Prediction of scour in plunge pools below outlet bucket using artificial intelligence

Accurate prediction of sediment scour hole dimensions downstream of hydraulic structures, e.g. the outlet bucket, is a complex and not straight forward engineering problem encountered worldwide. Dimensions of a scour hole are usually determined by empirical equations which their validation is limite...

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Bibliographic Details
Published in:KSCE journal of civil engineering 2016, 20(7), , pp.2981-2990
Main Authors: Lashkar-Ara, B., Ghotbi, S. M. H., Najafi, L.
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial intelligence
Artificial neural networks
Civil Engineering
Computation
Empirical equations
Engineering
Equations
Formulas (mathematics)
Geotechnical Engineering & Applied Earth Sciences
Hydraulic structures
Industrial Pollution Prevention
Learning theory
Mathematical models
Neural networks
Parameters
Scour
Scouring
Water Engineering
토목공학
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Summary:Accurate prediction of sediment scour hole dimensions downstream of hydraulic structures, e.g. the outlet bucket, is a complex and not straight forward engineering problem encountered worldwide. Dimensions of a scour hole are usually determined by empirical equations which their validation is limited by experimental conditions. As constructing physical models has its own difficulty, determining of scour hole parameters has been applied in this paper for a collection of previous experimental studies. Two artificial intelligence techniques (ANN & ANFIS) are used and the results are compared with empirical equation for maximum scour holes using nonlinear regression method. Artificial Neural Network (ANN) simply represents interconnection of neurons, each of which carries out the task of combining the input, determining its strength by comparing the combination and finding out the result. On the other hand, ANFIS is a hybrid scheme which uses the learning capability of the ANN to derive the fuzzy rules with membership functions. The results showed that maximum error caused by applying ANFIS techniques in estimating scour hole dimensions was 5.2 percent while the error in neural network model was 10.38 percent. The significance of different parameters was discussed and a simple, innovative formula was proposed. This formula is an interesting tool for the engineering community due to its preferences for estimating the parameters of complex phenomena like erosion procedures. It has been established that scour estimations could be improved if soft computation is used in place of the traditional formulae.
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-016-1523-0