###### Abstract  
Response reconstruction is essential in bridge health monitoring for recovering missing data and evaluating service status. Previous studies have focused on reconstructing responses at specific cross-sections using data from adjacent sections. To address this challenge, time-series prediction methods have been employed for response reconstruction. However, these methods often struggle with the inherent complexities of long-term time-varying traffic conditions, posing practical challenges. In this study, we analyzed the theoretical physical characteristics of high-frequency bridge dynamics within a simplified vehicle-bridge interaction (VBI) system. Our analysis revealed that the relationship between high-frequency bridge responses across different cross-sections is time-invariant and only dependent on the bridge's mode shape. This relationship remains unaffected by time-varying factors such as traffic loading and environmental conditions like air temperature. Based on these physical characteristics, we propose the backpropagation artificial neural network (BP-ANN) method for response reconstruction. The validity of these physical characteristics was confirmed through finite element models, and the effectiveness of the proposed method was demonstrated using field test data from a continuous bridge. Our verification results show that the BP-ANN method enables effective utilization of short-term monitoring data for long-term bridge health monitoring, without necessitating real-time adjustments for factors such as traffic conditions or air temperature. response reconstruction; physical characteristics; BP-ANN; vehicle-bridge interaction (VBI) system +
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creating an accurate finite element model via general model updating methods can be time-consuming. Moreover, estimating or measuring external loads may yield imprecise results, limiting the applicability of model-based approaches in practical engineering scenarios. In contrast, data-driven techniques simulate relationships [8; 9] directly using signals measured from different cross-sections. These methods typically involve deriving bridge mode shapes and using them with corresponding measured amplitudes to reconstruct responses across the entire structure. Widely used methods such as stochastic subspace identification (SSI) [10; 11] and the eigensystem realization algorithm [12; 13] have been developed and applied. However, mode shape identification poses an inverse problem, and existing methods [10; 11; 12; 13] rely on inverse calculations that can be sensitive to measurement noise. Consequently, these approaches may not be sufficiently effective for medium- and short-span bridges with low signal-to-noise ratios (SNRs), making accurate results challenging to obtain. Furthermore, ensuring the accuracy of identification processes often requires deploying a significant number of sensors. Recent years have seen significant progress in data-driven approaches, especially in machine learning techniques, which have proven advantageous in solving inverse problems [14]. In the context of bridge response reconstruction, traditional methods have been replaced by machine learning algorithms like LSTM networks [15], CNNs [16], and GANs [17]. These methods simulate direct relationships between signals using training data to generate time-series bridge responses. For instance, [PERSON] et al. [18] developed a ConvLSTM network to extract spatiotemporal features and forecast structural responses. [PERSON] [19] used Bilateral LSTM networks to correlate beam deflection and cable tension in cable-stayed bridges based on sensor data. [PERSON] [20] employed CNNs to approximate mappings among bridge responses, facilitating deflection reconstruction from alternative measurements. [PERSON] [21] proposed and validated an optimal CNN architecture for reconstructing beam-like structure responses through experiments. Additionally, [PERSON] [22] used CNNs to link structural responses with ambient air temperature.