普速铁路桥梁设备缺陷文本分类模型研究
Text classification model for bridge equipment defect of ordinary railway
郭心全 1李俊波 1沈鹍 1吴霞 1李林1
作者信息
- 1. 中国铁道科学研究院集团有限公司 电子计算技术研究所,北京 100081
- 折叠
摘要
为解决普速铁路桥梁设备缺陷采集效率低下的问题,提高现场检查作业效率,提出一种融合预训练模型RoBERTa-wwm-ext、双向长短时记忆网络和注意力机制的模型(简称:改进BiLSTM-Att模型),即普速铁路桥梁设备缺陷文本分类模型.采用该模型,以圬工桥缺陷部位(桥面、支座、墩台、梁拱、桥渡水文、附属设施)缺陷文本分类为目标,对 15个铁路局集团公司的普速铁路圬工桥设备缺陷描述文本数据进行了实验验证.结果表明,改进BiLSTM-Att模型的精确率、召回率和F1值均达到了 90%以上,相对于对比模型,这些指标均有显著提高;改进BiLSTM-Att模型可有效识别桥梁设备缺陷,辅助现场桥梁设备检查作业.
Abstract
To solve the problem of low efficiency in defect collection of ordinary railway bridge equipment and improve the efficiency of on-site inspection operations,this paper proposed a model that integrated pre-trained model RoBERTa wwm ext,bidirectional long short-term memory network,and attention mechanism(referred to as the improved BiLSTM Att model),namely the text classification model for bridge equipment defect of ordinary railway.It adopted the model and aimed to classify the defect text of the masonry bridge defect parts(bridge deck,bearings,piers and abutments,beam arches,bridge crossing hydrology,ancillary facilities).Experimental verification was conducted on the defect description text data of 15 railway group companies'ordinary railway masonry bridge equipment.The results indicate that the accuracy,recall,and F1 score of the improved BiLSTM Att model have all reached over 90%,and these indicators have significantly improved compared to the comparative model.The improving BiLSTM Att model can effectively identify defects in bridge equipment and assist in on-site bridge equipment inspection operations.
关键词
普速铁路/桥梁/缺陷/BiLSTM-Att模型/文本分类Key words
ordinary railway/bridge/defect/BiLSTM-Att model/text classification引用本文复制引用
出版年
2024
NETL
NSTL
万方数据