基于证据推理的隧道坍塌多源信息融合评估

A Multi-source Information Fusion Assessment for the Tunneling Collapse Disaster Based on Evidential Reasoning

由于影响因素众多,隧道坍塌风险评估是一个多属性决策问题.单源信息评估方法难以充分考虑所有的风险因素,导致预测结果存在偏差.为了评估隧道坍塌风险并提供更准确的风险控制策略,本研究提出了一种新的多源信息融合方法,该方法将云模型(CM)、支持向量机(SVM)和基于证据推理(ER)相结合.对多个信息源进行分析,得到不同的坍塌风险评估模型(目视检查数据通过SVM获取分类概率值,监测数据通过云模型获取概率值).每个模型的质量都由可信度和重要性权重来评价.然后运用ER规则融合各个评估模型的结果,给出总体的坍塌概率风险评估.与D-S理论相比,ER规则在处理高冲突信息方面具有更大的优势.当不同信息源的风险评估结果不一致时,D-S理论的融合结果往往会与常识相反,ER规则融合由于考虑了评估模型的重要性权重和可信度,更适用于高冲突信息的融合.该方法已成功应用于福建莆炎高速公路的鱼塘溪隧道.结果表明,所提出的多源信息融合方法的评价准确率为87.5%,而单源信息融合方法的评价准确率小于70%.此外,即使不同模型的风险结果存在较大的冲突,该融合模型也具有良好的性能.

Tunnel collapse risk assessment is a multi-attribute decision problem due to many influencing factors.It is difficult for the assessment method of a single information source to fully consider all risk factors,leading to bias in the prediction results.To assess the tunneling collapse risk and provide a more accurate risk-controlling strategy,this research proposes a new multi-source information fusion approach that combines cloud model(CM),support vector machine(SVM),and evidence-based reasoning(ER).Multiple sources of information were analyzed to obtain different collapse risk assessment models(where classification probability values for visual inspection data are obtained from SVM,and probability values for monitoring data are obtained from the cloud model).The quality of each model is evaluated by reliability and importance weights.The ER theory is then applied to fuse the results of each assessment model to give an overall collapse probability risk assessment.Compared with the D-S theory,the ER rule has more advantages in dealing with high-conflict information.When the risk assessment results of different single information sources are inconsistent,the fusion by the ER rules considers the importance weight and credibility of the assessment results,which is more suitable for the high-conflict information fusion.The novel approach has been successfully applied in the case of Yutangxi tunnel of Pu-Yan Highway(Fujian,China).The results indicate that the proposed multi-source information fusion method has an evaluation accuracy of 87.5%,while the single-source information method has an accuracy of less than 70%.Furthermore,the fusion model has excellent performance even if the risk result of different models has high conflict.