Abstract
With the overwhelming number of older reinforced concrete buildings that need to be assessed for seismic vulnerability in a city, local governments face the question of how to assess their building inventory. By leveraging engineering drawings that are stored in a digital format, a well-established method for classification reinforced concrete buildings with respect to seismic vulnerability, and machine learning techniques, we have developed a technique to automatically extract quantitative information from the drawings to classify vulnerability. Using this technique, stakeholders will be able to rapidly classify buildings according to their seismic vulnerability and have access to information they need to prioritize a large building inventory. The approach has the potential to have significant impact on our ability to rapidly make decisions related to retrofit and improvements in our communities. In the Los Angeles County alone it is estimated that several thousand buildings of this type exist. The Hassan index is adopted here as the method for automation due to its simple application during the classification of the vulnerable reinforced concrete buildings. This paper will present the technique used for automating information extraction to compute the Hassan index for a large building inventory.
Article PDF
Similar content being viewed by others
References
Abraham N and Khan NM (2019), “A Novel Focal Tversky Loss Function with Improved Attention U-Net for Lesion Segmentation,” 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 683–687.
Balbirnie K, Chandramohan R, Pujol S and Wright J (2021), “Identifying Vulnerable Reinforced Concrete Buildings in Wellington Using a Simple Assessment Methodology,” Proceedings of the 2021 New Zealand Society for Earthquake Engineering Annual Technical Conference.13.237.132.70/handle/nzsee/2368
Bradski G and Kaehler A (2000), “OpenCV,” Dr. Dobb’s Journal of Software Tools, 3: 2.
Brzev S, Pandey B, Maharjan DK and Ventura C (2017), “Seismic Vulnerability Assessment of Low-Rise Reinforced Concrete Buildings Affected by the 2015 Gorkha, Nepal, Earthquake,” Earthquake Spectra, 33(S1): 275–298.
Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M and Kalinin AA (2020), “Albumentations: Fast and Flexible Image Augmentations,” Information, 11(2): 125. doi: https://doi.org/10.3390/info11020125
Chungwook S, Laughery L, Chiou TC and Weng P (2018), 2017 Pohang Earthquake.https://datacenterhub.org/deedsdv/publications/view/296
Code P (2005), Eurocode 2: Design of Concrete Structures-Part 1–1: General Rules and Rules for Buildings, British Standard Institution, London, UK.
Deng J, Dong W, Socher R, Li LJ, Li K and Li FF (2009), “Imagenet: A Large-Scale Hierarchical Image Database,” 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255.
Gehan MA, Fahlgren N, Abbasi A, Berry JC and Sax T (2017), “PlantCV v2: Image Analysis Software for High-Throughput Plant Phenotyping,” PeerJ, 5: e4088.
Gimenez J, Hippolyte JL, Robert S, Suard F and Zreik K (2015), “Reconstruction of 3D Building Information Models from 2D Scanned Plans,” Journal of Building Engineering, 2: 24–35.
Hassan AF and Sozen MA (1997), “Seismic Vulnerability Assessment of Low-Rise Buildings in Regions with Infrequent Earthquakes,” ACI Struc, 94(1): 31–39.
Huang B, Reichman D, Collins LM, Bradbury K and Malof JM (2018), “Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations,” arXiv preprint arXiv, 1805.12219.
Jury R and Ferner H (2015), “Seismic Risk Management in the New Zealand Context,” Improving the Seismic Performance of Existing Buildings and Other Structures, pp. 728–740.
Kalinina D, Ingilevich V, Lantseva A and Ivanov S (2018), “Computing Concave Hull with Closed Curve Smoothing: Performance, Concaveness Measure and Applications,” Procedia Comput Sci, 136: 479–488. doi: https://doi.org/10.1016/j.procs.2018.08.258
Kalinina D, Ingilevich V, Lantseva A and Ivanov S (2018), “Computing Concave Hull with Closed Curve Smoothing: Performance, Concaveness Measure and Applications,” Procedia Comput Sci, 136: 479–488.
Kannala J (2019), “CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis,” Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, 11482: 28.
