
  29. Fu, R., He, J., Liu, G., Li, W., Mao, J., He, M. and Lin, Y. (2022), "Fast Seismic Landslide Detection Based on Improved Mask R-CNN", Remote Sens.-basel, 14(16), 3928. https://doi.org/10.3390/rs14163928
  30. German, S., Brilakis, I. and DesRoches, R. (2012), "Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments", Adv. Eng. Inform., 26(4), 846-858. https://doi.org/10.1016/j.aei.2012.06.005
  31. Ghorbanzadeh, O., Meena, S.R., Blaschke, T. and Aryal, J. (2019), "UAV-based slope failure detection using deep-learning convolutional neural networks", Remote Sens.-basel, 11(17), 2046. https://doi.org/10.3390/rs11172046
  32. Gorgin, R. and Wang, Z. (2021), "Baseline-free damage imaging technique for Lamb wave based structural health monitoring systems", Smart Struct. Syst., Int. J., 28(5), 689-698. https://doi.org/10.12989/sss.2021.28.5.689
  33. Guzzetti, F., Mondini, A.C., Cardinali, M., Fiorucci, F., Santangelo, M. and Chang, K.T. (2012), "Landslide inventory maps: New tools for an old problem", Earth-Sci. Rev., 112(1-2), 42-66. https://doi.org/10.1016/j.earscirev.2012.02.001
  34. Hashash, Y.M.A., Jeffrey, J.H., Birger, S. and John, I-C. Y. (2001), "Seismic design and analysis of underground structures", Tunn. Undergr. Sp. Tech., 16(4), 247-293. https://doi.org/10.1016/S0886-7798(01)00051-7
  35. Holden, T., Restrepo, J. and Mander, J.B. (2003), "Seismic performance of precast reinforced and prestressed concrete walls", J. Struct. Eng., 129(3), 286-296. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:3(286)
  36. Hu, S., Zhu, S. and Wang, W. (2022), "Machine learning-driven probabilistic residual displacement-based design method for improving post-earthquake repairability of steel moment-resisting frames using self-centering braces", J. Build. Eng., 61, 105225. https://doi.org/10.1016/j.jobe.2022.105225
  37. Huang, Q., Wang, Y., Xu, J., Nishyirimbere, A. and Li, Z. (2017), "Geo-hazard detection and monitoring using SAR and optical images in a snow-covered area: the Menyuan (China) test site", ISPRS Int. J. Geo-Inf., 6(10), 293. https://doi.org/10.3390/ijgi6100293
  38. Huang, W.L., Gao, F., Liao, J.P. and Chuai, X.Y. (2021), "A deep learning network for estimation of seismic local slopes", Petrol. Sci., 18(1), 92-105. https://doi.org/10.1007/s12182-020-00530-1
  39. Huynh, T.C., Park, J.H., Jung, H.J. and Kim, J.T. (2019), "Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing", Autom. Constr., 105, 102844. https://doi.org/10.1016/j.autcon.2019.102844
  40. Jalayer F. and Cornell, C.A. (2009), "Alternative non-linear demand estimation methods for probability-based seismic assessments", Earthq. Eng. Struct. D., 38(8), 951-972. https://doi.org/10.1002/eqe.876
  41. Ji, M., Liu, L. and Buchroithner, M. (2018), "Identifying collapsed buildings using post-earthquake satellite imagery and convolutional neural networks: A case study of the 2010 Haiti earthquake", Remote Sens.-basel, 10(11), 1689. https://doi.org/10.3390/rs10111689
  42. Jing, Y., Ren, Y., Liu, Y., Wang, D. and Yu, L. (2022), "Automatic Extraction of Damaged Houses by Earthquake Based on Improved YOLOv5: A Case Study in Yangbi", Remote Sens.-basel, 14(2), 382. https://doi.org/10.3390/rs14020382
  43. Kakooei, M. and Baleghi, Y. (2017), "Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment", Int. J. Remote Sens., 38(8-10), 2511-2534. https://doi.org/10.1080/01431161.2017.1294780
  44. Kaloop, M.R. and Kim, D. (2014), "GPS-structural health monitoring of a long span bridge using neural network adaptive filter", Surv. Rev., 46(334), 7-14. https://doi.org/10.1179/1752270613Y.0000000053
  45. Kamari, M., Kim, J. and Ham, Y. (2022), "Analyzing Safety Risk Imposed by Jobsite Debris to Nearby Built Environments Using Geometric Digital Twins and Vision-Based Deep Learning", J. Comput. Civil Eng., 36(6), 04022033. https://doi.org/10.1061/(ASCE)CP.1943-5487.0001044
  46. Kang, D. and Cha, Y.J. (2018), "Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging", Comput.-Aided Civil Infrastruct. Eng., 33(10), 885-902. https://doi.org/10.1111/mice.12375
