Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction
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- Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
- Zhang, Limao & Wu, Xianguo & Skibniewski, Miroslaw J. & Zhong, Jingbing & Lu, Yujie, 2014. "Bayesian-network-based safety risk analysis in construction projects," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 29-39.
- Chen, Huazhou & Chen, An & Xu, Lili & Xie, Hai & Qiao, Hanli & Lin, Qinyong & Cai, Ken, 2020. "A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources," Agricultural Water Management, Elsevier, vol. 240(C).
- Jinzhu Lu & Lijuan Tan & Huanyu Jiang, 2021. "Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
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Keywords
construction safety; unsafe behaviors; physical safety management; safety management issues; deep learning;All these keywords.
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