Collapse warning system using LSTM neural networks for construction disaster prevention in extreme wind weather
Abstract
Strong wind during extreme weather conditions (e.g., strong winds during typhoons) is one of the natural factors that cause the collapse of frame-type scaffolds used in façade work. This study developed an alert system for use in determining whether the scaffold structure could withstand the stress of the wind force. Conceptually, the scaffolds collapsed by the warning system developed in the study contains three modules. The first module involves the establishment of wind velocity prediction models. This study employed various deep learning and machine learning techniques, namely deep neural networks, long short-term memory neural networks, support vector regressions, random forest, and k-nearest neighbors. Then, the second module contains the analysis of wind force on the scaffolds. The third module involves the development of the scaffold collapse evaluation approach. The study area was Taichung City, Taiwan. This study collected meteorological data from the ground stations from 2012 to 2019. Results revealed that the system successfully predicted the possible collapse time for scaffolds within 1 to 6 h, and effectively issued a warning time. Overall, the warning system can provide practical warning information related to the destruction of scaffolds to construction teams in need of the information to reduce the damage risk.
Keyword : wind forecasting, machine learning, construction engineering, collapse warning, extreme weather
How to Cite
Wei, C.-C. (2021). Collapse warning system using LSTM neural networks for construction disaster prevention in extreme wind weather. Journal of Civil Engineering and Management, 27(4), 230-245. https://doi.org/10.3846/jcem.2021.14649
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Byeon, W., Liwicki, M., & Breuel, T. M. (2015). Scene analysis by mid-level attribute learning using 2D LSTM networks and an application to web-image tagging. Pattern Recognition Letters, 63, 23–29. https://doi.org/10.1016/j.patrec.2015.06.003
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Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9, 109. https://doi.org/10.3390/en9020109
Chen, J., Zeng, G., Zhou, W., Du, W., & Lu, K. (2018). Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy Conversion and Management, 165, 681–695. https://doi.org/10.1016/j.enconman.2018.03.098
Chen, K. Y., & Wang, C. H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 28, 215–226. https://doi.org/10.1016/j.tourman.2005.12.018
Cheng, C. C., Hsu, N. S., & Wei, C. C. (2008). Decision-tree analysis on optimal release of reservoir storage under typhoon warnings. Natural Hazards, 44, 65–84. https://doi.org/10.1007/s11069-007-9142-1
Chou, J. S., Truong, D. N., & Che, Y. (2020). Optimized multioutput machine learning system for engineering informatics in assessing natural hazards. Natural Hazards, 101, 727–754. https://doi.org/10.1007/s11069-020-03892-2
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent Neural networks on sequence modeling. In NIPS 2014 Deep Learning and Representation Learning Workshop. https://arxiv.org/abs/1412.3555v1
Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other Kernel-based learning methods. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511801389
Cutler, D., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forest for classification in ecology. Ecology, 88, 2783–2792. https://doi.org/10.1890/07-0539.1
Dongmei, H., Shiqing, H., Xuhui, H., & Xue, Z. (2017). Prediction of wind loads on high-rise building using a BP neural network combined with POD. Journal of Wind Engineering & Industrial Aerodynamics, 170, 1–17. https://doi.org/10.1016/j.jweia.2017.07.021
Du, J., & Xu, Y. (2017). Hierarchical deep neural network for multivariate regression. Pattern Recognition, 63, 149–157. https://doi.org/10.1016/j.patcog.2016.10.003
Fix, E., & Hodges, J. L. (1951). Discriminatory analysis, nonaparametric discrimination: Consistency properties (Technical Report 4). USAF School of Aviation Medicine, Randolph Field. https://doi.org/10.1037/e471672008-001
Glüge, S., Böck, R., Palm, G., & Wendemuth, A. (2014). Learning long-term dependencies in segmented-memory recurrent neural networks with backpropagation of error. Neurocomputing, 141, 54–64. https://doi.org/10.1016/j.neucom.2013.11.043
Graves, A. (2012). Supervised sequence labelling with recurrent neural networks (vol. 385). Springer. https://doi.org/10.1007/978-3-642-24797-2
Graves, A. (2013). Generating sequences with recurrent neural networks. https://arxiv.org/abs/1308.0850v5
Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18, 602–610. https://doi.org/10.1016/j.neunet.2005.06.042
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hu, Q., Zhang, R., & Zhou, Y. (2016). Transfer learning for shortterm wind speed prediction with deep neural networks. Renewable Energy, 85, 83–95. https://doi.org/10.1016/j.renene.2015.06.034
Huang, C. J., & Kuo, P. H. (2018). A short-term wind speed forecasting model by using artificial neural networks with stochastic optimization for renewable energy systems. Energies, 11, 2777. https://doi.org/10.3390/en11102777
Huang, X., Gao, L., Crosbie, R. S., Zhang, N., Fu, G., & Doble, R. (2019). Groundwater recharge prediction using linear regression, multi-layer perception network, and deep learning. Water, 11, 1879. https://doi.org/10.3390/w11091879
Huang, Y., Jin, L., Zhao, H., & Huang, X. (2018a). Fuzzy neural network and LLE Algorithm for forecasting precipitation in tropical cyclones: comparisons with interpolation method by ECMWF and stepwise regression method. Natural Hazards, 91, 201–220. https://doi.org/10.1007/s11069-017-3122-x
Huang, Y., Liu, S., & Yang, L. (2018b). Wind speed forecasting method using EEMD and the combination forecasting method based on GPR and LSTM. Sustainability, 10, 3693. https://doi.org/10.3390/su10103693
Kim, M., Park, M., Im, J., Park, S., & Lee, M. I. (2019). Machine learning approaches for detecting tropical cyclone formation using satellite data. Remote Sensing, 11, 1195. https://doi.org/10.3390/rs11101195
Kingma, D. P., & Ba, J. L. (2015). ADAM: A method for stochastic optimization. In International Conference on Learning Representations (ICLR 2015).
