Efficiency of artificial neural networks in determining scour depth at composite bridge piers

Amini, Ata; Hamidi, Shahriar; Shirzadi, Ataollah; Behmanesh, Javad; Akib, Shatirah

doi:10.1080/15715124.2020.1742138

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Besides, the pier diameter has the most signif-icant impact on predicting local scour depth based on sensitivity outcomes. Amini et al (2020) used an ANN model with a single hidden layer and three neurons to predict the scour depth at the composite piers of the bridges. They found that the proposed ANN model provides better results than the empirical methods, and the pile cap location and the flow depth have the most effects on the scour depth.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers

Ali

Günal

2021

Archives of Hydro-Engineering and Environmental Mechanics

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Local scour around bridge piers impairs the stability of bridges’ structures. Therefore, a delicate estimation of the local scour depth is vital in designing the bridge piers foundations. In this research, MATLAB software was used to train artificial neural network (ANN) models with four hundred laboratory datasets from different laboratory studies, including five parameters: pier diameter, flow depth flow velocity, critical sediment velocity, sediment particle size, and equilibrium local scour depth. The outcomes present that the ANN model with the Levenberg-Marquardt algorithm and 11 nodes in the single hidden layer gives an accurate estimation better than other ANN models trained with different training algorithms based on the regression results and mean squared error values. Besides, the ANN model accurately provides predicted local scour depth and is better than linear and nonlinear regression models. Furthermore, sensitivity analysis shows that removing pier diameter from training parameters diminishes the reliability of prediction.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers

Ali

Günal

2021

Archives of Hydro-Engineering and Environmental Mechanics

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“…Amini, Magdi and Truce [18][19][20] Bridge scour The majority of published studies are focused on bridge scour.…”

Section: Nature Of Study Main Findingsmentioning

confidence: 99%

Physical Modeling of the Scour Volume Upstream of a Slit Weir Using Uniform and Non-Uniform Mobile Beds

Hamdan

Al-Adili

Mohammad

2022

Water

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This study presents new laboratory data on the time-varying scour upstream of a slit weir used for sediment release near hydropower intake. The governing parameters in temporal and spatial scour hole development under steady and unsteady flow conditions were experimentally investigated. This study includes 40 scenarios for steady flow at center and side slit weirs for uniform and non-uniform sediment with median sizes of d50 = 0.24, 0.55 mm and four scenarios of unsteady flow conditions at a center slit weir under different flow intensities. The steel slit weirs were built in a rectangular brick and concrete flume with dimensions of 1.25 m wide, 8.0 m long and 1.0 m deep. The dimensional analysis supports recent studies. This study demonstrates an increment in the resulted scour volume for fine and uniform sands at the center slit weir of about 2 times the value of coarse sand and 1.25 times the value measured with the side slit weir for uniform and non-uniform sands. However, the resulted scour volume for fine non-uniform sand at the center slit weir was recorded as 2.5 times that of coarse sand. There was a dramatic increase in the scour volume of about 4 times at the center slit weir and 3–3.5 at the side slit weir when the flow rate increased by 4 times.

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“…Empirical formulas are simple to use but their prediction accuracy is low due to the complex scour-related factors [18]. Moreover, intelligence-based scour prediction methods, such as artificial neural network (ANN) [19,20], support vector machine (SVM) [21,22] and genetic algorithm (GA) [23], etc., provided more accurate results than empirical formulas [24]. However, both methods ignored influence of scour morphology on bridge safety [4].…”

Section: Introductionmentioning

confidence: 99%

A Review of Vibration-Based Scour Diagnosis Methods for Bridge Foundation

et al. 2023

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Foundation scour poses a serious threat to bridge safety in the whole life cycle and leads to many bridge failure incidents. Recently, as an important subfield of bridge structural health monitoring, vibration-based scour diagnosis methods have garnered widespread attention, particularly due to their rapid and low-cost features, which overcomes the difficulties of complex equipment installation associated with the traditional approaches. Recent advances of this method within the last decade are reviewed in this paper. Firstly, the principle of scour diagnosis and vibration excitation methods are introduced. Then, existing qualitative and quantitative studies on scour diagnosis are reviewed, respectively. The former refers to identifying the scour location based on the bridge dynamic characteristics or dynamic response changes, and the latter refers to identifying scour depth based on model updating or machine learning methods. Based on the above review, some important but neglected issues are summarized and discussed in depth, and some challenges and future trends are proposed, including innovative excitation methods, mitigation of environmental conditions interference, soil–structure interaction prediction and application of machine learning techniques.

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Efficiency of artificial neural networks in determining scour depth at composite bridge piers

Cited by 5 publications

References 17 publications

Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers

Artificial Neural Network for Estimation of Local Scour Depth Around Bridge Piers

Physical Modeling of the Scour Volume Upstream of a Slit Weir Using Uniform and Non-Uniform Mobile Beds

A Review of Vibration-Based Scour Diagnosis Methods for Bridge Foundation

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