Bilevel Thresholding–Based Iterative Analysis for Building-Surface Damage Detection in a Postearthquake Environment
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 5
Abstract
Manual building damage inspection in a postearthquake environment is typically resource-consuming and prone to limitations based on subjectivity. A bilevel thresholding–based iterative framework is proposed to automate the delineation of concrete-spalling using three-dimensional (3D) point-cloud data. Point-level surface variation was used for damage point characterization, and two stopping conditions were defined for process automation. Synthetic building element data with varying point distribution and damage region characteristics were used for quantitative analysis and comparison with the state-of-the-art iterative refinement analysis. Comparative analysis demonstrated that the proposed algorithm rendered damaged region detection with improved completeness and correctness. In this study, use of Matthews correlation coefficient (MCC) and mean generalized intersection over union (mGIoU) metrics for performance evaluation is proposed. For low-noise rectangular-wall samples used, an average increase of 20% and 55% MCC value compared with generalized iterative refinement analysis was observed for Stopping conditions I and II, respectively. Similarly, an average increase of 31% and 21% mGIoU was observed. Furthermore, the proposed algorithm has the potential to generalize better compared with the state-of-the-art because it does not require tuning or training using ground-truth data.
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Data Availability Statement
All the data, models, or code that support the findings of this study can be made available from the corresponding author upon reasonable request.
Acknowledgments
This project was supported by the funds from Texas A&M Engineering Experiment Station (TEES) startup, and the support is highly appreciated. The authors would like to thank Mr. Kaustubh Mahesh Tangsali, Mr. Kartik Prakash, Mr. Shah Akib Sarwar, and Mr. James Nolan for their valuable feedback on the writing.
References
Anders, N., J. Valente, R. Masselink, and S. Keesstra. 2019. “Comparing filtering techniques for removing vegetation from UAV-based photogrammetric point clouds.” Drones 3 (3): 61. https://doi.org/10.3390/drones3030061.
ATC (Applied Technology Council). 2005. Field manual: Postearthquake safety evaluation of buildings. Redwood City, CA: R. P. Gallagher Associates.
Axel, C., and J. A. van Aardt. 2017. “Building damage assessment using airborne lidar.” J. Appl. Remote Sens. 11 (4): 046024. https://doi.org/10.1117/1.JRS.11.046024.
Balz, T., and M. Liao. 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.
Belton, D., and D. D. Lichti. 2006. “Classification and segmentation of terrestrial laser scanner point clouds using local variance information.” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36 (5): 44–49.
Bhandari, A. K., V. K. Singh, A. Kumar, and G. K. Singh. 2014. “Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using kapur’s entropy.” Expert Syst. Appl. 41 (7): 3538–3560. https://doi.org/10.1016/j.eswa.2013.10.059.
Boughorbel, S., F. Jarray, and M. El-Anbari. 2017. “Optimal classifier for imbalanced data using Matthews correlation coefficient metric.” PLoS One 12 (6): e0177678. https://doi.org/10.1371/journal.pone.0177678.
Cha, Y.-J., W. Choi, and O. Büyüköztürk. 2017. “Deep learning-based crack damage detection using convolutional neural networks.” Comput.-Aided Civ. Infrastruct. Eng. 32 (5): 361–378. https://doi.org/10.1111/mice.12263.
Charrad, M., N. Ghazzali, V. Boiteau, and A. Niknafs. 2014. “Nbclust: An r package for determining the relevant number of clusters in a data set.” J. Stat. Software 61 (6): 1–36. https://doi.org/10.18637/jss.v061.i06.
Chen, G., G. J. Hay, L. M. Carvalho, and M. A. Wulder. 2012. “Object-based change detection.” Int. J. Remote Sens. 33 (14): 4434–4457. https://doi.org/10.1080/01431161.2011.648285.
