Application of Improved GMDH Models to Predict Local Scour Depth at Complex Bridge Piers

Habibeh Ghodsi,Mohammad Javad Khanjani

doi:10.28991/cej-2020-03091454

Habibeh Ghodsi, Mohammad Javad Khanjani

Open Access

https://doi.org/10.28991/cej-2020-03091454

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Abstract

Scour depth prediction is a vital issue in bridge pier design. Recently, good progress has been made in the development of artificial intelligence (AI) to predict scour depth around hydraulic structures base such as bridge piers. In this study, two hybrid intelligence models based on combination of group method of data handling (GMDH) with harmony search algorithm (HS) and shuffled complex evolution (SCE) have been developed to predict local scour depth around complex bridge piers using 82 laboratory data measured by authors and 615 data points from published literature. The results were compared to conventional GMDH models with two kinds of transfer functions called GMDH1 and GMDH2. Based upon the pile cap location, data points were divided into three categories. The performance of all utilized models was evaluated by the statistical criteria of R, RMSE, MAPE, BIAS, and SI. Performances of developed models were evaluated by experimental data points collected in laboratory experiments, together with commonly empirical equations. The results showed that GMDH2SCE was the superior model in terms of all statistical criteria in training when the pile cap was above the initial bed level and completely buried pile cap. For a partially-buried pile cap, GMDH1SCE offered the best performance. Among empirical equations, HEC-18 produced relatively good performances for different types of complex piers. This study recommends hybrid GMDH models, as powerful tools in complex bridge pier scour depth prediction.

Highlights

Physical and economic considerations may lead to complex bridge pier design
There could be summarized that the group method of data handling (GMDH)-type polynomial networks influence be contemporary artificial neural network algorithms with several other advantages: They offer adaptive network representations that can be tailored to the given task; They learn the weights rapidly in a single step by standard ordinary least squares (OLS) fitting which eliminates the need to search for their values, and which guarantees finding locally good weights due to the reliability of the fitting technique; Those polynomial networks feature sparse connectivity which means that the best discovered networks can be trained fast [44]
The capability of hybrid GMDH models in predicting the pier scour depth was comparatively investigated by a large set of experimental scour data

Summary

Introduction

Physical and economic considerations may lead to complex bridge pier design. Complex piers are commonly constructed of columns and pile caps which are founded on pile groups. Different artificial intelligence approaches such as artificial neural networks (ANN), adaptive Neuro-Fuzzy inference systems (ANFIS), genetic programming (GP), gene-expression programming (GEP), support vector machines (SVM), model trees (MT), evolutionary polynomial regressions (EPR), POS-SVM, multivariate adaptive regression splines (MARS), and self-adaptive extreme learning machines (SAELM) have been applied to predict the local scour depth around hydraulic structures [7,8,9,10,11,12,13,14,15,16] Among these soft computing techniques, group method of data handling (GMDH) methods were widely applied to predict the local scour depth around bridge piers and abutments, downstream of ski-jump bucket spillways, downstream of grade-control structures, and below pipelines induced currents and waves [17,18,19,20]. There could be summarized that the GMDH-type polynomial networks influence be contemporary artificial neural network algorithms with several other advantages: They offer adaptive network representations that can be tailored to the given task; They learn the weights rapidly in a single step by standard ordinary least squares (OLS) fitting which eliminates the need to search for their values, and which guarantees finding locally good weights due to the reliability of the fitting technique; Those polynomial networks feature sparse connectivity which means that the best discovered networks can be trained fast [44]

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