Abstract
A quick and accurate post-earthquake safety assessment is critical for emergency management and reconstruction. Accurate knowledge of the scenario enables optimal use of human and economic resources. In Earth-quake prone countries, National Emergency Management Agency defines standard forms to collect information during inspections (e.g., Italian AeDES form, New Zealand Earthquake rapid assessment form, American ATC-20 Rapid evaluation safety assessment form). Assisting the technicians in the compilation of the cards and assessing their correctness guarantees a faithful reconstruction of the reality. We propose a Deep Learning-based tool that can recognize, localize, and quantify damages starting from a set of photos of the building to be assessed. The analysis results are expressed in terms of a Damage Assessment Matrix, which allows a quick association to the safety form.
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References
Italian Civil Protection Department, AeDES form. www.protezionecivile.gov.it/en/normativa/dpcm-dell8-luglio-2014. Accessed 20 Mar 2022
Dolce, M., Speranza, E., Dalla Negra, R., Zuppiroli, M., Bocchi, F.: Constructive features and seismic vulnerability of historic centres through the rapid assessment of historic building stocks. The experience of Ferrara, Italy. In: Toniolo, L., Boriani, M., Guidi, G. (eds.) Built Heritage: Monitoring Conservation Management. RD, pp. 165–175. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-08533-3_14
Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley, Hoboken (2012)
Mondal, T.G., et al.: Deep learning-based multi-class damage detection for autonomous post-disaster reconnaissance. Struct. Control Health Monit. 27(4), e2507 (2020)
Mariniello, G., et al.: Layout-aware extreme learning machine to detect tendon malfunctions in prestressed concrete bridges using stress data. Autom. Constr. 132, 103976 (2021)
Mariniello, G., et al.: Structural damage detection and localization using decision tree ensemble and vibration data. Comput. Aided Civ. Infrast. Eng. 36, 1129–1149 (2020)
Abdeljaber, O., et al.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154–170 (2017)
Huseynov, F., et al.: Bridge damage detection using rotation measurements-experimental validation. Mech. Syst. Signal Process. 135, 106380 (2020)
Glisic, B., et al.: Damage detection and characterization using long-gauge and distributed fiber optic sensors. Opt. Eng. 52(8), 087101 (2013)
Bocherens, E., et al.: Damage detection in a radome sandwich material with embedded fiber optic sensors. Smart Mater. Struct. 9(3), 310 (2000)
Dworakowski, Z., et al.: Vision-based algorithms for damage detection and localization in structural health monitoring. Struct. Control Health Monit. 23(1), 35–50 (2016)
Patterson, B., et al.: Deep learning for automated image classification of seismic damage to built infrastructure. In: Eleventh US National Conference on Earthquake Engineering (2018)
Koch, C., et al.: Achievements and challenges in machine vision based inspection of large concrete structures. Adv. Struct. Eng. 17(3), pp. 303–318 (2014)
Yuan, C., et al.: A novel intelligent inspection robot with deep stereo vision for three-dimensional concrete damage detection and quantification. Struct. Health Monit., 14759217211010238 (2021)
Brencich, A., Gambarotta, L ., Lagomarsino, S.: A macroelement approach to the three-dimensional seismic analysis of masonry buildings. In: 11th European Conference on Earthquake Engineering, vol. 90, pp. 1–10 (1998)
Li, S., Zhao, X., Zhou, G.: Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network. Comput. Aided Civ. Infrastruct. Eng. 34(7), 616–634 (2019)
Dorafshan, S., Thomas, R.J., Maguire, M.: Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr. Build. Mater. 186, 1031–1045 (2018)
Özgenel, Ç.F., Gönenç Sorguç, A.: Performance comparison of pretrained convolutional neural networks on crack detection in buildings. In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, vol. 35, pp. 1–8. IAARC Publications (2018)
Deng, J.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Liu, S., Deng, W.: Very deep convolutional neural network based image classification using small training sample size. In: 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 730–734 (2015)
Huang, G., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472. IEEE (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
Smith, L.N.: A disciplined approach to neural network hyper-parameters: Part 1-learning rate, batch size, momentum, and weight decay. arXiv preprint arXiv:1803.09820 (2018)
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Giacco, G., Mariniello, G., Marrone, S., Asprone, D., Sansone, C. (2022). Toward a System for Post-Earthquake Safety Evaluation of Masonry Buildings. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13232. Springer, Cham. https://doi.org/10.1007/978-3-031-06430-2_26
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