scholarly journals Evaluation of Harmony Search Optimization to Predict Local Scour Depth around Complex Bridge Piers

2018 ◽  
Vol 4 (2) ◽  
pp. 402 ◽  
Author(s):  
Habibeh Ghodsi ◽  
Mohammad Javad Khanjani ◽  
Ali Asghar Beheshti
Keyword(s):  
Experimental Data ◽  
Search Algorithm ◽  
Harmony Search ◽  
Local Scour ◽  
Bridge Piers ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Scour Depth ◽  
Empirical Equations ◽  
Statistical Indices

One of the main causes of bridge collapse may be flood flow scour near piers. Several experimental and local field investigations were carried out to study scour depth. However, existing empirical equations do not commonly provide accurate scour prediction due to the complexity of the scour process. Physical and economic considerations often lead to bridge foundation constructs which included a pier column based on a pile cap supported by an array of piles. Piers with this configuration are referred to as complex piers. A few studies have been done on complex bridge pier scour depth estimation. Such efforts may be classified into theoretical and empirical equations. This paper investigates local scour around complex bridge piers by using harmony search algorithm under clear water conditions. Statistical indices such as the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were used to evaluate the performance of these methods. By designing laboratory tests, 82 experimental data points were measured by authors. Also 615 experimental data sets with the same measured experimental conditions were collected from published literature and used for optimization. The results show that the developed HS model can predict scour depth better than other equations according to statistical indices.

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

2020 ◽  
Vol 6 (1) ◽  
pp. 69-84 ◽  
Author(s):  
Habibeh Ghodsi ◽  
Mohammad Javad Khanjani
Keyword(s):  
Local Scour ◽  
Bridge Pier ◽  
Bridge Piers ◽  
Group Method ◽  
Scour Depth ◽  
Empirical Equations ◽  
Depth Prediction ◽  
Pile Cap ◽  
Data Points ◽  
Statistical Criteria

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.

scholarly journals Nose-Angle Bridge Piers as Alternative Countermeasures for Local Scour Reduction

2018 ◽  
Vol 13 (2) ◽  
pp. 110-120 ◽  
Author(s):  
Ibtesam Abudallah Habib ◽  
Wan Hanna Melini Wan Mohtar ◽  
Atef Elsaiad ◽  
Ahmed El-Shafie
Keyword(s):  
Froude Number ◽  
Local Scour ◽  
Bridge Piers ◽  
Scour Depth ◽  
Skew Angle ◽  
Flow Angle ◽  
Sediment Size ◽  
Low Froude Number ◽  
Skew Angles ◽  
Depth Reduction

This study investigates the performance nose-angle piers as countermeasures for local scour reduction around piers. Four nose angles were studied, i.e., 90°, 70°, 60° and 45° and tested in a laboratory. The sediment size was fixed at 0.39 mm whereas the flow angle of attack (or skew angle) was varied at four angles, i.e., skew angles, i.e., 0°, 10°, 20° and 30°. Scour reduction was clear when decreasing nose angles and reached maximum when the nose angle is 45°. Increasing the flow velocity and skew angle was subsequently increasing the scour profile, both in vertical and transversal directions. However, the efficiency of nose angle piers was only high at low Froude number less than 0.40 where higher Froude number gives minimal changes in the maximum scour depth reduction. At a higher skew angle, although showed promising maximum scour depth reduction, the increasing pier projected width resulted in the increase of transversal lengths.

scholarly journals Numerical and experimental investigation for the effect of permeability of spur dikes on local scour

2019 ◽  
Vol 21 (2) ◽  
pp. 335-342 ◽  
Author(s):  
Riham Mohsen Ezzeldin
Keyword(s):  
Experimental Data ◽  
Numerical Model ◽  
Empirical Equation ◽  
Coefficient Of Determination ◽  
Scour Depth ◽  
Performance Indices ◽  
Relative Maximum ◽  
Spur Dikes ◽  
International River ◽  
Acceptable Agreement

Abstract The effect of using permeable spur dikes on the produced maximum scour depth compared to that of solid spur dikes is numerically investigated. The numerical model used for such purpose is the Nays-2DH model of the International River Interface Cooperative (iRIC) software package for bed and bank erosion. The model results are verified using the experimental data collected in this study by conducting experiments on five different models of spur dikes having different opening ratios. Using the statistical performance indices, the root mean square error and the coefficient of determination, the results showed an acceptable agreement between the numerical model results for the relative maximum scour depth defined by the ratio of the maximum scour depth to the flow depth and their corresponding observed values. A new empirical equation using nonlinear regression is developed using the experimental data collected in this study and tested with another existing empirical equation available in the literature for their accuracy in determining the relative maximum scour depth.

Maximum local scour depth at bridge piers and abutments

1998 ◽  
Vol 36 (2) ◽  
pp. 183-198 ◽  
Author(s):  
J.K. Kandasamy ◽  
B.W. Melville
Keyword(s):  
Local Scour ◽  
Bridge Piers ◽  
Scour Depth

Three-dimensional numerical simulation of local scour in the vicinity of circular side-by-side bridge piers with ice cover

2020 ◽  
Author(s):  
Mohammad Reza Namaee ◽  
Jueyi Sui ◽  
Yongsheng Wu ◽  
Natalie Linklater
Keyword(s):  
Laboratory Experiments ◽  
Transport Model ◽  
Numerical Models ◽  
Three Dimensional ◽  
Ice Cover ◽  
Local Scour ◽  
Bridge Piers ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Cold Regions

