This study developed an Industry Foundation Classes (IFC) building information modeling (BIM) based framework for bridge health monitoring using behavioral prediction under complex loading conditions. The proposed framework predicts the behavior of the current bridge state under complex loading conditions then employs an anomaly detection method that compares the measured behavior of the bridge structure with the predicted normal value under the same loading condition. This behavioral prediction is accomplished using an artificial neural network (ANN) model based on structural analysis theory and trained using long-term sensor data. The proposed framework operates in an IFC-BIM environment to facilitate bridge management. The IFC spatial element provides a connection between the sensor and the bridge element and between the anomaly information and the IFC object of the bridge element. The proposed framework is then demonstrated on a field cable-stayed bridge in Korea. The results confirm the prediction accuracy of the proposed ANN model under complex loading conditions and its ability to identify element anomalies for maintenance.

本研究开发了一个基于行业基础类 (IFC) 建筑信息建模 (BIM) 的框架，用于在复杂负载条件下使用行为预测进行桥梁健康监测。所提出的框架预测复杂加载条件下当前桥梁状态的行为，然后采用异常检测方法将桥梁结构的测量行为与相同加载条件下的预测正常值进行比较。这种行为预测是使用基于结构分析理论的人工神经网络 (ANN) 模型完成的，并使用长期传感器数据进行训练。提议的框架在 IFC-BIM 环境中运行，以促进桥梁管理。IFC 空间元素提供了传感器和桥元素之间以及异常信息和桥元素的 IFC 对象之间的连接。然后在韩国的现场斜拉桥上展示了所提议的框架。结果证实了所提出的人工神经网络模型在复杂负载条件下的预测准确性及其识别维护元素异常的能力。