Anomaly detection of massive bridge monitoring data through multiple transfer learning with adaptively setting hyperparameters

Abstract

Civil infrastructure relies heavily on structural health monitoring systems. However, these systems often encounter challenges due to sensor failures and environmental damage. Consequently, numerous anomalous data points are generated, significantly distorting the accuracy of structural safety assessments. While deep neural networks have emerged as a promising tool for efficiently identifying abnormal data, the meticulous optimization of hyperparameters during training remains a challenge. To address this challenge, this paper introduces a novel approach termed multiple transfer learning, designed to continually enhance a model's classification performance without the need for meticulous hyperparameter configurations. This methodology achieves adaptive training by iteratively migrating across bridge anomaly datasets, bypassing the need for elaborate hyperparameter setting. In this study, five distinct hyperparameter working conditions are established and evaluated to validate the effectiveness of the multiple transfer learning method. The findings highlight the robustness of this approach, demonstrating that multiple transfer learning achieves satisfactory recognition accuracy levels irrespective of the initial hyperparameter setting during network model training. This method circumvents the need for continuous hyperparameters optimization, enabling the adaptive detection of abnormal bridge data.