  47. Khodaverdi zahraee, N and Rastiveis, H. (2017), "Object-oriented analysis of satellite images using artificial neural networks for post-earthquake buildings change detection", Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci., Tehran, Iran. (CD-ROM)
  48. Kim, M. and Song, J. (2022), "Near-real-time identification of seismic damage using unsupervised deep neural network", J. Eng. Mech., 148(3), 04022006. https://doi.org/10.1061/(ASCE)EM.1943-7889.0002066
  49. Kim, I.H., Jeon, H., Baek, S.C., Hong, W.H. and Jung, H.J. (2018), "Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle", Sensors, 18(6), 1881. https://doi.10.3390/s18061881
  50. Kim, T., Song, J. and Kwon, O.S. (2020), "Pre- and post-earthquake regional loss assessment using deep learning", Earthq. Eng. Struct. Dyn., 49(7), 657-678. https://doi.org/10.1002/eqe.3258
  51. Kubat, M., Holte, R.C. and Matwin S. (1998), "Machine learning for the detection of oil spills in satellite radar images", Mach. Learn., 30(2), 195-215. https://doi.org/10.1023/A:1007452223027
  52. Lagomarsino, S. and Giovinazzi, S. (2006), "Macroseismic and mechanical models for the vulnerability and damage assessment of current buildings", B. Earthq. Eng., 4(4), 415-443. https://doi.org/10.1007/s10518-006-9024-z
  53. LeCun, Y., Bengio, Y. and Hinton, G. (2015), "Deep learning", Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  54. Li, C.C., Zhang, G.S., Lei, T.J. and Gong, A.D. (2011), "Quick image-processing method of UAV without control points data in earthquake disaster area", Transact. Nonferrous Metals Soc. China, 21, s523-s528. https://doi.org/10.1016/S1003-6326(12)61635-5
  55. Li, J., He, Z. and Zhao, X. (2021), "A data-driven building's seismic response estimation method using a deep convolutional neural network", IEEE Access., 9, 50061-50077. https://doi.org/10.1109/ACCESS.2021.3065837
  56. Liou, Y.A., Kar, S.K. and Chang, L. (2010), "Use of high-resolution FORMOSAT-2 satellite images for post-earthquake disaster assessment: a study following the 12 May 2008 Wenchuan Earthquake", Int. J. Remote Sens., 31(13), 3355-3368. https://doi.org/10.1080/01431161003727655
  57. Liu, H., Koyama, C., Zhu, J., Liu, Q. and Sato, M. (2016), "Post-earthquake damage inspection of wood-frame buildings by a polarimetric GB-SAR system", Remote Sens., 8(11), 935. https://doi.org/10.3390/rs8110935
  58. Luo, X., Feng, Q., Jia, Y., Chen, H., Song, Y. and Xu, W. (2022), "The Large-Scale Investigation and Analysis of Lophodermium piceae in Subalpine Areas Based on Satellite Multi-Spectral Remote Sensing", Diversity, 14(9), 727. https://doi.org/10.3390/d14090727
  59. Ma, H., Liu, Y., Ren, Y. and Yu, J. (2019), "Detection of collapsed buildings in post-earthquake remote sensing images based on the improved YOLOv3", Remote Sens., 12(1), 44. https://doi.org/10.3390/rs12010044
  60. Mantawy, I.M. and Mantawy, M.O. (2022), "Convolutional neural network based structural health monitoring for rocking bridge system by encoding time-series into images", Struct. Control. Health Monit., 29(3), e2897. https://doi.org/10.1002/stc.2897
  61. Massonnet, D., Rossi, M., Carmona, C., Adragna, F., Peltzer, G., Feigl, K. and Rabaute, T. (1993), "The displacement field of the Landers earthquake mapped by radar interferometry", Nature, 364(6433), 138-142. https://doi.org/10.1038/364138a0
  62. Matin, S.S. and Pradhan, B. (2021), "Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review", Geocarto Int., 1-27. https://doi.org/10.1080/10106049.2021.1933213
  63. Matsuoka, M. and Nojima, N. (2010), "Building damage estimation by integration of seismic intensity information and satellite L-band SAR imagery", Remote Sens.-Basel, 2(9), 2111-2126. https://doi.org/10.3390/rs2092111
  64. Miura, H., Aridome, T. and Matsuoka, M. (2020), "Deep learning-based identification of collapsed, non-collapsed and blue tarp-covered buildings from post-disaster aerial images", Remote Sens., 12(12), 1924. https://doi.org/10.3390/rs12121924