Chiang, W., K. F. Liu, and J. Lee. 2000. “Bridge damage assessment through fuzzy petri net based expert system.” J. Comput. Civ. Eng. 14 (2): 141–149. https://doi.org/10.1061/(ASCE)0887-3801(2000)14:2(141).
Chicco, D., and G. Jurman. 2020. “The advantages of the Matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation.” BMC Genom. 21 (1): 6. https://doi.org/10.1186/s12864-019-6413-7.
Ci, T., Z. Liu, Y. Wang, Q. Lin, and D. Wu. 2018. “An automated technique for damage mapping after earthquakes by detecting changes between high-resolution images.” Nat. Hazards Earth Syst. Sci. Discuss. 1–19. https://doi.org/10.5194/nhess-2018-73.
Coburn, C., and A. C. Roberts. 2004. “A multiscale texture analysis procedure for improved forest stand classification.” Int. J. Remote Sens. 25 (20): 4287–4308. https://doi.org/10.1080/0143116042000192367.
Dai, M., W. O. Ward, G. Meyers, D. D. Tingley, and M. Mayfield. 2021. “Residential building facade segmentation in the urban environment.” Build. Environ. 199 (Jul): 107921. https://doi.org/10.1016/j.buildenv.2021.107921.
Dawood, T., Z. Zhu, and T. Zayed. 2017. “Machine vision-based model for spalling detection and quantification in subway networks.” Autom. Constr. 81 (Sep): 149–160. https://doi.org/10.1016/j.autcon.2017.06.008.
Delgado, R., and X.-A. Tibau. 2019. “Why Cohen’s kappa should be avoided as performance measure in classification.” PLoS One 14 (9): e0222916. https://doi.org/10.1371/journal.pone.0222916.
Doshi, J., S. Basu, and G. Pang. 2018. “From satellite imagery to disaster insights.” Preprints, submitted December 17, 2018. http://arxiv.org/abs/1812.07033.
El Amin, A. M., Q. Liu, and Y. Wang. 2017. “Zoom out CNNS features for optical remote sensing change detection.” In Proc., 2017 2nd Int. Conf. on Image, Vision and Computing (ICIVC), 812–817. New York: IEEE.
Erdogan, M., and A. Yilmaz. 2019. “Detection of building damage caused by van earthquake using image and digital surface model (DSM) difference.” Int. J. Remote Sens. 40 (10): 3772–3786. https://doi.org/10.1080/01431161.2018.1552816.
Erkal, B. G., and J. F. Hajjar. 2017. “Laser-based surface damage detection and quantification using predicted surface properties.” Autom. Constr. 83 (Nov): 285–302. https://doi.org/10.1016/j.autcon.2017.08.004.
FEMA. 2016. Damage assessment operations manual. Washington, DC: FEMA.
Freedman, D., and P. Diaconis. 1981. “On the histogram as a density estimator: L 2 theory.” Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 57 (4): 453–476. https://doi.org/10.1007/BF01025868.
Friedman, H. P., and J. Rubin. 1967. “On some invariant criteria for grouping data.” J. Am. Stat. Assoc. 62 (320): 1159–1178. https://doi.org/10.1080/01621459.1967.10500923.
Fytsilis, A. L., A. Prokos, K. D. Koutroumbas, D. Michail, and C. C. Kontoes. 2016. “A methodology for near real-time change detection between unmanned aerial vehicle and wide area satellite images.” ISPRS J. Photogramm. Remote Sens. 119 (Sep): 165–186. https://doi.org/10.1016/j.isprsjprs.2016.06.001.
Gandomi, A. H., X.-S. Yang, and A. H. Alavi. 2013. “Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems.” Eng. Comput. 29 (1): 17–35. https://doi.org/10.1007/s00366-011-0241-y.
Girardeau-Montaut, D. 2015. “CloudCompare version 2.6.1 user manual.” Accessed July 17, 2021. https://www.danielgm.net/cc/doc/qCC/CloudCompare%20v2.6.1%20-%20User%20manual.pdf.