Liang, K. H., Yao, X., & Newton, C. S. (2001). Adapting self-adaptive parameters in evolutionary algorithms. Applied Intelligence, 15, 171–180. https://doi.org/10.1023/A:1011286929823
Lin, C. C., & Yen, C. (2016). Research on the safety performance influence factors and safety design key points of scaffolding (Report No. ILOSH104-S310). Institute of Labor, Occupational Safety and Health, Ministry of Labor, Taiwan (in Chinese).
Lin, C. C., & Yen, C. (2017). Study on wind accidents and wind loads of facade frame type scaffolds (Report No. ILOSH105S307). Institute of Labor, Occupational Safety and Health, Ministry of Labor, Taiwan (in Chinese).
Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. https://arxiv.org/abs/1506.00019v4
Liu, H., Mi, X. W., & Li, Y. F. (2018). Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Conversion and Management, 156, 498–514. https://doi.org/10.1016/j.enconman.2017.11.053
Lu, W., Zhang, Y., Xu, C., Lin, C., & Huo, Y. (2019). A deep learning-based satellite target recognition method using radar data. Sensors, 19, 2008. https://doi.org/10.3390/s19092008
Mallick, M., Mohanta, A., Kumar, A., & Patra, K. C. (2020). Prediction of wind-induced mean pressure coefficients using GMDH neural network. Journal of Aerospace Engineering, 33, 04019104. https://doi.org/10.1061/(ASCE)AS.1943-5525.0001101
Masetic, Z., & Subasi, A. (2016). Congestive heart failure detection using random forest classifier. Computer Methods and Programs in Biomedicine, 130, 54–64. https://doi.org/10.1016/j.cmpb.2016.03.020
Ministry of the Interior. (2014). Building technical regulations (Act No. 1020812044). Taiwan (in Chinese).
Ministry of the Interior. (2015). Wind resistance design specifications and commentary of buildings (Act No. 1030805400). Taiwan (in Chinese).
Ministry of Labor. (2014). Establish safety and health facilities standards (Act No. 10302006411). Taiwan (in Chinese).
Monner, D., & Reggia, J. A. (2012). A generalized LSTM-like training algorithm for second-order recurrent. Neural Networks, 25, 70–83. https://doi.org/10.1016/j.neunet.2011.07.003
Nair, V., & Hinton, G. (2010). Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (pp. 807–814), Haifa, Israel.
Noorollahi, Y., Jokar, M., & Kalhor, A. (2016). Using artificial neural networks for temporal and spatial wind speed forecasting in Iran. Energy Conversion and Management, 115, 17–25. https://doi.org/10.1016/j.enconman.2016.02.041
Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26, 217–222. https://doi.org/10.1080/01431160412331269698
Panapakidis, I. P., Michailides, C., & Angelides, D. C. (2019). A data-driven short-term forecasting model for offshore wind speed prediction based on computational intelligence. Electronics, 8, 420. https://doi.org/10.3390/electronics8040420
Sak, H., Senior, A., & Beaufays, F. (2014). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In Proceedings of the Annual Conference of International Speech Communication Association (INTERSPEECH).
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
Sheela, K. G., & Deepa, S. N. (2013). Neural network based hybrid computing model for wind speed prediction. Neurocomputing, 122, 425–429. https://doi.org/10.1016/j.neucom.2013.06.008
Shi, X., Lei, X., Huang, Q., Huang, S., Ren, K., & Hu, Y. (2018). Hourly day-ahead wind power prediction using the hybrid model of variational model decomposition and long shortterm memory. Energies, 11, 3227. https://doi.org/10.3390/en11113227
Tan, K. C., Khor, E. F., Lee, T. H., & Sathikannan, R. (2003). An evolutionary algorithm with advanced goal and priority specification for multi-objective optimization. Journal of Artificial Intelligence Research, 18, 183–215. https://doi.org/10.1613/jair.842
Üstün, B., Melssen, W. J., Oudenhuijzen, M., & Buydens, L. M. C. (2005). Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization. Analytica Chimica Acta, 544, 292–305. https://doi.org/10.1016/j.aca.2004.12.024
Vapnik, V. (1995). The nature of statistical learning theory. Springer-Verlag. https://doi.org/10.1007/978-1-4757-2440-0
Wei, C. C. (2012). Wavelet kernel support vector machines forecasting techniques: case study on water-level predictions during typhoons. Expert Systems with Applications, 39, 5189– 5199. https://doi.org/10.1016/j.eswa.2011.11.020
Wei, C. C. (2014). Surface wind nowcasting in the Penghu Islands based on classified typhoon tracks and the effects of the Central Mountain Range of Taiwan. Weather and Forecasting, 29, 1425–1450. https://doi.org/10.1175/WAF-D-14-00027.1
Wei, C. C. (2015). Forecasting surface wind speeds over offshore islands near Taiwan during tropical cyclones: comparisons of data-driven algorithms and parametric wind representations. Journal of Geophysical Research: Atmospheres, 120, 1826–1847. https://doi.org/10.1002/2014JD022568
Wei, C. C. (2017). Conceptual weather environmental forecasting system for identifying potential failure of under-construction structures during typhoons. Journal of Wind Engineering and Industrial Aerodynamics, 168, 48–59. https://doi.org/10.1016/j.jweia.2017.05.010
Wei, C. C. (2019). Study on wind simulations using deep learning techniques during typhoons: a case study of Northern Taiwan. Atmosphere, 10, 684. https://doi.org/10.3390/atmos10110684
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