Kassem MM, Nazri FM and Farsangi EN (2020), “The Seismic Vulnerability Assessment Methodologies: A State-of-the-Art Review,” Ain Shams Engineering Journal, 11(4): 849–864.
Kestur R, Meduri A and Narasipura O (2019), “MangoNet: A Deep Semantic Segmentation Architecture for a Method to Detect and Count Mangoes in an Open Orchard,” Eng Appl Artif Intell, 77: 59–69.
Kim S, Park S, Kim H and Yu K (2021), “Deep Floor Plan Analysis for Complicated Drawings Based on Style Transfer,” Journal of Computing in Civil Engineering, 35(2): 04020066.
Labelbox (2022), https://labelbox.com.
Lenjani A, Dyke SJ, Bilionis L, Yeum CM, Kamiya K, Choi J, Liu XY and Chowdhury AG (2020), “Towards Fully Automated Post-Event Data Collection and Analysis: Pre-Event and Post-Event Information Fusion,” Eng Struct, 208: 109884.
Li H, Zhu F and Qiu J (2018), “CG-DIQA: No-Reference Document Image Quality Assessment Based on Character Gradient,” 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3622–3626. doi: https://doi.org/10.1109/ICPR.2018.8545433
Liu C, Schwing AG, Kundu K, Urtasun R and Fidler S (2015), “Rent3d: Floor-Plan Priors for Monocular Layout Estimation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3413–3421.
Liu X, Iturburu L, Dyke SJ, Lenjani A, Ramirez J and Zhang X (2022), “Information Fusion to Automatically Classify Post-Event Building Damage State,” Eng Struct, 253: 113765.
Loos S, Lallemant D, Baker JW, Mccaughey J and Singh R (2020), “G-DIF: A Geospatial Data Integration Framework to Rapidly Estimate Post-Earthquake Damage,” Earthquake Spectra, 36(4): 1695–1718.
Lu Q and Lee S (2017), “A Semi-Automatic Approach to Detect Structural Components from CAD Drawings for Constructing As-Is BIM Objects,” Computing in Civil Engineering, pp. 84–91.
Lv X, Zhao S, Yu X and Zhao B (2021), “Residential Floor Plan Recognition and Reconstruction,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16717–16726.
Marquis F, Kim JJ, Elwood KJ and Chang SE (2017), “Understanding Post-Earthquake Decisions on Multi-Storey Concrete Buildings in Christchurch, New Zealand,” Bulletin of Earthquake Engineering, 15(2): 731–758.
Messaoud NJ, Mansoura A, Aissi M, Ayaria R, Frih M, Abdallah AB and Bedoui MH (2022), “Automated Segmentation of Multiple Sclerosis Lesions Based on Convolutional Neural Networks,” Comput Methods Biomech Biomed Eng Imaging Vis, pp. 1–19.
Min XK, Gu K, Zhai GT, Yang XK, Zhang WJ, Le CP and Chen CW (2021), “Screen Content Quality Assessment: Overview, Benchmark, and Beyond,” ACM Computing Surveys (CSUR), 54(9): 1–36.
Müller D, Rey IS and Kramer K (2020), “Automated Chest CT Image Segmentation of Covid-19 Lung Infection Based on 3D U-Net,” arXiv preprint arXiv, 2007.04774.
Niblack W (1985), An Introduction to Digital Image Processing, Strandberg Publishing Company, USA.
Pedregosa F, Varoquaux G, Gramfort A, et al. (2011), “Scikit-Learn: Machine Learning in Python,” the Journal of Machine Learning Research, 12: 2825–2830.
Pujol S, Laughery L, Puranam A, Hesam P, Cheng LH, Lund A and Irfanoglu A (2020), “Evaluation of Seismic Vulnerability Indices for Low-rise Reinforced Concrete Buildings Including Data from the 6 February 2016 Taiwan Earthquake,” Journal of Disaster Research, 15(1): 9–19.
Ronneberger O, Fischer P and Brox T (2015), “U-net: Convolutional Networks for Biomedical Image Segmentation,” International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241.
Rousseeuw PJ (1987), “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis,” J Comput Appl Math, 20: 53–65.