Gong, M., J. Zhao, J. Liu, Q. Miao, and L. Jiao. 2015. “Change detection in synthetic aperture radar images based on deep neural networks.” IEEE Trans. Neural Networks Learn. Syst. 27 (1): 125–138. https://doi.org/10.1109/TNNLS.2015.2435783.
Gottfried, G. 1998. European macroseismic scale 1998. Luxembourg, UK: European Seismological Commission.
Gstaiger, V., J. Tian, R. Kiefl, and F. Kurz. 2018. “2D vs. 3D change detection using aerial imagery to support crisis management of large-scale events.” Remote Sens. 10 (12): 2054. https://doi.org/10.3390/rs10122054.
Hartigan, J. A., and M. A. Wong. 1979. “Algorithm AS 136: A k-means clustering algorithm.” J. R. Stat. Soc. C 28 (1): 100–108. https://doi.org/10.2307/2346830.
Hou, B., Y. Wang, and Q. Liu. 2016. “A saliency guided semi-supervised building change detection method for high resolution remote sensing images.” Sensors 16 (9): 1377. https://doi.org/10.3390/s16091377.
Hu, F., G.-S. Xia, J. Hu, and L. Zhang. 2015. “Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery.” Remote Sens. 7 (11): 14680–14707. https://doi.org/10.3390/rs71114680.
Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanley. 2013. “Change detection from remotely sensed images: From pixel-based to object-based approaches.” ISPRS J. Photogramm. Remote Sens. 80 (Jun): 91–106. https://doi.org/10.1016/j.isprsjprs.2013.03.006.
Ji, M., L. Liu, R. Zhang, and M. F. Buchroithner. 2020. “Discrimination of earthquake-induced building destruction from space using a pretrained CNN model.” Appl. Sci. 10 (2): 602. https://doi.org/10.3390/app10020602.
Kerle, N., F. Nex, M. Gerke, D. Duarte, and A. Vetrivel. 2020. “UAV-based structural damage mapping: A review.” ISPRS Int. J. Geo-Unformation 9 (1): 14. https://doi.org/10.3390/ijgi9010014.
Khodaverdi, N., H. Rastiveis, and A. Jouybari. 2019. “Combination of post-earthquake lidar data and satellite imagery for buildings damage detection.” Earth Obs. Geomatics Eng. 3 (1): 12–20. https://doi.org/10.22059/eoge.2019.278307.1046.
Kim, M.-K., H. Sohn, and C.-C. Chang. 2015. “Localization and quantification of concrete spalling defects using terrestrial laser scanning.” J. Comput. Civ. Eng. 29 (6): 04014086. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000415.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet classification with deep convolutional neural networks.” Adv. Neural Inf. Process. Syst. 25: 1097–1105.
Lebedev, M., Y. V. Vizilter, O. Vygolov, V. Knyaz, and A. Y. Rubis. 2018. “Change detection in remote sensing images using conditional adversarial networks.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 42 (2): 565–571. https://doi.org/10.5194/isprs-archives-XLII-2-565-2018.
Leichtle, T., C. Geiß, M. Wurm, T. Lakes, and H. Taubenböck. 2017. “Unsupervised change detection in VHR remote sensing imagery—An object-based clustering approach in a dynamic urban environment.” Int. J. Appl. Earth Obs. Geoinf. 54 (Feb): 15–27. https://doi.org/10.1016/j.jag.2016.08.010.
Li, Q., L. Gong, and J. Zhang. 2019. “A correlation change detection method integrating PCA and multi-texture features of SAR image for building damage detection.” Eur. J. Remote Sens. 52 (1): 435–447. https://doi.org/10.1080/22797254.2019.1630322.
Lin, T.-Y., M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. 2014. “Microsoft coco: Common objects in context.” In Computer vision: ECCV 2014, 740–755. Berlin: Springer. https://doi.org/10.1007/978-3-319-10602-1_48.
