A neural network surrogate model-based time-domain sparse Bayesian learning method for CA mortar void identification of slab track system

Vollständiger Abstract

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Concealed voids of the cement-emulsified asphalt (CA) mortar layer in the slab track system pose considerable structural risks to high-speed railways, necessitating early detection. Finite element model updating methods offer global structural health assessment but are computationally demanding due to numerous simulations in high-dimensional optimization. To overcome these limitations, a novel damage identification method integrating a parameterized neural network surrogate model with time-domain sparse Bayesian learning is proposed. The surrogate model combines convolutional neural networks and long short-term memory networks, employing a dual-channel architecture for static structural parameters and dynamic impacts, further enhanced by position encoding and residual learning to predict acceleration sequences. Parameter optimization in this framework is performed with an improved particle swarm optimization algorithm with a local search strategy. The surrogate model achieved predictions with an average mean squared error of 0.0022 and an R -squared value of 0.913. The feasibility of the proposed method was validated on a scaled model of the slab track system. The numerical and experimental results demonstrate that the proposed method can successfully identify the location and severity of the CA mortar void and quantify the associated uncertainties. Furthermore, the proposed method significantly enhances computational efficiency, which offers a viable technical foundation for structural health monitoring of the slab track system.