Evaluating ChatGPT for Dam Behavior Prediction, Interpretation, and Safety Assessment: An Experimental Study

Structural dam safety control is fundamentally based on visual inspections and on the analysis of monitoring data and their comparison with the expected behavior for a given load combination, which in turn is calculated with a predictive model. In recent decades, the development of machine learning algorithms has led engineers and researchers worldwide to explore their capabilities for predicting dam behavior and supporting safety assessment. Regardless of the model used, a prediction is generally obtained together with an expected model error, which is compared with the observed value in order to support decision-making. This process, however, requires the intervention of experts in dam engineering, and particularly in the prior behavior of the structure under analysis. In recent years, the emergence of generative artificial intelligence models has opened up considerable opportunities to improve processes in various fields, including civil engineering. The reported capabilities of these models make them well suited to some tasks in the field of dam safety, particularly for developing predictive models and interpreting their results. In this work, we tested ChatGPT to assess its potential in these tasks, using the data from the ICOLD 2022 Benchmark Workshop, in which 18 teams provided their best predictions of the behavior of an arch dam. ChatGPT was provided with the same problem description and monitoring data available to the participants and was asked, through brief and neutral prompts, to propose a modeling strategy, generate predictions for six response variables for short- and long-term periods, create the corresponding source code, and interpret the results from a dam safety perspective. The complete procedure was performed twice using the same information and prompts. The results show considerable potential for increasing productivity in building and applying machine learning models, with predictive accuracy comparable to that obtained by the Benchmark participants. ChatGPT achieved a lower mean absolute error than the participants’ average in 10 (first run) and 12 (second run) of the 12 variable–period combinations, and outperformed the best participant in one case. However, the interpretation of the results was considerably weaker, with important aspects being only partially addressed or overlooked. These findings highlight the potential of ChatGPT to assist in the development of data-based predictive models, but also reveal its limitations in terms of the domain-specific knowledge required to draw conclusions on dam safety.