Maintenance intervention predictions using entity-embedding neural networks

Data-driven decision support can substantially aid in smart and efficient maintenance planning of road bridges. However, many infrastructure managers primary rely on information obtained during visual inspection to subjectively decide on the follow-up maintenance actions. The subjective approach is likely to lack the appropriate use of inspection data and does not promise cost-effective maintenance plans. In this paper, we show that the historical and operational data, readily available at the agencies, is of vital importance and can be used effectively for the recommendations of maintenance advises for bridges. This is achieved by developing a machine learning system that is trained on the past asset management data and provide support to the decision-makers in the condition assessment, risk analysis, and maintenance planning tasks. We have evaluated several traditional learning algorithms as well as the deep neural networks with entity embedding to find the optimal predictive models in terms of predictive capability. Additionally, we have explored the multi-task learning framework that has a shared representation of related prediction tasks to develop a powerful unified model. The analysis of results shows that a unified multi-task learning model performed best for the considered problems followed by task-specific neural networks with entity embedding and class weights. The results of models are further evaluated by instance-level explanations, which provide insights about essential features and explain the importance of data attributes for a particular task.