Automatic Cause Inference of Construction Accident Using Long Short-Term Memory Neural Networks
Publication: ICCREM 2022
ABSTRACT
Research of predicting the causes of construction accidents from documents has attracted increased interest in the passing three decades. One main branch of this type of research is to use automatic methods to enable effective causal inference from a large amount of textual data. To improve the accuracy and reduce labor resources required, learning-based methods have been successfully employed over full texts of construction accident reports. However, to date, these methods are not capable of wide application in the construction industry, where most of the accident narratives are recorded as short texts. Moreover, the data imbalance problem is a frequent bottleneck in the classification performance. To achieve a higher degree of adaptability for construction accident classification, this study develops a framework consisting of data augmentation, Bi-LSTM and self-attention neural networks, and focal loss objective function, which is trained and tested over two data sets consisting of short-text and imbalanced data. The validation results showed that, even with much less information provided in the short texts, the proposed model has superior performance to existing methods.
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REFERENCES
Baek, S., Jung, W., and Han, S. H. (2021). “A critical review of text-based research in construction: Data source, analysis method, and implications.” Automation in Construction, 132, 103915.
Baker, H., Hallowell, M. R., and Tixier, A. J. P. (2020). “AI-based prediction of independent construction safety outcomes from universal attributes.” Automation in Construction, 118, 103146.
Cheng, M.-Y., Kusoemo, D., and Gosno, R. A. (2020). “Text mining-based construction site accident classification using hybrid supervised machine learning.” Automation in Construction, 118, 103265.
Dong, X. (2018). Construction chart book (6th edition), Sliver Spring, Georgia Ave.
Fang, W., Luo, H., Xu, S., Love, P. E. D., Lu, Z., and Ye, C. (2020). “Automated text classification of near-misses from safety reports: An improved deep learning approach.” Advanced Engineering Informatics, 44, 101060.
Goh, Y. M., and Ubeynarayana, C. U. (2017). “Construction accident narrative classification: An evaluation of text mining techniques.” Accident Analysis & Prevention, 108, 122–130.
Hochreiter, S., and Schmidhuber, J. (1997). “Long short-term memory.” Neural Computation, 9, 1735–1780.
Kim, T., and Chi, S. (2019). “Accident case retrieval and analyses: Using natural language processing in the construction industry.” Journal of Construction Engineering and Management, 145, 04019004.
Lin, T., Goyal, P., Girshick, R., He, K., and Dollár, P. (2020). “Focal loss for dense object detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318–327.
Martínez-Rojas, M., Martín Antolín, R., Salguero-Caparrós, F., and Rubio-Romero, J. C. (2020). “Management of construction safety and health plans based on automated content analysis.” Automation in Construction, 120, 103362.
Pennington, J., Socher, R., and Manning, C. (2014). “Glove: Global vectors for word representation.” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning representations by back-propagating errors.” Nature, 323, 533–536.
Sacks, R., Rozenfeld, O., and Rosenfeld, Y. (2009). “Spatial and temporal exposure to safety hazards in construction.” Journal of Construction Engineering and Management, 135, 726–736.
Soibelman, L., Wu, J., Caldas, C., Brilakis, I., and Lin, K.-Y. (2008). “Management and analysis of unstructured construction data types.” Advanced Engineering Informatics, 22, 15–27.
Wei, J., and Zou, K. (2019). “Eda: Easy data augmentation techniques for boosting performance on text classification tasks.” arXiv preprint arXiv:1901, 11196.
Xu, N., Ma, L., Wang, L., Deng, Y., and Ni, G. (2021). “Extracting domain knowledge elements of construction safety management: Rule-Based approach using Chinese natural language processing.” Journal of Management in Engineering, 37, 04021001.
Yu, A. W., Dohan, D., Luong, M.-T., Zhao, R., Chen, K., Norouzi, M., and Le, Q. V. (2018). “Qanet: Combining local convolution with global self-attention for reading comprehension.” arXiv preprint arXiv:1804, 09541.
Zhang, J., Zi, L., Hou, Y., Deng, D., Jiang, W., and Wang, M. (2020). “A C-BiLSTM approach to classify construction accident reports.” Applied Sciences, 10, 5754.
Zhong, B., Pan, X., Love, P. E. D., Ding, L., and Fang, W. (2020a). “Deep learning and network analysis: Classifying and visualizing accident narratives in construction.” Automation in Construction, 113, 103089.
Zhong, B., Pan, X., Love, P. E. D., Sun, J., and Tao, C. (2020b). “Hazard analysis: A deep learning and text mining framework for accident prevention.” Advanced Engineering Informatics, 46, 101152.
Zhou, Z., Li, Q., and Wu, W. (2012). “Developing a versatile subway construction incident database for safety management.” Journal of Construction Engineering and Management, 138, 1169–1180.
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Published online: Dec 15, 2022
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