Prediction of Maximum Scour Depth at Spur Dikes with Adaptive Neural Networks

Wu, X. M.; Lim, Sangseop

doi:10.4203/ccp.16.4.1

Cited by 8 publications

(11 citation statements)

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“…Among various architectures and paradigms, the back-propagation network is one of the simplest and most practicable network being used in performing higher level human tasks such as diagnosis, classification, decision-making, planning, and scheduling. The neural network based modelling process involves five main aspects: (1) data acquisition, analysis and problem representation; (2) architecture determination; (3) learning process deter mination; (4) training of the network; and (5) testing of the trained network for generalization evaluation, (Wu & Lim 1993). Fig.…”

Section: Artificial Neural Network: Definitions and Basic Conceptmentioning

confidence: 99%

Application of artificial neural network to forecast construction duration of buildings at the predesign stage

Bhokha

Ogunlana

1999

Engineering, Construction and Architectural Management

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The application of an artificial neural network (ANN) to forecast the construction duration of buildings at the predesign stage is described in this paper. A three‐layered back‐propagation (BP) network consisting of 11 input nodes has been constructed. Ten binary input nodes represent basic information on building features (i.e. building function, structural system, foundation, height, exterior finishing, quality of interior decorating, and accessibility to the site), and one real‐value input represents functional area. The input nodes are fully connected to one output node through hidden nodes. The network was implemented on a Pentium‐150 based microcomputer using a neurocomputer program written in C+ +. The Generalized Delta Rule (GDR) was used as learning algorithm. One hundred and thirty‐six buildings built during the period 1987–95 in the Greater Bangkok area were used for training and testing the network. The determination of the optimum number of hidden nodes, learning rate, and momentum were based on trial‐and‐error. The best network was found to consist of six hidden nodes, with a learning rate of 0.6, and null momentum. It was trained for 44700 epochs within 943 s such that the mean squared error (judgement) of training and test samples were reduced to 1.17 × 10−7 and 3.10 × 10−6, respectively. The network can forecast construction du‐ration at the predesign stage with an average error of 13.6%.

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Section: Artificial Neural Network: Definitions and Basic Conceptmentioning

confidence: 99%

Application of artificial neural network to forecast construction duration of buildings at the predesign stage

Bhokha

Ogunlana

1999

Engineering, Construction and Architectural Management

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“…Once the ANN is adequately trained, it can generalize to similar cases, which it has never seen. Detailed information about ANN and its working principles can be found in references (Altinkok and Koker, 2004;Wu and Lim, 1993;Emami et al, 1996;Yagawa and Okuda, 1996).…”

Section: Overview Of Ann and Proposed Ann Modelmentioning

confidence: 99%

“…The neural network based modeling process involves five main aspects: (a) data acquisition, analysis and problem representation; (b) architecture determination; (c) learning process determination; (d) training of the networks; and (e) testing of the trained network for generalization evaluation (Wu and Lim, 1993).…”

Section: Overview Of Ann and Proposed Ann Modelmentioning

confidence: 99%

Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artificial neural networks

Eyerci̇oğlu

Kanca

Pala

et al. 2008

Journal of Materials Processing Technology

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“…Choi and Cheong (2006) described a method for predicting local scour around bridge piers using an artificial neural network (ANN) with emphasis on selecting input variables, calibrations of network control parameters, learning process, and verifications. Wu and Lim (1993) carried out the study of prediction of maximum scour depth at spur dikes using adaptive neural networks. Many investigators have already made comprehensive investigations on abutment scour in the past and numerous reliable databases are available in the literature.…”

Section: Introductionmentioning

confidence: 99%

Application of Neural Networks To Scour Depth Prediction at The Bridge Abutments

Muzzammil¹

2008

Engineering Applications of Computational Fluid Mechanics

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An estimation of scour depth is a prerequisite for the efficient foundation design of the abutment of a bridge. Many equations and models are available in literature for predicting abutment scour depth based on experimental and theoretical approaches. It is still difficult to obtain a general model to provide accurate estimation of scour depth due to the presence of complex flow structure at the base of the abutment. The artificial neural network (ANN) is generally considered as an alternative approach to the experimental and theoretical methods. It acts as a universal function approximator and consequently it is very useful in modelling problems wherein the relationship between dependent and independent variables is poorly understood. In the present study, ANN models with commonly used algorithm of training have, therefore, been developed for the equilibrium scour depth prediction at bridge abutments using a sizable amount of laboratory data from various sources. The ANN models of various training schemes have been found to be better than the conventional regression model based on the performance parameters for both calibration as well as validation set of data. The present study also indicates that the prediction based on the raw data (dimensional) is better than those based on non-dimensional parameters.

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Prediction of Maximum Scour Depth at Spur Dikes with Adaptive Neural Networks

Cited by 8 publications

References 0 publications

Application of artificial neural network to forecast construction duration of buildings at the predesign stage

Application of artificial neural network to forecast construction duration of buildings at the predesign stage

Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artificial neural networks

Application of Neural Networks To Scour Depth Prediction at The Bridge Abutments

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