| Civil Engineering |
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| Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods |
| Danial Behnia, Kaveh Ahangari, Ali Noorzad, Sayed Rahim Moeinossadat |
| Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran; Faculty of Water and Environmental Engineering, Power and Water University of Technology, Hakimieh, Tehran, Iran |
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Abstract This paper deals with the estimation of crest settlement in a concrete face rockfill dam (CFRD), utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the body of the dam can develop during the first impoundment of the reservoir. Although there is vast experience worldwide in CFRD design and construction, few accurate experimental relationships are available to predict the settlement in CFRD. The goal is to advance the development of intelligent methods to estimate the subsidence of dams at the design stage. Due to dam zonification and uncertainties in material properties, these methods appear to be the appropriate choice. In this study, the crest settlement behavior of CFRDs is analyzed based on compiled data of 24 CFRDs constructed during recent years around the world, along with the utilization of gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) methods. In addition, dam height (H), shape factor (Sf), and time (t, time after first operation) are also assessed, being considered major factors in predicting the settlement behavior. From the relationships proposed, the values of R2 for both equations of GEP (with and without constant) were 0.9603 and 0.9734, and for the three approaches of ANFIS (grid partitioning (GP), subtractive clustering method (SCM), and fuzzy c-means clustering (FCM)) were 0.9693, 0.8657, and 0.8848, respectively. The obtained results indicate that the overall behavior evaluated by this approach is consistent with the measured data of other CFRDs.
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Received: 09 November 2012
Published: 01 August 2013
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