Analysis of experimental data sets for local scour depth around bridge abutments using artificial neural networks

Abstract The paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

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Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

Archives of Environmental Protection ◽

10.1515/aep-2017-0034 ◽

2017 ◽

Vol 43 (4) ◽

pp. 26-32 ◽

Cited By ~ 2

Author(s):

Sinan Mehmet Turp

Keyword(s):

Neural Network ◽

Experimental Data ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Back Propagation ◽

Data Sets ◽

Adsorption Efficiency ◽

Back Propagation Algorithm ◽

Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

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

Volume 7: Ocean Engineering ◽

10.1115/omae2015-42387 ◽

2015 ◽

Cited By ~ 1

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.

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Prediction of scour below submerged pipeline crossing a river using ANN

Water Science & Technology ◽

10.2166/wst.2011.459 ◽

2011 ◽

Vol 63 (10) ◽

pp. 2225-2230 ◽

Cited By ~ 17

Author(s):

H. Md. Azamathulla ◽

Nor Azazi Zakaria

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Empirical Model ◽

Local Scour ◽

Accurate Estimation ◽

Scour Depth ◽

Data Sets ◽

Regression Equations ◽

Laboratory Measurements ◽

Pipeline Crossing

The process involved in the local scour below pipelines is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of artificial neural networks (ANN) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published works and used to train the network or evolve the program. The developed networks were validated by using the observations that were not involved in training. The performance of ANN was found to be more effective when compared with the results of regression equations in predicting the scour depth around pipelines.

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

10.20944/preprints201906.0164.v1 ◽

2019 ◽

Cited By ~ 1

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.

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Identifying nonclassicality from experimental data using artificial neural networks

Physical Review Research ◽

10.1103/physrevresearch.3.023229 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Valentin Gebhart ◽

Martin Bohmann ◽

Karsten Weiher ◽

Nicola Biagi ◽

Alessandro Zavatta ◽

...

Keyword(s):

Experimental Data ◽

Neural Networks ◽

Artificial Neural Networks ◽

Artificial Neural

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Reconstruction of road defects and road roughness classification using Artificial Neural Networks simulation and vehicle dynamic responses: Application to experimental data

Journal of Terramechanics ◽

10.1016/j.jterra.2014.03.002 ◽

2014 ◽

Vol 53 ◽

pp. 1-18 ◽

Cited By ~ 26

Author(s):

H.M. Ngwangwa ◽

P.S. Heyns ◽

H.G.A. Breytenbach ◽

P.S. Els

Keyword(s):

Experimental Data ◽

Neural Networks ◽

Artificial Neural Networks ◽

Dynamic Responses ◽

Vehicle Dynamic ◽

Road Roughness ◽

Artificial Neural ◽

Roughness Classification

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Symbiotic coevolution of artificial neural networks and training data sets

Lecture Notes in Computer Science - Parallel Problem Solving from Nature — PPSN V ◽

10.1007/bfb0056893 ◽

1998 ◽

pp. 511-520 ◽

Cited By ~ 9

Author(s):

Helmut A. Mayer

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Training Data ◽

Data Sets ◽

Artificial Neural ◽

And Training

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Simulation of flood flow in a river system using artificial neural networks

Hydrology and Earth System Sciences ◽

10.5194/hess-9-313-2005 ◽

2005 ◽

Vol 9 (4) ◽

pp. 313-321 ◽

Cited By ~ 45

Author(s):

R. R. Shrestha ◽

S. Theobald ◽

F. Nestmann

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Flow Simulation ◽

Simulation Models ◽

River System ◽

Training Data ◽

Data Sets ◽

Network Training ◽

Artificial Neural ◽

Flood Flow

Abstract. Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.

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