  65. Miyamoto, T. and Yamamoto, Y. (2021), "Using 3-D Convolution and Multimodal Architecture for Earthquake Damage Detection Based on Satellite Imagery and Digital Urban Data", IEEE J-Stars., 14, 8606-8613. https://doi.org/10.1109/JSTARS.2021.3102701
  66. Narazaki, Y., Hoskere, V., Chowdhary, G. and Spencer Jr, B.F. (2022), "Vision-based navigation planning for autonomous post-earthquake inspection of reinforced concrete railway viaducts using unmanned aerial vehicles", Autom. Constr., 137, 104214. https://doi.org/10.1016/j.autcon.2022.104214
  67. Nedjati, A., Vizvari, B. and Izbirak, G. (2016), "Post-earthquake response by small UAV helicopters", Natural Hazards, 80(3), 1669-1688. https://doi.org/10.1007/s11069-015-2046-6
  68. Nex, F., Duarte, D., Tonolo, F.G. and Kerle, N. (2019), "Structural building damage detection with deep learning: Assessment of a state-of-the-art CNN in operational conditions", Remote Sens.-basel, 11(23), 2765. https://doi.org/10.3390/rs11232765
  69. Ouzounov, D., Bryant, N., Logan, T., Pulinets, S. and Taylor, P. (2006), "Satellite thermal IR phenomena associated with some of the major earthquakes in 1999-2003", Phys. Chem. Earth, Parts A/B/C, 31(4-9), 154-163. https://doi.org/10.1016/j.pce.2006.02.036
  70. Rajput, S., Ippili, A., Puraswani, D., Johri, S., Nadathur, A. and Dhar, S. (2020), "Impact of earthquakes based on satellite images using IoT and sensor networks", Proceedings of the 12th International Conference on Communication Software and Networks, Chong Qing, China (CD-ROM).
  71. Rao, G.N.S. and Satyam, D.N. (2022), "Deterministic and Probabilistic Seismic Hazard Analysis of Tindharia, Darjeeling Sikkim Himalaya, India", J. Geol. Soc. India, 98(9), 1295-1300. https://doi.org/10.1007/s12594-022-2165-0
  72. Riedel, I., Gueguen, P., Dalla Mura, M., Pathier, E., Leduc, T. and Chanussot, J. (2015), "Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods", Natural Hazards, 76(2), 1111-1141. https://doi.org/10.1007/s11069-014-1538-0
  73. Rosen, P.A., Hensley, S., Wheeler, K., Sadowy, G., Miller, T., Shaffer, S., Muellerschoen, R., Jones, C., Madsen, S. and Zebker, H. (2007), "UAVSAR: New NASA airborne SAR system for research", IEEE Aero. El. Sys. Mag., 22(11), 21-28. https://doi.org/10.1109/MAES.2007.4408523
  74. Saba, S.B., Ali, M., Turab, S.A., Waseem, M. and Faisal, S. (2022), "Comparison of pixel, sub-pixel and object-based image analysis techniques for co-seismic landslides detection in seismically active area in Lesser Himalaya, Pakistan", Nat. Hazards, 1-16. https://doi.org/10.1007/s11069-022-05642-y
  75. Sajedi, S.O. and Liang, X. (2019), "A Convolutional Cost-Sensitive Crack Localization Algorithm for Automated and Reliable RC Bridge Inspection", arXiv preprint arXiv: 1905.09716. https://doi.org/10.48550/arXiv.1905.09716
  76. Santos, R., Ribeiro, D., Lopes, P., Cabral, R. and Calcada, R. (2022), "Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles", Autom. Constr., 139, 104324. https://doi.org/10.1016/j.autcon.2022.104324
  77. Seydi, S.T., Rastiveis, H., Kalantar, B., Halin, A.A. and Ueda, N. (2022), "BDD-Net: An End-to-End Multiscale Residual CNN for Earthquake-Induced Building Damage Detection", Remote Sens.-Basel, 14(9), 2214. https://doi.org/10.3390/rs14092214
  78. Sezen, H., Whittaker, A.S., Elwood, K.J. and Mosalam, K.M. (2003), "Performance of reinforced concrete buildings during the August 17, 1999 Kocaeli, Turkey earthquake, and seismic design and construction practise in Turkey", Eng. Struct., 25(1), 103-114. https://doi.org/10.1016/S0141-0296(02)00121-9
  79. Shang, Q., Guo, X., Li, J. and Wang, T. (2022), "Post-earthquake health care service accessibility assessment framework and its application in a medium-sized city", Reliab. Eng. Syst. Safe., 228, 108782. https://doi.org/10.1016/j.ress.2022.108782
  80. Song, D., Tan, X., Wang, B., Zhang, L., Shan, X. and Cui, J. (2020), "Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery", Int. J. Remote Sens., 41(3), 1040-1066. https://doi.org/10.1080/01431161.2019.1655175
