Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before data fusion, the improved self-support function is adopted to preprocess the original data. The process of data fusion is divided into three steps: Firstly, the feature of the same kind of sensor data is extracted by the adaptive weighted average method as the input source of BPNN. Then, BPNN is trained and its output is used as the basic probability assignment (BPA) of D-S evidence theory. Finally, Bhattacharyya Distance (BD) is introduced to improve D-S evidence theory from two aspects of evidence distance and conflict factors, and multi-source data fusion is realized by D-S synthesis rules. In practical application, a three-level information fusion framework of the data level, the feature level, and the decision level is proposed, and the safety status of buildings is evaluated by using multi-source sensor data. The results show that compared with the fusion result of the traditional D-S evidence theory, the algorithm improves the accuracy of the overall safety state assessment of the building and reduces the MSE from 0.18 to 0.01%.

基于单个传感器数据的建筑物安全评估存在可靠性低和不确定性高的问题。因此，本文提出了一种基于改进的Dempster-Shafer（DS）证据理论和反向传播神经网络（BPNN）的多源传感器数据融合方法。在数据融合之前，采用改进的自支持功能对原始数据进行预处理。数据融合的过程分为三个步骤：首先，通过自适应加权平均法提取同类传感器数据的特征作为BPNN的输入源。然后，对BPNN进行训练，并将其输出用作DS证据理论的基本概率分配（BPA）。最后，从证据距离和冲突因素两个方面介绍了Bhattacharyya距离（BD），以改进DS证据理论，通过DS综合规则实现多源数据融合。在实际应用中，提出了数据级别，特征级别和决策级别的三级信息融合框架，并利用多源传感器数据对建筑物的安全状态进行了评估。结果表明，与传统DS证据理论的融合结果相比，该算法提高了建筑物总体安全状态评估的准确性，并将MSE从0.18％降低到0.01％。