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http://dx.doi.org/10.12989/sss.2022.30.2.145

Structural live load surveys by deep learning  

Li, Yang (College of Civil Engineering, Tongji University)
Chen, Jun (College of Civil Engineering, Tongji University)
Publication Information
Smart Structures and Systems / v.30, no.2, 2022 , pp. 145-157 More about this Journal
Abstract
The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.
Keywords
big data; deep learning; live load survey; web crawler; YOLOv3;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Choi, E.C.C. (1991), "Extraordinary live load in office buildings", J. Struct. Eng., 117(11), 3216-3227. https://doi.org/10.1061/(ASCE)0733-9445(1991)117:11(3216)   DOI
2 Ge, S.J., Chen, H., Sun, Z.S. and Li, J.B. (2008), "Survey and statistic of floor live load of residential building in central plains region", Build Struct., (07), 125-128. https://doi.org/10.19701/j.jzjg.2008.07.038   DOI
3 He, K., Zhang, X., Ren, S. and Sun, J. (2016), "Deep residual learning for image recognition", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, July.
4 Bahdanau, D., Cho, K. and Bengio, Y. (2014), "Neural machine translation by jointly learning to align and translate", Proceedings of International Conference on Learning Representations, San Diego, CA, USA, May.
5 Andam, K.A. (1986), "Floor live loads for office buildings", Build. Environ., 21(3-4), 211-219. https://doi.org/10.1016/0360-1323(86)90032-6   DOI
6 Asantey, S.B.A. and Andam, K.A. (1996), "Factory and warehouse live load survey", Build. Environ., 31(2), 167-178. https://doi.org/10.1016/0360-1323(95)00035-6   DOI
7 Cha, Y.-J., Choi, W. and Buyukozturk, O. (2017), "Deep learning-based crack damage detection using convolutional neural networks", Comput.-Aid. Civil Infrastruct. Eng., 32(5), 361-378. https://doi.org/10.1111/mice.12263   DOI
8 Choi, E.C.C. (1990), "Live load for office buildings: effect of occupancy and code comparison", J. Struct. Eng., 116(11), 3162-3174. https://doi.org/10.1061/(ASCE)0733-9445(1990)116:11(3162)   DOI
9 Guo, A., Jiang, A., Lin, J. and Li, X. (2019), "Data mining algorithms for bridge health monitoring: kohonen clustering and lstm prediction approaches", J. Supercomput., 76(2), 932-947. https://doi.org/10.1007/s11227-019-03045-8   DOI
10 Duan, Y., Chen, Q., Zhang, H., Yun, C., Wu, S. and Zhu, Q. (2019), "CNN-based damage identification method of tied-arch bridge using spatial-spectral information", Smart Struct. Syst., Int. J., 23(5), 507-520. https://doi.org/10.12989/sss.2019.23.5.507   DOI
11 Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P. and Kingsbury, B. (2012), "Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups", IEEE Signal Process. Mag., 29(6), 82-97. https://doi.org/10.1109/msp.2012.2205597   DOI
12 Maas, A.L., Hannun, A.Y. and Ng, A.Y. (2013), "Rectifier nonlinearities improve neural network acoustic models", Proceedings of the 30th Workshop on Deep Learning for Audio, Speech and Language Processing, Atlanta, GA, USA, June.
13 Jain, V. and Learned-Miller, E. (2010), "Fddb: a benchmark for face detection in unconstrained settings", UMass Amherst Technical Report; University of Massachusetts Amherst.
14 Kingma, D.P. and Ba, J. (2015), "Adam: a method for stochastic optimization", Proceedings of International Conference on Learning Representations 2015, San Diego, CA, USA, May.
15 Kumar, M., Bhatia, R. and Rattan, D. (2017), "A survey of web crawlers for information retrieval", Wiley Interdiscip. Rev.-Data Mining Knowl. Discov., 7(6), e1218. https://doi.org/10.1002/widm.1218   DOI
16 Park, S., Jeong, H., Min, H., Lee, H. and Lee, S. (2018), "Waveletlike convolutional neural network structure for time-series data classification", Smart Struct. Syst., Int. J., 22(2), 175-183. https://doi.org/10.12989/sss.2018.22.2.175   DOI
17 Xie, P., Zhou, A. and Chai, B. (2019), "The application of long short-term memory (LSTM) method on displacement prediction of multifactor-induced landslides", IEEE Access, 7, 54305-54311. https://doi.org/10.1109/access.2019.2912419   DOI
18 Luo, L., Feng, M.Q., Wu, J. and Leung, R.Y. (2019), "Autonomous pothole detection using deep region-based convolutional neural network with cloud computing", Smart Struct. Syst., Int. J.., 24(6), 745-757. https://doi.org/10.12989/sss.2019.24.6.745   DOI
19 Wang, J. and Guo, Y. (2012), "Scrapy-based crawling and user-behavior characteristics analysis on taobao", Proceedings of 2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Sanya, China, October.
