## 1 Introduction  
The advent of innovative materials, design methodologies, and construction technologies has led to the widespread adoption of steel truss bridges, which possess the capacity to bear substantial loads, span considerable distances, and exhibit aesthetic appeal [1, 2, 3]. However, steel truss bridges are susceptible to component failure during long-term service. In the absence of timely notification regarding local damage, a chain reaction may be initiated, potentially resulting in significant impairment to the structural system in its entirety [4]. Therefore, the implementation of an effective structural health monitoring (SHM) system is imperative to ensure the safe operation of steel truss bridges in service [5, 6]. The reliability of the monitoring data directly correlates with the effectiveness of the structural health monitoring system. The optimal arrangement of sensors is a pivotal technology for ensuring data integrity. Research has demonstrated that a reasonable sensor arrangement design can enhance the quality of data acquisition [7; 8]. However, under the influence of complex natural environments, anomalies in the collected monitoring data are often observed [9; 10; 11]. These anomalies can be categorized into two distinct groups. The first category is attributed to the structural integrity of the bridge itself, influenced by its performance and the progressive deterioration of the materials used. This results in anomalies in the monitoring data. The second category is associated with errors in the sensor monitoring system itself, leading to data anomalies [12]. The latter type of anomalous data, caused by objective factors, is not reflective of the actual state of the structure to which the anomalies relate. This type of anomaly is referred to as a monitoring data anomaly. The necessity arises to employ effective methods to locate the abnormal monitoring data, as well as to troubleshoot and repair the data [13; 14; 15]. In the extant research on the monitoring of data from steel truss bridges, the majority of scholars employ conventional methods, such as the moving average and interpolation, to rectify anomalous data. However, these methods frequently neglect the correlation between abnormal data points, which hinders the precise identification of complex abnormalities and consequently reduces the accuracy of repairs. Additionally, their data simulation capabilities are constrained, impeding the capture of intricate relationships within time-series information and failing to accurately mirror the dynamic alterations in steel truss bridge structures [16; 17; 18]. In order to address the intricacies and nonlinear properties inherent in the monitoring data of steel truss girder bridges, and to ensure the precision and dependability of the rectification of anomalous monitoring data, this paper proposes the implementation of an Attention Mechanism-based Bidirectional Long Short-Term Memory Neural Network (ATT-BiLSTM) model, which integrates the Attention Mechanism (Attention) and the Bidirectional Long Short-Term Memory (BiLSTM) network. This method has been demonstrated to be more adaptive in the context of bridge monitoring data, with the capacity to more accurately identify and rectify anomalous data, thereby enhancing the efficacy of the overall repair process. BiLSTM is a model that emphasizes the temporal relationships present in the entirety of the sequence. The Attention Mechanism enables the model to dynamically adjust the level of attention allocated to different time steps, thereby facilitating more effective handling of anomalies at various points in time [19; 20]. Therefore, the present paper integrates a range of theoretical frameworks to collectively address the restoration of deflection monitoring data for primary girders of steel truss bridges. The detection of abnormalities is facilitated by the utilization of correlation heat maps and Gray correlation for pivotal measurement point data. Subsequently, the ATT-BiLSTM model is employed to rectify the abnormal data.