Local scour prediction around piers with complex geometry

Amini, Ata; Mohammad, Thamer Ahmed

doi:10.1080/1064119x.2016.1256923

Cited by 37 publications

(20 citation statements)

References 14 publications

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“…Five models (CPs) were used and experiments were performed over the whole range of possible pile cap elevations with different geometrical characteristics. More details of the data are presented in Amini et al [7,50] and Amini et al [51]. The second dataset of the experiment works were carried out at Sharif University of Technology, Tehran, Iran by Ataie-Ashtiani et al [52].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The term "complex pier" (CPs) is used in contrary to the simple pier. By definition, the complex pier is a term that defines a special kind of non-uniform pier that is comprised of a column, a pile cap, and a pile group [7]. At piers with complex geometry, due to scouring during a flood, the pile cap position with respect to the initial stream bed level changes.…”

Section: Introductionmentioning

confidence: 99%

“…At piers with complex geometry, due to scouring during a flood, the pile cap position with respect to the initial stream bed level changes. As a result, the influence of pile cap may be changed from a protective to intensifying role at the scour process when it is entirely buried and exposed to the flow, respectively [7]. Such roles increase the complicity of scour mechanisms and prediction at CPs [8].…”

Section: Introductionmentioning

confidence: 99%

“…(HEC-18) [9], the Florida Department of Transportation (FDOT) bridge mechanisms scour manual [2,10,11]. In addition, a procedure was proposed by Amini and Mohammad [7] which, based on field data, gives reasonable estimates of the scour depth at CPs [12]. For calculations of scour depth, the HEC-18 and FDOT methods apply a superposition procedure to combine the effect of each element of CPs.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

et al. 2020

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Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.

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Section: Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

et al. 2020

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“…In these studies, authors tried to find the relationship between the upper limit of the pile cap undercut elevation and the pile cap thickness. A few experimental and numerical investigations have been carried out to predict scour depth around complex bridge piers [2][3][4][5][6]. By comparing the results of theoretical and empirical equations, it was obvious that they are not accurate enough to predict scour depth.…”

Section: Introductionmentioning

confidence: 99%

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

Ghodsi

Khanjani

2020

Civ Eng J

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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.

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