Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers

2007 ◽  
Vol 38 (2) ◽  
pp. 102-111 ◽  
Author(s):  
S. Mohyeddin Bateni ◽  
Dong-Sheng Jeng ◽  
Bruce W. Melville
Keyword(s):  
Neural Networks ◽  
Time Dependent ◽  
Bridge Piers ◽  
Scour Depth ◽  
Bayesian Neural Networks

scholarly journals Prediction of local scour depth at bridge piers under clear-water and live-bed conditions: comparison of literature formulae and artificial neural networks

2011 ◽  
Vol 13 (4) ◽  
pp. 812-824 ◽  
Author(s):  
E. Toth ◽  
L. Brandimarte
Keyword(s):  
Neural Networks ◽  
Sediment Transport ◽  
Field Data ◽  
Local Scour ◽  
Bridge Piers ◽  
Validation Dataset ◽  
Scour Depth ◽  
Clear Water ◽  
Transport Mode ◽  
Artificial Neural

The scouring effect of the flowing water around bridge piers may undermine the stability of the structure, leading to extremely high direct and indirect costs and, in extreme cases, the loss of human lives. The use of Artificial Neural Network (ANN) models has been recently proposed in the literature for estimating the maximum scour depth around bridge piers: this study aims at further investigating the potentiality of the ANN approach and, in particular, at analysing the influence of the experimental setting (laboratory or field data) and of the sediment transport mode (clear water or live bed) on the prediction performances. A large database of both field and laboratory observations has been collected from the literature for predicting the maximum local scour depth as a function of a parsimonious set of variables characterizing the flow, the sediments and the pier. Neural networks with an increasing degree of specialization have been implemented – using different subsets of the calibration data in the training phase – and validated over an external validation dataset. The results confirm that the ANN scour depths' predictions outperform the estimates obtained by empirical formulae conventionally used in the literature and in the current engineering practice, and demonstrate the importance of taking into account the differences in the type of available data – laboratory or field data – and the sediment transport mode – clear water or live bed conditions.

scholarly journals Efficiency of Artificial Neural Networks in Determining Scour Depth at Composite Bridge Piers

2019 ◽  
Author(s):  
Ata Amini ◽  
Shahriar Hamidi ◽  
Marlinda Malek ◽  
Thamer Mohammad ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bridge Piers ◽  
Scour Depth ◽  
Ann Model ◽  
Composite Bridge ◽  
Artificial Neural ◽  
Bridge Failure ◽  
Propagation Network

Scouring is the most common cause of bridge failure. This study was conducted to evaluate the efficiency of the Artificial Neural Networks (ANN) in determining scour depth around composite bridge piers. The experimental data, attained in different conditions and various pile cap locations, were used to obtain the ANN model and to compare the results of the model with most well-known empirical, HEC-18 and FDOT, methods. The data were divided into training and evaluation sets. The ANN models were trained using the experimental data, and their efficiency was evaluated using statistical test. The results showed that to estimate scour at the composite piers, feedforward propagation network with three neurons in the hidden layer and hyperbolic sigmoid tangent transfer function was with the highest accuracy. The results also indicated a better estimation of the scour depth by the proposed ANN than the empirical methods.

scholarly journals The impact of cables on local scouring of bridge piers using experimental study and ANN, ANFIS algorithms

2021 ◽  
Author(s):  
Rasoul Daneshfaraz ◽  
Masoud Abam ◽  
Manouchehr Heidarpour ◽  
Salim Abbasi ◽  
Mehran Seifollahi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Experimental Study ◽  
Twist Angle ◽  
Computing Methods ◽  
Bridge Piers ◽  
Scour Depth ◽  
Type I ◽  
Type Ii ◽  
Soft Computing Methods ◽  
The Impact

Abstract The main purpose of this study is to analyze and predict scour depth and hydraulic performance of piers using soft computing methods and to estimate scour depths using artificial neural networks and ANFIS methods. In the present study, three situations were studied: a rectangular pier without a cable (type I), a rectangular pier with a cable that has a diameter equal to 10% of the pier diameter, and a cable twist angle of 15 degrees (type II), and a rectangular pier with a cable 15% of the pier diameter and an angle of twist of 12 degrees (type III). Tests were carried out with different flow-approach angles: zero, 5, 10, and 15 degrees. Dimensional analysis based on the π Buckingham method was performed. Then, the effect of different parameters, and their importance for estimating scour depth was investigated. Piers with an angle of 15 degrees with respect to the direction of flow had the greatest depth of scouring. Cables can reduce scour depths at this angle; for the second and third classes of piers, the scouring is 10 and 22% compared to the first pier classification. For the second type of pier, angles of 5, 10, and 15 degrees led to increases in scouring depths of 3, 21, and 37% compared to the zero-angle situation.