Local scour around piers is one of the primary causes of collapse of bridges that cross rivers. The most severe scouring occurs in cold regions where ice cover significantly changes the velocity profile. Having an accurate estimation of the maximum scour depth around bridge piers, especially in cold regions, is necessary for a safer design of piers. In this study, 3-D numerical models are compared to laboratory experiments to examine the process of local scour around bridge piers with and without smooth and rough ice cover. By using the equation of Meyer-Peter Müller, the sediment transport model is validated to approximate the transport of the sediment particles. Numerical results showed good agreements with experimental observations where the maximum scour depth and Turbulent Kinetic Energy (TKE) around bridge piers were the highest under rough ice cover conditions.

scholarly journals Local Scour for Vertical Piles in Steady Currents: Review of Mechanisms, Influencing Factors and Empirical Equations

2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Bingchen Liang ◽  
Shengtao Du ◽  
Xinying Pan ◽  
Libang Zhang
Keyword(s):  
Influencing Factors ◽  
Local Scour ◽  
Future Research ◽  
Scour Depth ◽  
Empirical Equations ◽  
Research Directions ◽  
Fundamental Knowledge ◽  
Future Research Directions ◽  
Flow Intensity ◽  
Bridge Failure

Scour induced by currents is one of the main causes of the bridge failure in rivers. Fundamental knowledge and mechanisms on scour processes due to currents are often taken as a basis for scour studies, which are the focus of this review. Scour development induced by waves and in combined wave–current conditions are also briefly discussed. For the design of structure foundations, the maximum scour depths need to be estimated. The mechanisms of local scour and predictions of maximum local scour depths have been studied extensively for many years. Despite the complexity of the scour process, a lot of satisfying results and progresses have been achieved by many investigators. In order to get a comprehensive review of local scour for vertical piles, major progresses made by researchers are summarized in this review. In particular, maximum scour depth influencing factors including flow intensity, sediments, pile parameters and time are analyzed with experimental data. A few empirical equations referring to temporary scour depth and maximum scour depth were classified with their expressing forms. Finally, conclusions and future research directions are addressed.

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

2015 ◽  
Author(s):  
Ehsan Sarshari ◽  
Philippe Mullhaupt
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Empirical Models ◽  
Local Scour ◽  
Bridge Piers ◽  
Local Conditions ◽  
The Neural Network ◽  
Artificial Neural

Scour can have the effect of subsidence of the piers in bridges, which can ultimately lead to the total collapse of these systems. Effective bridge design needs appropriate information on the equilibrium depth of local scour. The flow field around bridge piers is complex so that deriving a theoretical model for predicting the exact equilibrium depth of local scour seems to be near impossible. On the other hand, the assessment of empirical models highly depends on local conditions, which is usually too conservative. In the present study, artificial neural networks are used to estimate the equilibrium depth of the local scour around bridge piers. Assuming such equilibrium depth is a function of five variables, and using experimental data, a neural network model is trained to predict this equilibrium depth. Multilayer neural networks with backpropagation algorithm with different learning rules are investigated and implemented. Different methods of data normalization besides the effect of initial weightings and overtraining phenomenon are addressed. The results show well adoption of the neural network predictions against experimental data in comparison with the estimation of empirical models.

Application of Neural Networks To Simulate the Growth Profile of Lactic Acid Bacteria in Green Olive Fermentation

2007 ◽  
Vol 70 (8) ◽  
pp. 1909-1916 ◽  
Author(s):  
EFSTATHIOS Z. PANAGOU ◽  
CHRYSOULA C. TASSOU ◽  
ELEFTHERIOS K. A. SARAVANOS ◽  
GEORGE-JOHN E. NYCHAS
Keyword(s):  
Experimental Data ◽  
Lactic Acid ◽  
Lactic Acid Bacteria ◽  
Multilayer Perceptron ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Data Set ◽  
Growth Profile ◽  
Multilayer Perceptron Network ◽  
Green Olive

The growth profile of five strains of lactic acid bacteria (Lactobacillus plantarum ACA-DC 287, L. plantarum ACA-DC 146, Lactobacillus paracasei ACA-DC 4037, Lactobacillus sakei LQC 1378, and Leuconostoc mesenteroides LQC 1398) was investigated in controlled fermentation of cv. Conservolea green olives with a multilayer perceptron network, a combined logistic-Fermi function, and a two-term Gompertz function. Neural network training was based on the steepest-descent gradient learning algorithm. Model performance was compared with the experimental data with five statistical indices, namely coefficient of determination (R2), root mean square error (RMSE), mean relative percentage error (MRPE), mean absolute percentage error (MAPE), and standard error of prediction (SEP). The experimental data set consisted of 125 counts (CFU per milliliter) of lactic acid bacteria during the green olive fermentation process for up to 38 days (5 strains × 25 sampling days). For model development, a standard methodology was followed, dividing the data set into training (120 data) and validation (25 data) subsets. Our results demonstrated that the developed network was able to model the growth and survival profile of all the strains of lactic acid bacteria during fermentation equally well with the statistical models. The performance indices for the training subset of the multilayer perceptron network were R2 = 0.987, RMSE = 0.097, MRPE = 0.069, MAPE = 0.933, and SEP = 1.316. The relevant mean values for the logistic-Fermi and two-term Gompertz functions were R2 = 0.981 and 0.989, RMSE = 0.109 and 0.083, MRPE = 0.026 and 0.030, MAPE = 1.430 and 1.076, and SEP = 1.490 and 1.127, respectively. For the validation subset, the network also gave good predictions (R2 = 0.968, RMSE = 0.149, MRPE = 0.100, MAPE = 1.411, and SEP = 2.009).

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