Abstract

Accurate structural behavior interpretation via finite element models is often disrupted by uncertainties, while data-driven approaches can struggle with long datasets, complex fluctuations, and the omission of essential spatio-temporal features. Additionally, these methods are limited by their reliance on interpolative predictions. This paper introduces a novel, model-free approach that integrates Moving Principal Component Analysis (MPCA), bidirectional gated recurrent units (biGRU), and attention mechanisms (AM) within an encoder–decoder (ED) architecture. MPCA reduces dimensional complexity, extracts spatial features, and consolidates them into new time-series data for subsequent analysis. The biGRU module captures past and future dependencies, while AM emphasizes most relevant information. Validated on a full-scale pedestrian bridge dataset, the presented MPCA–biGRU–AM model converges 19% faster than MPCA–GRU and reduces anomaly detection lag by 46–78%. Although its per-step processing time (8 ms) slightly exceeds that of MPCA–GRU (3 ms), the model demonstrates greater robustness across diverse damage scenarios. These results highlight its potential for real-time structural health monitoring by effectively capturing spatio-temporal patterns with computational efficiency.