基于知识图谱的梁桥检测报告自动抽取与分析OA

Bridge health monitoring serves as a crucial technical approach for bridge operation and maintenance.While widely applied to large-span bridges,the performance evaluation of small-and medium-span bridges predominantly relies on manual inspections,which are documented in professional and standardized text reports.Most small-and medium-span bridges are girder bridges with a design service life exceeding fifty years.Since at least one inspection report is generated annually,a massive volume of bridge inspection reports with a relatively uniform language style is accumulated.With the increasing number of bridges,the information within massive historical inspection reports must be reorganized to extract the historical performance evolution patterns and the spatiotemporal correlation characteristics among multiple regional bridges.Consequently,manual reading for effective information extraction becomes a highly time-consuming and labor-intensive analytical method.To enhance the extraction efficiency of effective information from inspection reports,a knowledge-graph-based automatic information extraction and analysis method tailored for the bridge domain was proposed.Based on existing large-scale pre-trained models,a bridge-domain pre-trained model,BriBERT,was trained following the"pre-training+continued pre-training+fine-tuning"research paradigm.Through this method,information is extracted from the defect description sentences within the reports,and historical information is stored via knowledge graphs.Ultimately,a bridge defect development database is constructed to provide data support for bridge condition analysis,research,and prediction..