Model of identifying entities of safety specification for hydropower engineering construction

doi:10.16265/j.cnki.issn1003-3033.2024.09.0008

Abstract

Abstract:

To accurately identify the entities of hydropower engineering construction safety specification, the named entity recognition model of hydropower engineering construction safety specification was constructed. The rich semantic information in the text was mined by the BERT. The semantic features of the specification were extracted by using BILSTM. The dependency relationship between entities was analyzed by relying on CRFs. The Technical Specification for Safety Protection in Construction of Water Conservancy and Hydropower Projects (SL714-2015) was taken as an example to calculate the named entity recognition model accuracy rate. The results show that the accuracy rate of the BERT-BILSTM-CRF model is 94.21%. Compared with the three traditional methods, the accuracy is significantly improved. The research will effectively assist in the intelligent management of safety regulations knowledge for hydropower engineering construction, and provide important support for the intelligent identification of construction safety hazards.

Key words: named entity identification, hydropower engineering construction, safety specification, bidirectional encoder representation from transformers (BERT), bi-directional long and short-term memory neural network (BILSTM), conditional random field (CRF)

CLC Number:

X948

CHEN Shu, ZHANG Chao, CHEN Yun, ZHANG Guangfei, LI Zhi. Model of identifying entities of safety specification for hydropower engineering construction[J]. China Safety Science Journal, 2024, 34(9): 19-26.