Liu, H., M. Yang, J. Chen, J. Hou, and M. Deng. 2018. “Line-constrained shape feature for building change detection in VHR remote sensing imagery.” ISPRS Int. J. Geo-Inf. 7 (10): 410. https://doi.org/10.3390/ijgi7100410.
Liu, W., and S.-E. Chen. 2013. “Reliability analysis of bridge evaluations based on 3d light detection and ranging data.” Struct. Control Health Monit. 20 (12): 1397–1409. https://doi.org/10.1002/stc.1533.
Maas, H.-G., and G. Vosselman. 1999. “Two algorithms for extracting building models from raw laser altimetry data.” ISPRS J. Photogramm. Remote Sens. 54 (2–3): 153–163. https://doi.org/10.1016/S0924-2716(99)00004-0.
Maltezos, E., and C. Ioannidis. 2015. “Automatic detection of building points from lidar and dense image matching point clouds.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2: 33–40. https://doi.org/10.5194/isprsannals-II-3-W5-33-2015.
Matin, S. S., and B. Pradhan. 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.
Matthews, B. W. 1975. “Comparison of the predicted and observed secondary structure of t4 phage lysozyme.” Biochim. Biophys. Acta Protein Struct. 405 (2): 442–451. https://doi.org/10.1016/0005-2795(75)90109-9.
Milligan, G. W., and M. C. Cooper. 1985. “An examination of procedures for determining the number of clusters in a data set.” Psychometrika 50 (2): 159–179. https://doi.org/10.1007/BF02294245.
Mohammadi, M. E., R. L. Wood, and C. E. Wittich. 2019. “Non-temporal point cloud analysis for surface damage in civil structures.” ISPRS Int. J. Geo-Inf. 8 (12): 527. https://doi.org/10.3390/ijgi8120527.
Molino, L. N., Jr. 2006. “Appendix A: Incident command post systems position description checklists.” In Emergency incident management systems, 233–275. Hoboken, NJ: Wiley.
Mostafa, K., and T. Hegazy. 2021. “Review of image-based analysis and applications in construction.” Autom. Constr. 122 (Feb): 103516. https://doi.org/10.1016/j.autcon.2020.103516.
Murphy, R. R., J. Kravitz, S. L. Stover, and R. Shoureshi. 2009. “Mobile robots in mine rescue and recovery.” IEEE Robot. Autom. Mag. 16 (2): 91–103. https://doi.org/10.1109/MRA.2009.932521.
Narváez, E. A. L., and N. E. L. Narváez. 2006. “Point cloud denoising using robust principal component analysis.” In Proc., 1st Int. Conf. on Computer Graphics Theory and Applications—GRAPP, 51–58. Setúbal, Portugal: Science and Technology Publications. https://doi.org/10.5220/0001358900510058.
Nex, F., D. Duarte, F. G. Tonolo, and N. Kerle. 2019. “Structural building damage detection with deep learning: Assessment of a state-of-the-art CNN in operational conditions.” Remote Sens. 11 (23): 2765. https://doi.org/10.3390/rs11232765.
Nogueira, K., O. A. Penatti, and J. A. Dos Santos. 2017. “Towards better exploiting convolutional neural networks for remote sensing scene classification.” Pattern Recognit. 61: 539–556. https://doi.org/10.1016/j.patcog.2016.07.001.
Novikov, G., A. Trekin, G. Potapov, V. Ignatiev, and E. Burnaev. 2018. “Satellite imagery analysis for operational damage assessment in emergency situations.” In Proc., Int. Conf. on Business Information Systems, 347–358. New York: Springer.
Olsen, M. J. 2015. “In situ change analysis and monitoring through terrestrial laser scanning.” J. Comput. Civ. Eng. 29 (2): 04014040. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000328.
