###### Abstract  
In structural health monitoring (SHM), ensuring data completeness is critical for enhancing the accuracy and reliability of structural condition assessments. SHM data are prone to random missing values due to signal interference or connectivity issues, making precise data imputation essential. A latent factorization of tensor (LFT)-based method has proven effective for such problems, with optimization typically achieved via stochastic gradient descent (SGD). However, SGD-based LFT models and other imputation methods exhibit significant sensitivity to learning rates and slow tail-end convergence. To address these limitations, this study proposes an RMSprop-incorporated latent factorization of tensor (RLFT) model, which integrates an adaptive learning rate mechanism to dynamically adjust step sizes based on gradient magnitudes. Experimental validation on a scaled bridge accelerometer dataset demonstrates that RLFT achieves faster convergence and higher imputation accuracy compared to state-of-the-art models including SGD-based LFT and the long short-term memory (LSTM) network, with improvements of at least 10% in both imputation accuracy and convergence rate, offering a more efficient and reliable solution for missing data handling in SHM. structural health monitoring; data imputation; latent factorization of tensors; stochastic gradient descent; RMSprop-incorporated latent factorization of tensors [table]capposition=top  
exhibits limited imputation accuracy for non-low-rank structured data and high sensitivity to initialization, failing to deliver stable high-precision results. These limitations become particularly pronounced when processing temporally complex datasets. To overcome ALS limitations, convolutional neural networks (CNNs) have been introduced for data imputation tasks. CNNs automatically learn spatial features through convolutional layers, demonstrating strong nonlinear modeling capabilities to capture complex patterns and improve imputation accuracy [7; 8]. However, CNNs require highly structured spatial data and fail to fully exploit temporal dependencies when processing time-series data. To address the CNN's limitations in temporal data processing, long short-term memory (LSTM)-based imputation algorithms were proposed. As a specialized recurrent neural network, LSTM effectively captures long-term dependencies in time-series data through memory cells and gating mechanisms, making it particularly suitable for time-dependent SHM data. This enables more accurate missing data reconstruction by leveraging temporal patterns [9; 10]. Although LSTMs excel at temporal modeling, their training complexity and susceptibility to overfitting remain challenges. Consequently, deep learning methods often struggle to fully characterize the inherent spatiotemporal coupling features in bridge SHM data [11]. Building upon these developments, the stochastic gradient descent (SGD)-based latent factorization of tensor (LFT) model was proposed [12; 13; 14; 15]. This approach integrates tensor decomposition with SGD optimization to effectively handle multi-dimensional complex data relationships while maintaining scalability for large-scale dataset training. Compared to ALS, CNN, and LSTM, the LFT model demonstrates superior performance when processing high-dimensional, sparse, and structurally complex data. [PERSON] et al. [16] present a momentum-incorporated biased non-negative and adaptive LFT model, which shows higher computational efficiency and missing link prediction accuracy in efficient representation learning of dynamic weighted directed networks than existing advanced models. However, the SGD-based LFT model suffers from learning rate sensitivity and slow tail-end convergence, which impair training stability and convergence speed. Additionally, its demanding hyperparameter tuning requirements limit practical usability and operational robustness.