Multitask learning for simultaneous damage detection and localization in beam-like structures using fibre-optic vibration sensor

Abstract

Structural health monitoring based on vibration signal analysis has demonstrated itself as a powerful technique for preventing structural damage, since only the dynamic response of structural elements is required. Moreover, in beam-like structures, damage alters the system’s vibration characteristics, providing valuable information for damage assessment. In this work, a light-intensity-modulated fibre-optic reflective vibration sensing system is combined with a multitask learning (MTL) framework for simultaneous classification and localization of damage in cantilever beams. The proposed framework adopts a unified neural network architecture that learns multiple tasks simultaneously and uses the spectral properties of the optical fibre vibration response as input features. Specifically, it performs 12-class structural-condition classification and mechanism-specific regression of crack- and wear-type damage locations. Under the evaluated acquisition-level protocol, the multitask framework achieved 96.4% classification accuracy and mean absolute localization errors of 1.16 mm for crack-type damage and 1.52 mm for wear-type damage. Performance was compared with multiple independently trained single-task baselines, including multilayer perceptron (MLP), support vector machine, k-nearest neighbours, and decision tree models, together with additional linear and probabilistic approaches. Across ten matched random seeds, comparison with the strongest overall single-task baseline, the MLP, showed a statistically significant but small classification advantage and larger reductions in localization error. The observed performance reflects generalization to new vibration acquisitions under the experimental conditions represented in the dataset, rather than to entirely unseen specimens, supporting the framework’s potential for simultaneous damage classification and localization in beam-like structures.