A self-tuning system for dam behavior modeling based on evolving artificial neural networks

Abstract

© 2016 Elsevier Ltd. All rights reserved. Most of the existing methods for dam behavior modeling presuppose temporal immutability of the modeled structure and require a persistent set of input parameters. In real-world applications, permanent structural changes and failures of measuring equipment can lead to a situation in which a selected model becomes unusable. Hence, the development of a system capable to automatically generate the most adequate dam model for a given situation is a necessity. In this paper, we present a self-tuning system for dam behavior modeling based on artificial neural networks (ANN) optimized for given conditions using genetic algorithms (GA). Throughout an evolutionary process, the system performs near real-time adjustment of ANN architecture according to currently active sensors and a present measurement dataset. The model was validated using the Grancarevo dam case study (at the Trebisnjica river located in the Republic of Srpska), where radial displacements of a point inside the dam structure have been modeled as a function of headwater, temperature, and ageing. The performance of the system was compared to the performance of an equivalent hybrid model based on multiple linear regression (MLR) and GA. The results of the analysis have shown that the ANN/GA hybrid can give rather better accuracy compared to the MLR/GA hybrid. On the other hand, the ANN/GA has shown higher computational demands and noticeable sensitivity to the temperature phase offset present at different geographical locations.