卡尔曼滤波（Kalman filter, KF）和最大化后验概率法（Maximum a Posteriori, MAP）是结构荷载识别中常见的两类广义贝叶斯滤波算法，KF法计算效率高但数值稳定性较差，MAP法适用性强却需要复杂的矩阵求逆运算，加之这两类方法对荷载形式和测点布置的苛刻要求，目前仅适用于简单荷载的识别。为此，本研究提出了针对任意分布式荷载的贝叶斯全局响应重构方法，从在线和离线两个角度改进了现有方法。针对在线KF方法，本研究从结构动力特性中导出等效荷载向量来降低未知荷载的维度，得到满足可控性条件的等效系统模型，并采用输入状态联合估计方法同时识别等效荷载和全局响应。针对离线MAP方法，引入考虑了空间相关性的荷载先验分布，采用MAP策略同时对等效荷载和观测噪声进行迭代估计，随后根据识别得到的等效荷载重构全局响应。改进后的在线和离线方法均不需要提前获取荷载位置或分布形式。最后，通过青州大桥在风荷载和交通荷载下采集的响应数据对所提方法的精度和适用性进行了验证。

The Kalman filter (KF) and Maximum a Posteriori (MAP) methods are two types of generalized Bayesian filters in structural load identification. KF is computationally efficient with poor numerical stability, and in contrast, MAP is highly applicable requiring complex matrix inversion operations. Additionally, both methods have limitations on the load distribution and sensor arrangement, applying to simple types of loads. To address these challenges, a Bayesian full-field response reconstruction approach is proposed for arbitrary distributed loads, enhancing both online and offline methods. In online KF methods, a set of equivalent force vectors derived from the structural dynamic property is used to reduce the dimension of unknown loads, and a reduced system model is obtained with sufficient controllability on the origin system model. The joint input-state estimation filter is utilized to simultaneously identify equivalent loads and full-field responses. In the offline MAP method, a load prior distribution considering spatial correlation is introduced. The most probable load parameters and hyperparameters are iteratively estimated by MAP strategy. Consequently, the identified distributed loads are adopted to reconstruct the full-field structural responses. The improved online and offline methods both do not require prior information of load positions or distribution. Finally, the proposed methods are validated using the structural response data of Qingzhou Bridge to wind and traffic loads.