Hierarchical Feature Fusion Reconstruction of Missing Acceleration Responses for Offshore Platforms

Missing acceleration responses caused by sensor faults and transmission interruptions can compromise the reliability of structural health monitoring for offshore platforms. To address continuous data loss, this study proposes a hierarchical feature fusion reconstruction (HFFR) method. HFFR organizes response information at three complementary levels. Multi-scale local response morphology encoding captures local dynamic characteristics over different temporal neighborhoods, bidirectional temporal modeling exploits response evolution before and after the missing segment, and correlation-based adaptive feature weighting emphasizes temporal information that is more relevant to reconstruction. The method is validated using numerical acceleration responses from a jacket offshore platform under multiple wave-current conditions and long-term measured responses from the Hardanger Bridge. Ablation studies confirm the complementary contributions of the three information levels, while comparisons with CNN-LSTM and BiLSTM-SA demonstrate improved reconstruction accuracy and better preservation of response characteristics. Time-domain and frequency-domain analyses further show that HFFR can recover major waveform variations and principal dynamic frequency components. Additional measured-data experiments under different continuous missing ratios demonstrate stable performance as data loss increases. These results demonstrate the effectiveness of HFFR for continuous missing-response reconstruction across the evaluated numerical and measured datasets, while more complex missing patterns and noise conditions remain to be investigated.