Acknowledgement
This paper is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00143371). The paper is also expanded from a conference paper presented at 2023 KSCE Convention held in Yeosu, South Korea on Oct. 18-20, 2023.
References
- Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O. and Ahmed, A. A. (2020). "Deep learning in the construction industry: A review of present status and future innovation." Journal of Building Engineering, Elsevier, Vol. 32. https://doi.org/10.1016/j.jobe.2020.101827.
- Bae, S., Ham, S., Lee, I., Lee, G. P. and Kim, D. (2019). "Deep learning based crack detection from tunnel cement concrete lining." Journal of Korean Tunnelling and Underground Space Association, KTA, Vol. 24, No. 6, pp. 583-598, https://doi.org/10.9711/KTAJ.2022.24.6.583 (in Korean).
- Cahuantzi, R., Chen, X. and Guttel, S. (2023). "A comparison of LSTM and GRU networks for learning symbolic sequences." Proceeding of Science and Information Conference, London, UK, pp. 771-785, https://doi.org/10.1007/978-3-031-37963-5_53.
- Choi, S. J., Choi, S. W., Kim, J. H. and Lee, E. B. (2021). "AI and text-mining applications for analyzing constractor's risk in Invitation to Bid(ITB) and contracts for engineering procurement and construction (EPC) projects." Energies, MDPI, Vol. 14, No. 15. https://doi.org/10.3390/en14154632.
- Choi, S. W. and Lee, E. B. (2022). "Contractor's risk analysis of engineering procurement and construction(EPC) contracts using ontological semantic model and bi-long short-term memory (LSTM) technology." Sustainability, MDPI, Vol. 14, No. 11. https://doi.org/10.3390/su14116938.
- Eom, S. H., Cha, G., Park, S. K., Park, S. and Park, J. (2023). "Analysis of potential construction risk types in formal documents using text mining." KSCE Journal of Civil and Environmental Engineering Research, KSCE, Vol. 43, No. 1, pp. 91-98, https://doi.org/10.12652/Ksce.2023.43.1.0091 (in Korean).
- Kang, E. A., Kim, S. and Kim, S. (2022). "Quality control of reinforced concrete work using deep-learning based on object recognition." Journal of the Regional Association of Architectural Institute of Korea, AIKRA, Vol. 24, No. 2, pp. 17-24 (in Korean).
- Kim, M. H. (2019). Application of computer vision based deep learning technique for detecting safety helmet of construction workers, Msc. thesis, Pukyong National University, Busan, Korea (in Korean).
- Kim, S., Cha, G., Cho, M. and Park, S. (2022). "Text mining based analysis of construction accident causes and risk factors." Proceedings of the 2022 Spring Conference of the Korea Academia-Industrial Cooperation Society, Jeju, South Korea, pp. 272-273 (in Korean).
- Kim, J., Lee, C. W., Park, S. H., Lee, J. H. and Hong, C. H. (2020). "Development of fire detection model for underground utility facilities using deep learning: Training data supplement and bias optimization." Journal of the Korea Academia-Industrial Cooperation Society, KAIS, Vol. 21, No. 12, pp. 320-330, https://doi.org/10.5762/KAIS.2020.21.12.320 (in Korean).
- Kim, J., Park, S. and Hong, C. H. (2023). "A study on falling detection of workers in the underground utility tunnel using dual deep learning techniques." Journal of the Society of Disaster Information, KOSDI, Vol. 19, No. 3, pp. 498-509, https://doi.org/10.15683/kosdi.2023.9.30.498 (in Korean).
- Kudo, T., Yamamoto, K. and Matsumoto, Y. (2004). "Applying conditional random fields to Japanese morphological analysis." Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, ACL, Barcelona, Spain, pp. 230-237.
- Lee, J. H. (2019). "Global research trend based on Natural Language Processing of irregular text data in the construction industry." Magazine of Construction Management, KICEM, Vol. 20, No. 2, pp. 62-65 (in Korean).
- Lee, K., Kang, S. and Shin, Y. (2022). "A study on the application of object detection method in construction site through real case analysis." Journal of the Society of Disaster Information, KOSDI, Vol. 18, No. 2, pp. 269-279, https://doi.org/10.15683/KOSDI.2022.6.30.269 (in Korean).
- Lee, D., Yeon, J., Hwang, I. and Lee, S. (2010)."KKMA : A tool for utilizing sejong corpus based on relational database." JKIISE Transactions on Computing Practices, KIISE, Vol. 16, No. 11, pp. 1046-1050 (in Korean).
- Lee, D. H., Yoon, G. H. and Kim, J. J. (2019). "Development of ITB risk management model based on AI in bidding phase for oversea EPC projects." The Journal of the Institute of Internet, Broadcasting and Communication, IIBC, Vol. 19, No. 4, pp. 151-160, https://doi.org/10.7236/JIIBC.2019.19.4.151 (in Korean).
- Moon, S., Chi, S. and Im, S. B. (2022). "Automated detection of contractual risk clauses from construction specifications using bidirectional encoder representations from transformers (BERT)." Automation in Construction, Elsevier, Vol. 142, 104465, https://doi.org/10.1016/j.autcon.2022.104465.
- Moon, S., Lee, G. and Chi, S. (2021a). "Semantic text-pairing for relevant provision identification in construction specification reviews." Automation in Construction, Elsevier, Vol. 128, 103780, https://doi.org/10.1016/j.autcon.2021.103780.
- Moon, S., Lee, G., Chi, S. and Oh, H. (2021b). "Automated construction specification review with named entity recognition using natural language processing." Journal of Construction Engineering and Management, ASCE, Vol. 147, No. 1. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001953.
- Park, S. (2021). Development of visualization system for deep learning-based progress comparison using the real image of construction site and 4D model, Ph.D thesis, Gyeongsang National University (in Korean).
- Park, E. J. and Cho, S. Z. (2014). "KoNLPy: Korean natural language processing in Python." Proceedings of the 26th Annual Conference on Human & Cognitive Language Technology, KIISE, Kangwon, Korea, pp. 133-136 (in Korean).
- Park, K. and Kim, H. (2021). "Analysis of seasonal Importance of construction hazards using text mining." KSCE Journal of Civil and Environmental Engineering Research, KSCE, Vol. 41, No. 3, pp. 305-316, https://doi.org/10.12652/Ksce.2021.41.3.0305 (in Korean).
- Prabowo, Y. D., Warnars, H. L. H. S., Budiharto, W., Kistijantoro, A. I., Heryadi, Y. and Lukas (2018). "Lstm and simple rnn comparison in the problem of sequence to sequence on conversation data using Bahasa Indonesia." Proceedings of 2018 Indonesian Association for Pattern Recognition International Conference, IEEE, Jakarta, Indonesia, pp. 51-56, https://doi.org/10.1109/INAPR.2018.8627029.
- Saitoh, K. (2018). Deep Learning from Scratch 2, Hanbit Media, translated by Gaeapmapsi (in Korean).
- Shewalkar, A. N. (2018). Comparison of RNN, LSTM and GRU on Speech Recognition Data, Msc. thesis, North Dakota State University, North Dakota, USA.
- Wiliams, T. P. and Gong, J. (2014). "Predicting construction cost overruns using text mining, numerical data and ensemble classifiers." Automation in Construction, Elsevier, Vol. 43, pp. 23-29, https://doi.org/10.1016/j.autcon.2014.02.014.