Deep Learning Based Image Recognition Technology for Civil Engineering Applications

[1] Malik, S., Fatima, F., Imran, A., Chuah, L. F., & Bokhari, A. (2019). Improved project control for sustainable development of construction sector to reduce environment risks. Journal of Cleaner Production, 240, 118214. Search in Google Scholar

[2] Raviv, G., Shapira, A., & Fishbain, B. (2017). Ahp-based analysis of the risk potential of safety incidents: case study of cranes in the construction industry. Safety Science, 91(c), 298-309. Search in Google Scholar

[3] Islam, M. S., Islam, R., & Mahmud, T. (2017). Safety practices and causes of fatality in building construction projects: a case study for bangladesh. Jordan Journal of Civil Engineering, 11(2), 267-278. Search in Google Scholar

[4] Gao, R., Chan, A. P. C., Lyu, S., Zahoor, H., & Utama, W. P. (2018). Investigating the difficulties of implementing safety practices in international construction projects. Safety Science, 108, 39-47. Search in Google Scholar

[5] Uzo, E., & Issa, M. H. (2018). Case study application of prevention through design to enhance workplace safety and health in manitoba heavy construction projects. Canadian Journal of Civil Engineering, 46, cjce-2017-0454-. Search in Google Scholar

[6] Xing-Peng, W., Shu-Hua, L., Wen-Mei, L., & University, S. T. (2018). Research on the emergency decision-making method for the safety accidents in subway construction based on cbr. Journal of Railway Engineering Society. Search in Google Scholar

[7] Cao, Ruijun, Hsu, Shu-Chien, Lu, & Jin-Chun, et al. (2017). A self organizing map optimization based image recognition and processing model for bridge crack inspection. Automation in Construction. Search in Google Scholar

[8] Akylbekov, O., Said, N. A., Rebeca Martínez-García, & Gura, D. (2022). Ml models and neural networks for analyzing 3d data spatial planning tasks: example of khasansky urban district of the russian federation. Advances in engineering software. Search in Google Scholar

[9] Liu, J., Luo, H., & Liu, H. (2022). Deep learning-based data analytics for safety in construction. Automation in construction(Aug.), 140. Search in Google Scholar

[10] Jeon, J., & Cai, H. (2023). Wearable eeg-based construction hazard identification in virtual and real environments: a comparative study. Safety Science. Search in Google Scholar

[11] Lin, Z. H., Chen, A. Y., & Hsieh, S. H. (2021). Temporal image analytics for abnormal construction activity identification. Automation in Construction, 124, 103572. Search in Google Scholar

[12] Chen, H. L. S. (2019). Advanced geological prediction technology of tunnel based on image recognition. Arabian journal of geosciences, 12(20). Search in Google Scholar

[13] Rahimian, F. P., Seyedzadeh, S., Oliver, S., Rodriguez, S., & Dawood, N. (2019). On-demand monitoring of construction projects through a game-like hybrid application of bim and machine learning. Automation in Construction, 110(February 2020), 14. Search in Google Scholar

[14] Lu, Q., & Lee, S. (2017). Image-based technologies for constructing as-is building information models for existing buildings. Journal of Computing in Civil Engineering, 31(4), 04017005. Search in Google Scholar

[15] Hoang, N. D. (2019). Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression. Automation in Construction, In Press(SEP.), 102843.1-102843.12. Search in Google Scholar

[16] Lu, Q., Lee, S., & Chen, L. (2018). Image-driven fuzzy-based system to construct as-is ifc bim objects. Automation in Construction, 92(AUG.), 68-87. Search in Google Scholar

[17] Li, G., Zhao, X., Du, K., Ru, F., & Zhang, Y. (2017). Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine. Automation in Construction, 78(JUN.), 51-61. Search in Google Scholar

[18] Bai, Z., Liu, T., Zou, D., Zhang, M., Zhou, A., & Li, Y. (2023). Image-based reinforced concrete component mechanical damage recognition and structural safety rapid assessment using deep learning with frequency information. Automation in construction. Search in Google Scholar

[19] Tang, S., & Golparvar-Fard, M. (2021). Machine learning-based risk analysis for construction worker safety from ubiquitous site photos and videos. Journal of computing in civil engineering(6), 35. Search in Google Scholar

[20] Zhang, M., Zhu, M., & Zhao, X. (2020). Recognition of high-risk scenarios in building construction based on image semantics. Journal of Computing in Civil Engineering, 34(4), 04020019. Search in Google Scholar

[21] Yang, Q., Zhang, Y., & Zhao, T. (2018). Example-based image super-resolution via blur kernel estimation and variational reconstruction. Pattern Recognition Letters. Search in Google Scholar

[22] Zhong, X., Peng, X., Yan, S., Shen, M., & Zhai, Y. (2018). Assessment of the feasibility of detecting concrete cracks in images acquired by unmanned aerial vehicles. Automation in Construction, 89, 49-57. Search in Google Scholar

[23] Yi, Y. K., Zhang, Y., & Myung, J. (2020). House style recognition using deep convolutional neural network. Automation in Construction, 118, 103307. Search in Google Scholar

[24] Lee, S. K., & Yu, J. H. (2023). Ontological inference process using ai-based object recognition for hazard awareness in construction sites. Automation in construction. Search in Google Scholar

[25] Kropp, C., Koch, C., & Knig, M. (2018). Interior construction state recognition with 4d bim registered image sequences. Automation in construction, 86(FEB.), 11-32. Search in Google Scholar