Evaluating the accuracy of predicted bridge condition using machine learning: the role of condition history

Effective maintenance decisions for bridges depend on accurate performance prediction. Machine learning (ML) models use historical bridge performance data to learn and predict performance. However, in many agencies, the condition history of bridges is limited and does not go beyond a few years. The question, therefore, is, to what extent does condition history help us make better predictions? To address this question, a ML model was developed that analysed more than 600,000 bridge decks with 27 years of condition history. Two data selection methods were designed: non-overlapping and overlapping data. The non-overlapping data are typically used to train the model. The overlapping data introduced in this study uses the data more efficiently for model training recognising that strings of historical data convey more information. Longer term predictions were found to be positively impacted by every additional year of condition history. Short-term condition prediction (one or two years) does not need significant historical data. It was also found that overlapping data, compared to non-overlapping data, produced larger training samples and had higher prediction accuracy in the majority of experiments, but at the cost of higher running time due to a larger sample size.

Automated summary

Effective maintenance decisions for bridges rely on accurate performance prediction using machine learning models. Long-term predictions benefit from additional years of condition history, while short-term predictions do not require significant historical data. Overlapping data, despite the longer running time, produces larger training samples and higher prediction accuracy compared to non-overlapping data.