Pang, S., X. Hu, Z. Cai, J. Gong, and M. Zhang. 2018. “Building change detection from bi-temporal dense-matching point clouds and aerial images.” Sensors 18 (4): 966. https://doi.org/10.3390/s18040966.
Pang, S., X. Hu, Z. Wang, and Y. Lu. 2014. “Object-based analysis of airborne lidar data for building change detection.” Remote Sens. 6 (11): 10733–10749. https://doi.org/10.3390/rs61110733.
Pauly, M., M. Gross, and L. P. Kobbelt. 2002. “Efficient simplification of point-sampled surfaces.” In Proc., IEEE Visualization, 2002. VIS 2002, 163–170. New York: IEEE.
Penatti, O. A., K. Nogueira, and J. A. Dos Santos. 2015. “Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 44–51.
Powers, D. M. 2020. “Evaluation: From precision, recall and f-measure to roc, informedness, markedness and correlation.” Preprints, submitted October 11, 2020. http://arxiv.org/abs/2010.16061.
Rezatofighi, H., N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese. 2019. “Generalized intersection over union: A metric and a loss for bounding box regression.” In Proc., IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 658–666. New York: IEEE.
Ross, T., H. Sorensen, S. Savage, and J. Carson. 1990. “Daps: Expert system for structural damage assessment.” J. Comput. Civ. Eng. 4 (4): 327–348. https://doi.org/10.1061/(ASCE)0887-3801(1990)4:4(327).
Rousseeuw, P. J. 1987. “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.” J. Comput. Appl. Math. 20 (Nov): 53–65. https://doi.org/10.1016/0377-0427(87)90125-7.
Rusu, R. B., and S. Cousins. 2011. “3D is here: Point cloud library (PCL).” In Proc., IEEE Int. Conf. on Robotics and Automation (ICRA). Shanghai, China: IEEE.
Sokolova, M., N. Japkowicz, and S. Szpakowicz. 2006. “Beyond accuracy, f-score and roc: A family of discriminant measures for performance evaluation.” In Proc., Australasian Joint Conf. on Artificial Intelligence, 1015–1021. Menlo Park, CA: Association for the Advancement of Artificial Intelligence.
Stal, C., F. Tack, P. De Maeyer, A. De Wulf, and R. Goossens. 2013. “Airborne photogrammetry and lidar for dsm extraction and 3d change detection over an urban area–a comparative study.” Int. J. Remote Sens. 34 (4): 1087–1110. https://doi.org/10.1080/01431161.2012.717183.
Sui, H., J. Tu, Z. Song, G. Chen, and Q. Li. 2014. “A novel 3d building damage detection method using multiple overlapping uav images.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XL-7 (7): 173. https://doi.org/10.5194/isprsarchives-XL-7-173-2014.
Teza, G., A. Galgaro, and F. Moro. 2009. “Contactless recognition of concrete surface damage from laser scanning and curvature computation.” NDT E. Int. 42 (4): 240–249. https://doi.org/10.1016/j.ndteint.2008.10.009.
Torres-González, M., A. Prieto, F. Alejandre, and F. Blasco-López. 2021. “Digital management focused on the preventive maintenance of world heritage sites.” Autom. Constr. 129 (Sep): 103813. https://doi.org/10.1016/j.autcon.2021.103813.
Tran, T. H. G., C. Ressl, and N. Pfeifer. 2018. “Integrated change detection and classification in urban areas based on airborne laser scanning point clouds.” Sensors 18 (2): 448. https://doi.org/10.3390/s18020448.
Tu, J., H. Sui, W. Feng, and Z. Song. 2016. “Automatic building damage detection method using high-resolution remote sensing images and 3d gis model.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 3 (8): 43–50. https://doi.org/10.5194/isprs-annals-III-8-43-2016.
Vetrivel, A., M. Gerke, N. Kerle, F. Nex, and G. Vosselman. 2018. “Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning.” ISPRS J. Photogramm. Remote Sens. 140 (Jun): 45–59. https://doi.org/10.1016/j.isprsjprs.2017.03.001.