  81. Sony, S., Dunphy, K., Sadhu, A. and Capretz, M. (2021), "A systematic review of convolutional neural network-based structural condition assessment techniques", Eng. Struct., 226, 111347. https://doi.org/10.1016/j.engstruct.2020.111347
  82. Spencer Jr, B.F., Hoskere, V. and Narazaki, Y. (2019), "Advances in computer vision-based civil infrastructure inspection and monitoring", Engineering, 5(2), 199-222. https://doi.org/10.1016/j.eng.2018.11.030
  83. Sun, X., Chen, X., Yang, L., Wang, W., Zhou, X., Wang, L. and Yao, Y. (2022), "Using InSAR and PolSAR to Assess Ground Displacement and Building Damage after a Seismic Event: Case Study of the 2021 Baicheng Earthquake", Remote Sens.-Basel, 14(13), 3009. https://doi.org/10.3390/rs14133009
  84. Syifa, M., Kadavi, P.R. and Lee, C.W. (2019), "An artificial intelligence application for post-earthquake damage mapping in Palu, central Sulawesi, Indonesia", Sensors, 19(3), 542. https://doi.org/10.3390/s19030542
  85. Tang, Z., Chen, Z., Bao, Y. and Li, H. (2019), "Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring", Struct. Control Hlth., 26(1), e2296. https://doi.org/10.1002/stc.2296
  86. Tronin, A.A. (2009), "Satellite remote sensing in seismology. A review", Remote Sens.-Basel, 2(1), 124-150. https://doi.org/10.3390/rs2010124
  87. Vafaei, M., Adnan, A.B. and Rahman A.B.A. (2013), "Real-time seismic damage detection of concrete shear walls using artificial neural networks", J. Earthq. Eng., 17(1), 137-154. https://doi.org/10.1080/13632469.2012.713559
  88. Verykokou, S., Ioannidis, C., Athanasiou, G., Doulamis, N. and Amditis, A. (2018), "3D reconstruction of disaster scenes for urban search and rescue", Multimed. Tools Appl., 77(8), 9691-9717. https://doi.org/10.1007/s11042-017-5450-y
  89. Wang, X. and Li, P. (2015), "Extraction of earthquake-induced collapsed buildings using very high-resolution imagery and airborne lidar data", Int. J. Remote. Sens., 36(8), 2163-2183. http://dx.doi.org/10.1080/01431161.2015.1034890
  90. Wang, T., Wei, S. and Jonsson, S. (2015), "Coseismic displacements from SAR image offsets between different satellite sensors: Application to the 2001 Bhuj (India) earthquake", Geophys. Res. Lett., 42(17), 7022-7030. https://doi.org/10.1002/2015GL064585
  91. Wang, B., Tan, X., Song, D.M. and Zhang, L. (2020), "Rapid identification of post-earthquake collapsed buildings via multi-scale morphological profiles with multi-structuring elements", IEEE Access, 8, 122036-122056. https://doi.org/10.1109/ACCESS.2020.3007255
  92. Wang, C., Antos, S.E. and Triveno, L.M. (2021a), "Automatic detection of unreinforced masonry buildings from street view images using deep learning-based image segmentation", Autom. Constr., 132, 103968. https://doi.org/10.1016/j.autcon.2021.103968
  93. Wang, L., Spencer Jr, B.F., Li, J. and Hu, P. (2021b), "A fast image-stitching algorithm for characterization of cracks in large-scale structures", Smart Struct. Syst., Int. J., 27(4), 593-605. https://doi.org/10.12989/sss.2021.27.4.593
  94. Wang, Y., Cui, L., Zhang, C., Chen, W., Xu, Y. and Zhang, Q. (2022), "A two-stage seismic damage assessment method for small, dense, and imbalanced buildings in remote sensing images", Remote Sens., 14(4), 1012. https://doi.org/10.3390/rs14041012
  95. Xiong, W., Chen, W., Wang, D.Z., Wen, Y.M., Nie, Z.S., Liu, G., Wang, D.J., Yu, P.F., Qiao, X.J. and Zhao, B. (2022), "Coseismic slip and early afterslip of the 2021 Mw 7.4 Maduo, China earthquake constrained by GPS and InSAR data", Tectonophysics, 840, 229558. https://doi.org/10.1016/j.tecto.2022.229558
  96. Xu, B., Wu, Z., Yokoyama, K., Harada, T. and Chen, G. (2005), "A soft post-earthquake damage identification methodology using vibration time series", Smart Mater. Struct., 14(3), S116. https://doi.org/10.1088/0964-1726/14/3/014
  97. Xu, X., Li, X. and Liu, C. (2012), "Building damage detection based on single high-resolution remote sensing imagery", Proceedings of the International Conference on Automatic Control and Artificial Intelligence, Xia Men, China (CD-ROM).
