Delamination is a prevalent type of damage in composite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in composite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameterized finite element model of the composite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying sizes and locations. The radial basis function neural network surrogate model is then constructed based on the numerical simulation results and strain responses captured from the distributed fiber optic sensors. Finally, a multi-island genetic algorithm is employed for global optimization to identify the size and location of the damage. The efficacy of the proposed method is validated through numerical examples and experiment studies, examining the correlations between damage location, damage size, and strain responses. The findings confirm that the model updating technique, in conjunction with distributed fiber optic sensors, can precisely identify delamination in composite structures.

Figure 1. Multi-island genetic algorithm flow chart

Figure 2. Surrogate model curve diagram

Figure 3. Strain comparison diagram before and after correction

Figure 4. Optical fiber path layout diagram

DOI:https://doi.org/10.32604/sdhm.2024.051393