Vetrivel, A., M. Gerke, N. Kerle, and G. Vosselman. 2015a. “Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images.” ISPRS J. Photogramm. Remote Sens. 105 (Jul): 61–78. https://doi.org/10.1016/j.isprsjprs.2015.03.016.
Vetrivel, A., M. Gerke, N. Kerle, and G. Vosselman. 2015b. “Segmentation of UAV-based images incorporating 3D point cloud information.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XL-3/W2: 261–268. https://doi.org/10.5194/isprsarchives-XL-3-W2-261-2015.
Voigt, S., et al. 2016. “Global trends in satellite-based emergency mapping.” Science 353 (6296): 247–252. https://doi.org/10.1126/science.aad8728.
Wang, J., C. Luo, H. Huang, H. Zhao, and S. Wang. 2017. “Transferring pre-trained deep cnns for remote scene classification with general features learned from linear PCA network.” Remote Sens. 9 (3): 225. https://doi.org/10.3390/rs9030225.
Wang, L., J. Dang, X. Wang, and A. Shrestha. 2021. “Waveform-based fracture identification of steel beam ends using convolutional neural networks.” Struct. Control Health Monit. 28 (9): e2777. https://doi.org/10.1002/stc.2777.
Weinmann, M., B. Jutzi, and C. Mallet. 2014. “Semantic 3d scene interpretation: A framework combining optimal neighborhood size selection with relevant features.” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. II-3 (3): 181. https://doi.org/10.5194/isprsannals-II-3-181-2014.
Wu, C., F. Zhang, J. Xia, Y. Xu, G. Li, J. Xie, Z. Du, and R. Liu. 2021. “Building damage detection using u-net with attention mechanism from pre-and post-disaster remote sensing datasets.” Remote Sens. 13 (5): 905. https://doi.org/10.3390/rs13050905.
Yang, X.-S. 2010. “Cuckoo search (CS) algorithm.” Accessed August 19, 2020. https://ww2.mathworks.cn/matlabcentral/fileexchange/29809-cuckoo-search-cs-algorithm.
Yang, X.-S., and S. Deb. 2009. “Cuckoo search via lévy flights.” In Proc., 2009 World congress on nature & biologically inspired computing (NaBIC), 210–214. New York: IEEE.
Zhang, Y., and L. Bai. 2015. “Rapid structural condition assessment using radio frequency identification (RFID) based wireless strain sensor.” Autom. Constr. 54 (Jun): 1–11. https://doi.org/10.1016/j.autcon.2015.02.013.
Zhang, Z., G. Vosselman, M. Gerke, D. Tuia, and M. Yang. 2018. “Change detection between multimodal remote sensing data using Siamese CNN.” Preprints, submitted July 5, 2018. http://arxiv.org/abs/1807.09562.
Zhao, R., M. Pang, and M. Wei. 2018. “Accurate extraction of building roofs from airborne light detection and ranging point clouds using a coarse-to-fine approach.” J. Appl. Remote Sens. 12 (2): 026011. https://doi.org/10.1117/1.JRS.12.026011.
Zhou, B., H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba. 2017. “Scene parsing through ade20k dataset.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 633–641. New York: IEEE.
Zhu, Z. 2011. “Column recogniton and defects/damage properties retrieval for rapid infrastructure assessment and rehabilitation using machine vision.” Ph.D. thesis, School of Civil and Environmental Engineering, Georgia Institute of Technology.
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Journal of Computing in Civil Engineering
Volume 36 • Issue 5 • September 2022
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© 2022 American Society of Civil Engineers.
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Received: Jan 14, 2021
Accepted: Apr 5, 2022
Published online: Jul 12, 2022
Published in print: Sep 1, 2022
Discussion open until: Dec 12, 2022
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