  98. Xu, Y., Wei, S., Bao, Y. and Li, H. (2019), "Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network", Struct. Control. Health Monit., 26(3), e2313. https://doi.org/10.1002/stc.2313
  99. Xu, Y., Lu, X., Cetiner, B. and Taciroglu, E. (2021), "Real-time regional seismic damage assessment framework based on long short-term memory neural network", Comput.-Aided Civil Infrastruct. Eng., 36(4), 504-521. https://doi.org/10.1111/mice.12628
  100. Yao, Q.L. and Qiang, Z.J. (2012), "Thermal infrared anomalies as a precursor of strong earthquakes in the distant future", Natural Hazards, 62(3), 991-1003. https://doi.org/10.1007/s11069-012-0130-8
  101. Ye, X.W., Jin, T. and Yun, C.B. (2019), "A review on deep learning-based structural health monitoring of civil infrastructures", Smart Struct. Syst., Int. J., 24(5), 567-585. https://doi.org/10.12989/sss.2019.24.5.567
  102. Ye, X.W., Jin, T., Li, Z.X., Ma, S.Y., Ding, Y. and Ou, Y.H. (2021), "Structural crack detection from benchmark data sets using pruned fully convolutional networks", J. Struct. Eng., 147(11), 04721008. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003140
  103. Ye, X.W., Ma, S.Y., Liu, Z.X., Ding, Y., Li, Z.X. and Jin, T. (2022), "Post-earthquake damage recognition and condition assessment of bridges using UAV integrated with deep learning approach", Struct. Control Hlth., e3128. https://doi.org/10.1002/stc.3128
  104. Yu, Y., Wang, C., Gu, X. and Li, J. (2019), "A novel deep learning-based method for damage identification of smart building structures", Struct. Health Monit., 18(1), 143-163. https://doi.org/10.1177/1475921718804132
  105. Zhai, W., Huang, C. and Pei, W. (2018), "Two new polarimetric feature parameters for the recognition of the different kinds of buildings in earthquake-stricken areas based on entropy and eigenvalues of PolSAR decomposition", Remote Sens., 10(10), 1613. https://doi.org/10.3390/rs10101613
  106. Zhang, M., Yang, X., Zhang, J. and Li, G. (2022a), "Post-earthquake resilience optimization of a rural 'road-bridge' transportation network system", Reliab. Eng. Syst. Safe., 225, 108570. https://doi.org/10.1016/j.ress.2022.108570
  107. Zhang, Q., Li, Y., Zhang, J., Tian, Y., Tian, T. and Li, B. (2022b), "Slip deformation along the Gyaring Co fault from InSAR and GPS", Acta Geophys., 1-11. https://doi.org/10.1080/01431161003727655
  108. Zhao, B., Wang, Y., Li, W., Lu, H. and Li, Z. (2022), "Evaluation of factors controlling the spatial and size distributions of landslides, 2021 Nippes earthquake, Haiti", Geomorphology, 415, 108419. https://doi.org/10.1016/j.geomorph.2022.108419
  109. Zhu, Z., German, S. and Brilakis, I. (2011), "Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation", Autom. Constr., 20(7), 874-883. https://doi.org/10.1016/j.autcon.2011.03.004 Zou, D., Zhang, M., Bai, Z., Liu, T., Zhou, A., Wang, X. and
  110. Zhang, S. (2022), "Multicategory damage detection and safety assessment of post-earthquake reinforced concrete structures using deep learning", Comput-Aided. Civil Infrastruct. Eng., 37, 1188-1204. https://doi.org/10.1111/mice.12815