Sauvola J and Pietikäinen M (2000), “Adaptive Document Image Binarization,” Pattern Recognit, 33(2): 225–236.
So C, Baciu G and Sun H (1998), “Reconstruction of 3D Virtual Buildings from 2D Architectural Floor Plans,” Proceedings of the ACM symposium on Virtual Reality Software and Technology, pp. 17–23.
Szczyrba J, Zhang Y, Pamukcu D, Eroglu DI and Weiss R (2021), “Quantifying the Role of Vulnerability in Hurricane Damage via a Machine Learning Case Study,” Nat Hazards Rev, 22(3): 04021028.
Tam R and Heidrich W (2003), “Shape Simplification Based on the Medial Axis Transform,” IEEE Visualization, VIS 2003, pp. 481–488.
van der Walt S, Schnberger JL, Juan NI and Boulogne F (2014), “Scikit-Image: Image Processing in Python,” PeerJ, 2: e453.
Vassilvitskii S and Arthur D (2006), “K-Means++: The Advantages of Careful Seeding,” Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035.
Virtanen P, Gommers R, Oliphant TE, et al. (2020), “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,” Nat Methods, 17(3): 261–272.
Wang C, Yu Q, McKenna F, Cetiner B, Yu SX, Taciroglu E and Law KH (2019), NHERI-SimCenter/BRAILS: vl.0.1.https://github.com/NHERI-SimCenter/BRAILS
Wang CF, Yu Q, Law KH, McKenna F, Yu SX, Taciroglu E, Zsarnóczay A, Elhaddad W and Cetiner B (2021), “Machine Learning-Based Regional Scale Intelligent Modeling of Building Information for Natural Hazard Risk Management,” Autom Constr, 122: 103474. doi: https://doi.org/10.1016/j.autcon.2020.103474
Xu J, Ye P, Li Q, Liu Y and Doermann D (2016), “No-Reference Document Image Quality Assessment Based on High order Image Statistics,” 2016 IEEE International Conference on Image Processing (ICIP), pp. 3289–3293.
Yeum CM and Dyke SJ (2016), “Big Visual Data Analytics for Damage Classification in Civil Engineering,” Transforming the Future of Infrastructure Through Smarter Information: Proceedings of the International Conference on Smart Infrastructure and Construction, pp. 569–574.
Yeum CM, Dyke SJ and Ramirez J (2018), “Visual Data Classification in Post-Event Building Reconnaissance,” Eng Struct, 155: 16–24.
Yeung M, Sala E, Schönlieb CB and Rundo L (2021), “Unified Focal Loss: Generalising Dice and Cross Entropy-Based Losses to Handle Class Imbalanced Medical Image Segmentation,” Computerized Medical Imaging and Graphics, 102026.
Yu Q, Wang CF, Cetiner B, Yu SX, McKenna F, Taciroglu E and Law KH (2019), “Building Information Modeling and Classification by Visual Learning at a City scale,” ArXiv preprint arXiv, 1910.06391.
Zhang Y, Ca J and Cai H (2020), “CNN-Based Symbol Recognition in Piping Drawings,” Construction Research Congress 2020: Computer Applications, pp. 576–584.
Zhao Y, Deng X and Lai H (2020), “A YOLO-Based Method to Recognize Structural Components from 2D Drawings,” Construction Research Congress 2020: Computer Applications, pp. 753–762.
Zhao Y, Deng X and Lai H (2021), “Reconstructing BIM from 2D Structural Drawings for Existing Buildings,” Autom Constr, 128: 103750.
Zheng L, Shen L, Chen J, An P and Luo J (2019), “No-Reference Quality Assessment for Screen Content Images Based on Hybrid Region Features Fusion,” IEEE Trans Multimedia, 21(8): 2057–2070.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by: US National Science Foundation under Grant No. NSF- OAC-1835473
Rights and permissions
About this article
Cite this article
Iturburu, L., Kwannandar, J., Dyke, S.J. et al. Towards rapid and automated vulnerability classification of concrete buildings. Earthq. Eng. Eng. Vib. 22, 309–332 (2023). https://doi.org/10.1007/s11803-023-2171-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11803-023-2171-2