Simultaneous Prediction of Causes and Consequences in Hydrogen-Related Accidents Using Transformer-Based Multi-Task Learning

Hydrogen is emerging as a critical alternative in the global transition to sustainable energy.However, its characteristics -such as high flammability, rapid diffusion, and low ignition energy -pose significant safety challenges in the operation of hydrogen systems.To prevent future accidents, a thorough understanding of hydrogen-related incidents is crucial.This work proposes a novel approach utilizing transformer-based multi-task learning to simultaneously predict accident causes and consequences from unstructured accident narratives within the Hydrogen Incident and Accident Database (HIAD 2.1).By fine-tuning a pre-trained BERT model, the accident narratives are processed to identify root causes while estimating the potential consequences.The proposed multitask model uses shared representations, enabling efficient learning of both causal and consequence patterns from historical hydrogen accidents.Our results show that multi-task learning enhances the model's ability to generalize across multiple prediction tasks, outperforming single-task models.By suggesting likely causes and consequences, this methodology supports risk identification and assessment, contributing to the safer adoption of hydrogen technologies.