SOLDIER: SOLution for Dam behavior Interpretation and safety Evaluation with boosted Regression trees

Abstract

Decision making in dam safety is fundamentally based on the comparison between the predictions of a behavior model and the records of the monitoring system. Traditionally, simple linear regression models have been used. Recently, models based on machine learning are being explored, which generally offer greater precision –therefore, greater capacity for detecting anomalies –, higher flexibility and versatility. We have developed an interactive application based on R-Shiny to generate models based on boosted regression trees, evaluate their accuracy and analyze the effect of predictor variables on the system response. This allows for identifying changes in dam behavior, detecting potential anomalies and better understanding the effect of the loads on the structure. The availability of the software will contribute to the penetration of machine learning techniques in the dam engineering sector and will open the door to its use in structural health monitoring for other civil infrastructures.

Keywords

• Machine learning
• Dams
• Structural health monitoring
• Boosted regression trees
• Shiny