Figures/Tables 10

Fig.1

Fig.2

Table 1

Table 2

Table 3

Table 4

Fig.3

Fig.4

Table 5

Table 6

References 20

[1]	陈述, 习俊博, 王建平, 等. 水电工程施工安全隐患关联规则挖掘[J]. 中国安全科学学报, 2021, 31(8): 75-82. doi: 10.16265/j.cnki.issn1003-3033.2021.08.011
	CHEN Shu, XI Junbo, WANG Jianping, et al. Mining association rules of near-misses of hydropower projects construction[J]. China Safety Science Journal, 2021, 31(8): 75-82. doi: 10.16265/j.cnki.issn1003-3033.2021.08.011
[2]	樊启祥, 林鹏, 魏鹏程, 等. 水电工程安全事故发生机制与管理对策[J]. 中国安全科学学报, 2019, 29(1): 144-149. doi: 10.16265/j.cnki.issn1003-3033.2019.01.024
	FAN Qixiang, LIN Peng, WEI Pengcheng, et al. Hydropower engineering safety accident occurrence mechanism and management strategies[J]. China Safety Science Journal, 2019, 29(1): 144-149. doi: 10.16265/j.cnki.issn1003-3033.2019.01.024
[3]	CHEN Shu, TIAN Ya, JIN Lianghai, et al. Estimating the frequency of exposure to uncertain hazards: impact of wind conditions on concrete dam construction[J]. Journal of Construction Engineering and Management, 2021, 147(2): 1-13.
[4]	张小衡, 王玲玲. 中文机构名称的识别与分析[J]. 中文信息学报, 1997, 11(4): 21-32.
	ZHANG Xiaoheng, WANG Lingling. Identification and analysis of Chinese institutional names[J]. Journal of Chinese Information Processing, 1997, 11(4): 21-32.
[5]	BIKEL D M, SCHWARTA R, WEISCHEDEL R M. An algorithm that learns what's in a name[J]. Machine Learning Journal Special Issue on Natural Language Learning, 1999, 34(1/2/3): 211-231.
[6]	ZHANG Qiqi, XUE Cong, ZHOU Peng, et al. Named entity recognition for Chinese construction documents based on conditional random field[J]. Frontiers of Engineering Management, 2022, 10(12): 237-249.
[7]	TAEKHYUNG K, SEOKHO C. Accident case retrieval and analyses: using natural language processing in the construction industry[J]. Journal of Construction Engineering and Management, 2019, 145(3): 1-13.
[8]	李明超, 田丹, 沈扬, 等. 融入Attention机制改进Word2vec技术的水利水电工程专业词智能提取与分析方法[J]. 水利学报, 2020, 51(7): 816-826.
	LI Mingchao, TIAN Dan, SHEN Yang, et al. An intelligent extraction and analysis approach of professional technical words for hydraulic engineering by improved Word2vec technology with Attention mechanism[J]. Journal of Hydraulic Engineering, 2020, 51(7): 816-826.
[9]	刘婷, 张社荣, 王超, 等. 水利施工事故文本智能分析的BERT-BiLSTM混合模型[J]. 水力发电学报, 2022, 41(7): 1-12.
	LIU Ting, ZHANG Sherong, WANG Chao, et al. Text intelligent analysis for hydraulic construction accidents based on BERT-BiLSTM hybrid model[J]. Journal of Hydroelectric Engineering, 2022, 41(7): 1-12.
[10]	杨秀璋, 郭明镇, 候红涛, 等. 融合情感词典的改进BILSTM-CNN + Attention情感分类算法[J]. 科学技术与工程, 2022, 22(20): 8761-8770.
	YANG Xiuzhang, GUO Mingzhen, HOU Hongtao, et al. Improved BILSTM-CNN + Attention sentiment classification algorithm fused with sentiment dictionary[J]. Science Technology and Engineering, 2022, 22(20): 8761-8770.
[11]	易明, 李藿然, 刘继月. 基于Glove-BILSTM的在线研讨信息分类模型研究[J]. 情报理论与实践, 2022, 45(9): 173-179,172. doi: 10.16353/j.cnki.1000-7490.2022.09.023
	YI Ming, LI Huoran, LIU Jiyue. Research on online discussion information classification model based on Glove-BILSTM[J]. Information Studies: Theory & Application, 2022, 45(9): 173-179, 172.
[12]	杨秀璋, 彭国军, 李子川, 等. 基于Bert和BiLSTM-CRF的APT攻击实体识别及对齐研究[J]. 通信学报, 2022, 43(6): 58-70. doi: 10.11959/j.issn.1000-436x.2022116
	YANG Xiuzhang, PENG Guojun, LI Zichuan, et al. Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF[J]. Journal on Communications, 2022, 43(6): 58-70. doi: 10.11959/j.issn.1000-436x.2022116
[13]	CHEN Shu, XI Junbo, CHEN Yun, et al. Association mining of near misses in hydropower engineering construction based on convolutional neural network text classification[J]. Computational Intelligence and Neuroscience, 2022, 2022: 1-16.
[14]	赵鹏辉, 李俊杰, 康飞. 河道山地灾害的卷积神经网络快速识别方法[J]. 水利水运工程学报, 2019(2): 65-70.
	ZHAO Penghui, LI Junjie, KANG Fei. Fast recognition method for mountain hazards in river courses based on convolutional neural networks[J]. Hydro-Science and Engineering, 2019, 19(2): 65-70.
[15]	于媛芳, 张勇, 左皓阳, 等. 基于角色信息引导的多轮事件论元抽取[J]. 北京大学学报:自然科学版, 2023, 59(1): 83-91.
	YU Yuanfang, ZHANG Yong, ZUO Haoyang, et al. Multi-turn event argument extraction based on role information guidance[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2023, 59(1): 83-91.
[16]	乔卫亮, 刘阳, 周群, 等. 基于模糊人工神经网络的安全风险评估模型[J]. 安全与环境学报, 2021, 21(4): 1405-1411.
	QIAO Weiliang, LIU Yang, ZHOU Qun, et al. A risk assessment model based on fuzzy artificial neural network[J]. Journal of Safety and Environment, 2021, 21(4): 1405-1411.
[17]	宋敦江, 杨霖, 钟少波. 基于BERT的灾害三元组信息抽取优化研究[J]. 中国安全科学学报, 2022, 32(2): 115-120. doi: 10.16265/j.cnki.issn1003-3033.2022.02.016
	SONG Dunjiang, YANG Lin, ZHONG Shaobo. Research on optimization of disaster triplet information extraction based on BERT[J]. China Safety Science Journal, 2022, 32(2): 115-120. doi: 10.16265/j.cnki.issn1003-3033.2022.02.016
[18]	黄光球, 王瑞泽, 陆秋琴. 基于K-CNN-BiLSTM的关联区域VOCs浓度时空关联预测[J]. 安全与环境学报, 2023, 23(4): 1336-1348.
	HUANG Guangqiu, WANG Ruize, LU Qiuqin. Spatial and temporal correlation prediction of VOCs concentration in associated areas based on K-CNN-BiLSTM[J]. Journal of Safety and Environment, 2023, 23(4): 1336-1348.
[19]	SUBAKTI A, MURFI H, HARIADI N. The performance of BERT as data representation of text clustering[J]. Journal of Big Data, 2022, 9(8): 163-172.
[20]	SL714—2015,水利水电工程施工安全防护设施技术规范[S].
	SL714-2015,Technical specification for safety protection facilities for construction of water and hydropower projects[S].

实体	定义	示例
PLA	施工区域	作业面、进出口
FAC	施工设备	脚手架、钢爬梯
ACT	施工行为	敷设、拆除
PEO	施工人员	员工、作业人员

实体类别	次数
PLA	394
FAC	523
ACT	249
PEO	267

实体类别	P	R	F₁
PLA	91.31	89.55	90.43
FAC	98.12	97.66	97.89
ACT	92.79	91.53	92.16%
PEO	95.18	97.53	96.36

模型	P	R	F₁
BILSTM	78.72	77.07	77.89
LSTM-CRF	86.79	84.51	85.65
BILSTM-CRF	89.52	88.16	88.84
BERT-BILSTM-CRF	94.35	94.07	94.21

模型	P	R	F₁
BERT	67.53	65.05	66.29
BERT-BILSTM	87.65	86.33	86.99
BERT-BILSTM-CRF	94.35	94.07	94.21