Evaluation of Selected Pier-Scour Equations Using Field Data

1996 ◽  
Vol 1523 (1) ◽  
pp. 186-195 ◽  
Author(s):  
Mark N. Landers ◽  
David S. Mueller
Keyword(s):  
Field Measurements ◽  
Bridge Piers ◽  
Critical Ratio ◽  
Scour Depth ◽  
Flow Depth ◽  
Clear Water ◽  
Pier Scour ◽  
Logarithmic Space ◽  
State Highway ◽  
Federal Highway

Field measurements of channel scour at bridges are needed to improve the understanding of scour processes and the ability to accurately predict scour depths. An extensive data base of pier-scour measurements has been developed over the last several years in cooperative studies between state highway departments, the Federal Highway Administration, and the U.S. Geological Survey. Selected scour processes and scour design equations are evaluated using 139 measurements of local scour in live-bed and clear-water conditions. Pier-scour measurements were made at 44 bridges around 90 bridge piers in 12 states. The influence of pier width on scour depth is linear in logarithmic space. The maximum observed ratio of pier width to scour depth is 2.1 for piers aligned to the flow. Flow depth and scour depth were found to have a relation that is linear in logarithmic space and that is not bounded by some critical ratio of flow depth to pier width. Comparisons of computed and observed scour depths indicate that none of the selected equations accurately estimate the depth of scour for all of the measured conditions. Some of the equations performed well as conservative design equations; however, they overpredict many observed scour depths by large amounts. Some equations fit the data well for observed scour depths less than about 3 m (9.8 ft), but significantly underpredict larger observed scour depths.

scholarly journals Analysis of Scour Depth Around Bridge Piers With Round Nose Shape by HEC-RAS 5.0.7 Software

2021 ◽  
Vol 1764 (1) ◽  
pp. 012151
Author(s):  
C S Silvia ◽  
M Ikhsan ◽  
A Wirayuda ◽  
Mastiar
Keyword(s):  
Bridge Piers ◽  
Scour Depth ◽  
Nose Shape

scholarly journals Hybrid Scour Depth Prediction Equations for Reliable Design of Bridge Piers

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2019
Author(s):  
Hossein Hamidifar ◽  
Faezeh Zanganeh-Inaloo ◽  
Iacopo Carnacina
Keyword(s):  
Flow Velocity ◽  
Critical Velocity ◽  
Depth Estimation ◽  
Hybrid Models ◽  
Velocity Estimation ◽  
Bridge Piers ◽  
Critical Flow ◽  
Scour Depth ◽  
Critical Flow Velocity ◽  
Estimation Equations

Numerous models have been proposed in the past to predict the maximum scour depth around bridge piers. These studies have all focused on the different parameters that could affect the maximum scour depth and the model accuracy. One of the main parameters individuated is the critical velocity of the approaching flow. The present study aimed at investigating the effect of different equations to determine the critical flow velocity on the accuracy of models for estimating the maximum scour depth around bridge piers. Here, 10 scour depth estimation equations, which include the critical flow velocity as one of the influencing parameters, and 8 critical velocity estimation equations were examined, for a total combination of 80 hybrid models. In addition, a sensitivity analysis of the selected scour depth equations to the critical velocity was investigated. The results of the selected models were compared with experimental data, and the best hybrid models were identified using statistical indicators. The accuracy of the best models, including YJAF-VRAD, YJAF-VARN, and YJAI-VRAD models, was also evaluated using field data available in the literature. Finally, correction factors were implied to the selected models to increase their accuracy in predicting the maximum scour depth.

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