TL;DR: This paper investigates transfer learning (TL) as a strategy to adapt a multilayer perceptron (MLP) trained on a stiffened AS4/8552 CFRP panel to ten alternative sensor layouts, simulated via controlled sensor-index permutations grouped into three families of increasing severity.
Passive impact localization using piezoelectric sensor (PZT) networks and machine learning is an established approach for structural health monitoring of composite aerospace panels. A key practical limitation is that models trained on one sensor layout fail when deployed on a structurally similar panel with a different instrumentation arrangement, and full retraining is costly. This paper investigates transfer learning (TL) as a strategy to adapt a multilayer perceptron (MLP) trained on a stiffened AS4/8552 CFRP panel to ten alternative sensor layouts, simulated via controlled sensor-index permutations grouped into three families of increasing severity: intra-column swaps, intra-row swaps, and full-plate reflections. Six TL strategies, from output-head-only retraining to full fine-tuning, are benchmarked at five adaptation data fractions against a from-scratch baseline. Results show that full fine-tuning is the only strategy that consistently recovers baseline localization accuracy (25.7~mm mean Euclidean error) across all permutation families, requiring as little as 5\% of the adaptation dataset for mild and moderate shifts and 25\% for severe full-plate reflections. Partial fine-tuning strategies fail to recover acceptable accuracy at any data fraction, due to the globally distributed nature of sensor-layout encoding in hand-feature MLPs. These findings provide practical guidelines for redeploying impact localization models across panels with varying sensor configurations.
The ever-growing need for renewable energy has driven the development of increasingly large offshore wind turbines. Alongside improved design codes and changing control strategies, this has led to fatigue becoming an operational concern. Farm operators require information about t…
TL;DR: This study demonstrates that transfer learning enables efficient adaptation to real-world conditions, offering a cost-effective and scalable solution for data-driven SHM.
While neural networks represent a promising approach for evaluating sensor data to assess damage presence, location and severity, large amounts of data are required for training. However, the generation of experimental data is both labor-intensive and costly. Transfer learning is…
A physics-informed neural network (PINN) is developed for modeling time-harmonic Lamb-wave excitation in a two-dimensional elastic waveguide under surface loading. The displacement and stress fields are represented by the network, and its trainable weights are determined by enfor…
TL;DR: This study provides a validated pathway toward scalable, real-time, and feature-free SHM systems for deployment in operational bridge networks, supporting continuous monitoring, early damage detection, and maintenance decision-making in the future.
Current Structural Health Monitoring (SHM) systems remain constrained by their reliance on handcrafted feature extraction and centralized cloud processing, limiting their real-time performance, scalability, and deployment on resource-constrained infrastructure. This study seeks t…
TL;DR: An input-robust hybrid physics informed neural network (rHPINN) framework is proposed that integrates physics-based system dynamics with the temporal learning capability of HPINN, allowing accurate estimation of system states and spatial health parameters without input force measurements.
System identification (SI) is critical for ensuring the reliability of structural and mechanical components across engineering applications. Traditional model-based SI methods often struggle with complex dynamics and the scarcity of accurate physical models, while purely data-dri…
TL;DR: The results demonstrate that PINN achieves more accurate and stable full-field vibration reconstructions than conventional PINNs, particularly under conditions involving high-frequency modes, and highlights the potential of hybrid data-physics neural frameworks as an efficient and reliable approach for solving complex PDE-governed dynamical systems.
Physics-informed neural networks (PINNs) have emerged as a powerful paradigm in scientific machine learning by embedding governing physical laws into neural network training through loss functions. They have demonstrated remarkable success in solving various forward and inverse p…