Grnn Based Modelling of Pier Scour Depth Using Field Dataset

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

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

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 vari- ables, and using experimental data, a neural network model is trained to predict this equilibrium depth. Multilayer neural net- works 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

Using long short-term memory networks for river flow prediction

This is the final version. Available on open access from IWA Publishing via the DOI in this recordData availability statement: All relevant data are available from an online repository or repositories (http://www.hydroshare.org/resource/93f1f580de88403a8c52d2b3238297eb).Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash-Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.National Natural Science Foundation of ChinaRoyal SocietyEngineering and Physical Sciences Research Council (EPSRC

The science behind scour at bridge foundations : a review

Foundation scour is among the main causes of bridge collapse worldwide, resulting in significant direct and indirect losses. A vast amount of research has been carried out during the last decades on the physics and modelling of this phenomenon. The purpose of this paper is, therefore, to provide an up-to-date, comprehensive, and holistic literature review of the problem of scour at bridge foundations, with a focus on the following topics: (i) sediment particle motion; (ii) physical modelling and controlling dimensionless scour parameters; (iii) scour estimates encompassing empirical models, numerical frameworks, data-driven methods, and non-deterministic approaches; (iv) bridge scour monitoring including successful examples of case studies; (v) current approach for assessment and design of bridges against scour; and, (vi) research needs and future avenues

Estimation of Clear-Water Local Scour at Pile Groups Using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS).

M.S. Thesis. University of Hawaiʻi at Mānoa 2017

Seabed scour around marine structures in mixed and layered sediments

The inherent uncertainty in the prediction of seabed scour depth at offshore structures such as wind turbines is currently a significant barrier to realising optimised, cost effective foundation design. One significant aspect thought to contribute to this uncertainty which has not been extensively studied is the variation in the sediment properties in the marine environment where sediment beds often consist of complex mixtures of materials stratified with depth. This research project encompasses the design and execution of an extensive laboratory study investigating this aspect of the scour problem. Two uniformly graded sands were used to build a range of simplified mixed and layered sediment beds, as a first step to improving understanding of scour behaviour in these situations. A variety of hydrodynamic conditions including unidirectional current, tidal and wave-current flows were tested to ensure relevance to the marine environment. As part of the experiment design a detailed review of scour measurement techniques was conducted leading to the implementation of a photogrammetry system which delivered high resolution, high accuracy scour hole profiles. This study has led to a number of original results. In a layered bed of fine sand overlying coarse sand interaction effects at the grain scale resulted in an enhancement of scour depth in the underlying coarse sand. Bimodally distributed mixed sands were found to alter significantly the scour time development curve. Novel scour tests under a spring-neap tidal cycle in the clear water regime indicated a considerable lengthening of the time to equilibrium. A review of prediction methodologies was undertaken and modifications proposed to take into account the research outcomes. This project has demonstrated that the configuration of the sediment bed is highly influential on scour development, and will contribute towards the future development of more sophisticated design models for implementation in industry