20 Wu, X.Q., Yao, J.T. and Liu, Y.J. (2012), "Statistical analysis of live load on residence floor and analysis of residence floor reliability", Eng. Mech., 29(3), 90-94.
21 Ren, S., He, K., Girshick, R. and Sun, J. (2016), "Faster r-cnn: towards real-time object detection with region proposal networks", IEEE Trans. Pattern. Anal. Mach. Intell., 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031   DOI
22 Ni, F., Zhang, J. and Chen, Z. (2018), "Pixel-level crack delineation in images with convolutional feature fusion", Struct. Control Health Monitor., 26(1), e2286. https://doi.org/10.1002/stc.2286   DOI
23 Xiong, J. and Chen, J. (2019), "A generative adversarial network model for simulating various types of human-induced loads", Int. J. Struct. Stab. Dyn., 19(08), 1950092. https://doi.org/10.1142/s0219455419500925   DOI
24 Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J. and Zisserman, A. (2010), "The pascal visual object classes (voc) challenge", Int. J. Comput. Vision, 88(2), 303-338. https://doi.org/10.1007/s11263-009-0275-4   DOI
25 Xu, Y., Bao, Y., Chen, J., Zuo, W. and Li, H. (2019), "Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images", Struct. Health Monitor., 18(3), 653-674. https://doi.org/10.1177/1475921718764873   DOI
26 Ioffe, S. and Szegedy, C. (2015), "Batch normalization: accelerating deep network training by reducing internal covariate shift", Proceedings of the 32nd International Conference on Machine Learning, Lille, France, July.
27 Jin, X. and Zhao, J. (2012), "Development of the design code for building structures in China", Struct. Eng. Int., 22(2), 195-201. https://doi.org/10.2749/101686612X13291382990886   DOI
28 Kaimal, J.C., Wyngaard, J.C., Izumi, Y. and Cote, O.R. (1972), "Spectral characteristics of surface-layer turbulence", Q. J. R. Meteorol. Soc., 98(417), 563-589. https://doi.org/10.1002/qj.49709841707   DOI
29 Kumar, S. (2002b), "Live loads in office buildings: point-in-time load intensity", Build. Environ., 37(1), 79-89. https://doi.org/10.1016/S0360-1323(00)00074-3   DOI
30 Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P. and Zitnick, C.L. (2014), "Microsoft coco: common objects in context", Proceedings of European Conference on Computer Vision, Zurich, Switzerland, September.
31 Graves, A., Mohamed, A. and Hinton, G. (2013), "Speech recognition with deep recurrent neural networks", Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, May.
32 Kumar, S. (2002a), "Live loads in office buildings: lifetime maximum load", Build. Environ., 37(1), 91-99. https://doi.org/10.1016/S0360-1323(00)00075-5   DOI
33 Redmon, J. and Farhadi, A. (2018), "YOLOv3: an incremental improvement", arXiv preprint arXiv:1804.02767.
34 Ruiz, S.E. and Sampayo-Trujillo, A. (1997), "Design live loads for classrooms in United States and Mexico", J. Struct. Eng., 123(12), 1652-1657. https://doi.org/10.1061/(ASCE)0733-9445(1997)123:12(1652)   DOI
35 Tang, Z., Chen, Z., Bao, Y. and Li, H. (2018), "Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring", Struct. Control Health Monitor., 26(1), e2296. https://doi.org/10.1002/stc.2296   DOI
36 Wang, D. and Li, J. (2012), "A random physical model of seismic ground motion field on local engineering site", Sci. China: Technol. Sci., 55(7), 2057-2065. https://doi.org/10.1007/s11431-012-4850-5   DOI
37 Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. and Kavukcuoglu, K. (2016), "Wavenet: a generative model for raw audio", arXiv preprint arXiv:1609.03499.
38 Redmon, J. and Farhadi, A. (2017), "YOLO9000: better, faster, stronger", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, July.