Structural health monitoring of metal structures using an improved carbon nanotube bucky paper sensor and LSTM neural network

In this paper, an improved fabrication method is presented for fabricating carbon nanotube (CNT) based multi-functional bucky paper (CNT-BP) sensors that will be primarily used for adaptive sensing in structural health monitoring applications. A large number of BPs were fabricated using multi-walled CNTs with varying methanol-CNT compositions, sonication times, temperatures, curing durations, membrane thicknesses, and electrode placements to determine the optimal configuration for large-scale production. The obtained optimal configuration of the ingredients that yields an adequate sensitivity and ductility of the CNT-BP was then employed for measuring the crack propagation behavior in the fatigued samples. Further, a long short-term memory (LSTM)-based neural network was proposed for prognosis in a metallic plate with fatigue crack propagation. The actual crack lengths of the fatigue crack obtained by the high-speed digital camera were correlated with that predicted by the CNT-BP-based model and LSTM, showing good agreement. Thus, the present study demonstrates that the proposed improved method of CNT-BP is highly efficient in the diagnosis and prognosis of fatigue cracks in metallic structures.