Event-Triggered Hybrid State-Space LSTM-Liquid Neural Network for Multi-Source Seismic Vulnerability State Assessment of Ancient Halls

We propose a novel hybrid architecture that integrates a continuous-time Liquid Neural Network with an optimized Long Short-Term Memory network for seismic vulnerability state assessment of ancient halls. The proposed system processes multi-source structural monitoring data, including acceleration, strain, displacement, temperature, and humidity signals. A central challenge in this domain is the long-term state drift that occurs when continuous-time models are exposed to prolonged seismic events or sequences of aftershocks. To address this issue, we introduce a learnable event-triggered discrete reset mechanism that monitors a prediction error signal and an auxiliary drift accumulation variable. When a trigger condition is met, the Liquid Neural Network hidden state is reinitialized to a learned baseline state, thereby preventing divergence from physically plausible structural dynamics. This reset mechanism is inspired by hybrid small-gain and impulsive control frameworks and provides rigorous stability guarantees. The continuous-time dynamics are modeled as a neural ordinary differential equation with a sinusoidal activation function, solved via a fixed-step Runge–Kutta integrator. The discrete-time pathway employs a two-layer LSTM with Bayesian-optimized hyperparameters. A learned attention mechanism fuses the hidden states from both pathways, and the combined representation is passed through a feedforward network to produce a four-class seismic vulnerability index. The entire system is trained end-to-end with an adaptive loss function that balances reconstruction accuracy, drift penalization, and classification performance. We first pre-train the model on synthetic data generated from a calibrated finite element model of a representative ancient hall in Rucheng, Hunan. Transfer learning then fine-tunes the model on real monitoring data. Our approach replaces conventional fragility-curve-based estimates with a data-driven, adaptive vulnerability assessment that is site-specific and robust to non-stationary excitation. The hybrid switched-system framework therefore offers a principled solution to the state drift problem while maintaining the expressive power of continuous-time neural dynamics.