地下交通枢纽BIM-KNN损伤识别方法研究  

Research on BIM-KNN Damage Identification Method for Underground Transportation Hub

作　　者：汪国良[1] WANG Guoliang(China Railway Siyuan Survey and Design Group Co.Ltd.,Wuhan Hubei 430063,China)

机构地区：[1]中铁第四勘察设计院集团有限公司,湖北武汉430063

出　　处：《铁道建筑技术》2024年第8期5-9,87,共6页Railway Construction Technology

基　　金：中铁第四勘察设计院集团有限公司科技研究开发计划(2018K139,KY20230265)。

摘　　要：为实现地下交通枢纽损伤状态识别预测,结合光谷综合体枢纽工程研究及实践,提出基于BIM-KNN(建筑信息模型-最小近邻算法)的地下交通枢纽损伤识别方法。通过对构建的地下交通枢纽方案进行数字化仿真模拟、计算分析,提取特征点的控制指标信息,构建大数据训练样本集;根据数值仿真分析结果,对受力薄弱和变形薄弱或关键部位进行特征点监测以获取特征点监测数据信息。选取K个特征点实测结构控制指标监测值作为测试样本集,利用有限的监测点数据测试样本,提出改进的KNN算法,实现地下交通枢纽损伤状态的识别预测,合理评价结构健康状态,辅助运维管养决策。In order to achieve the identification and prediction of damage status in underground transportation hub,combined with the research and practice experience of the Optics Valley Complex Hub Project,the BIM-KNN(Building Information Modeling-K-Nearest Neighbors)based damage identification method for underground transportation hub was proposed.Through digital simulation,calculation and analysis of the constructed underground transportation hub scheme,the control index information of feature points was extracted,and the big data training sample set was constructed.Based on the numerical simulation analysis results,feature point monitoring was carried out for weak stress and deformation parts or key parts to obtain the monitoring data information.K measured structural control index monitoring values of feature points were selected as the test sample set.Using limited monitoring point data in test sample set,an improved KNN algorithm was proposed to identify and predict the damage status of underground transportation hubs,and reasonably evaluate the structural health status,assist in operation,maintenance and management decision-making to support structure service life.

关 键 词：地下交通枢纽 损伤识别 统计分析 BIM-KNN 大数据 样本集 

分 类 号：TU317[建筑科学—结构工程] TP391.9[自动化与计算机技术—计算机应用技术]