Validation of Some Bridge Pier Scour Formulae Using Field and Laboratory Data

Mohamed, Thamer Ahmed; Noor, Megat Johari Megat Mohd; Ghazali, Abdul Halim; Huat, Bujang B. K.

doi:10.3844/ajessp.2005.119.125

Cited by 31 publications

(11 citation statements)

References 8 publications

(7 reference statements)

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“…were obtained for the model developments in this study from Landers and Mueller (1999) (384 sets of data) and Mohammed et al (2005) (14 sets of data). Table 1 summarizes…”

Section: Local Scour Problem Around a Piermentioning

confidence: 99%

“…Johnson (1995) reported that the Melville and Sutherland formula tends to over-predict d s to greater extent than any other formulae. Recently, Mohammed et al (2005) showed that the Laursen and Toch (1956) and the Colorado State University (CSU) formulae (Mohammed et al, 2005) give reasonable estimates, whilst the Melville and Sutherland (1988) and Jain and Fisher (1980) formulae over-predict pier scour based on the comparison of some bridge pier scour formulae using field and laboratory data.…”

Section: Introductionmentioning

confidence: 99%

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Genetic Programming to Predict Bridge Pier Scour

et al. 2010

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Bridge pier scouring is a significant problem for the safety of bridges. Extensive laboratory and field studies have been conducted examining the effect of relevant variables. This note presents an alternative to the conventional regression-based equations (HEC-18 and regression equation developed by authors), in the form of artificial neural networks (ANNs) and genetic programming (GP). 398 data sets of field measurements were collected from published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The performance of GP was found more effective when compared to regression equations and ANNs in predicting the scour depth of bridge piers.

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“…were obtained for the model developments in this study from Landers and Mueller (1999) (384 sets of data) and Mohammed et al (2005) (14 sets of data). Table 1 summarizes…”

Section: Local Scour Problem Around a Piermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic Programming to Predict Bridge Pier Scour

et al. 2010

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“…Originally, this formula was presented in HEC-18 (Richardson and Davis 2001) and was recently used by Mohamed et al (2005) for comparison with other regression-based equations.…”

Section: Richardson and Davis (2001) Formulamentioning

confidence: 99%

Prediction of local scour around bridge piers: artificial-intelligence-based modeling versus conventional regression methods

Rady

2020

Appl Water Sci

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This paper presents the use of two artificial intelligence modeling methods, namely genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS), to predict pier scour depth based on clear water conditions of 320 data sets of laboratory and field data measurements. The scour depth was modeled as a function of five main dimensionless parameters: pier width, approaching flow depth, Froude number, standard deviation of grain size distribution, and channel open ratio. A functional relationship was established using the trained GP model, and its performance was verified by comparing the results with those obtained by the ANFIS model and seven conventional regression-based formulas. Numerical tests indicated that the GP model yielded much superior agreement than the ANFIS model or any other empirical equation. The advantage of the GP model was confirmed by applying the derived GP equation to predict the scour depth around the piers of Imbaba Bridge, Egypt. Keywords Local scour depth · Genetic programming · Adaptive neuro-fuzzy · Regression methods · Bridge piers List of symbols BChannel width (m) D Pier width (m) d s Equilibrium scour depth (m) d 50 Mean sediment size (m) D/d 50 Dimensionless pier width d s /D Dimensionless pier scour depth F r Froude number (dimensionless) g Gravitational acceleration (m/s 2 ) L Length of pier (m) U Approach flow velocity (m/s) R Correlation coefficient Y Approach flow depth (m) Y/D Dimensionless approach flow depth α Channel open ratio θ Angle of attack (°) σ Standard deviation of grain size distribution Abbreviations ANFIS Adaptive neuro-fuzzy inference systems AI Artificial intelligence ANNs Artificial neural networks GAs Genetic algorithms GP Genetic programming HAD High Aswan Dam HRI Hydraulics Research Institute LGP Linear genetic programming MAPE Mean absolute percentage error MCM Million cubic meters RMSE Root-mean-square error

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“…In recent decades, many studies have been undertaken to predict the exact depth of local scour (for example Laursen and Toch, 1956;Shen, 1971;Hancu, 1971;Breusers et al, 1977;Melville and Sutherland, 1988;Richardson et al, 1993;Mohammed et al, 2005;Alias et al, 2008;Kumar and Sreeja, 2012;among others). Through physical laboratory tests, these studies derived the dominant parameters of local scour, such as flow velocity, water depth, pier width, pier length, and flow angle, and then proposed empirical formulae using these parameters.…”

Section: Introductionmentioning

confidence: 98%

Evaluation of the applicability of pier local scour formulae using laboratory and field data

Park

Cho

2014

Marine Georesources & Geotechnology

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The reliable estimation of the local scour depth at a bridge pier is essential for proper design and maintenance of bridge piers. Most local scour formulae have been developed based on the results of laboratory experiments. The formulae based on laboratory data do not often produce reasonable predictions for field piers, because laboratory investigations are apt to oversimplify or ignore many of the complexities of the flow fields around the bridge piers. Validation of the formulae is necessary in order to ascertain which of the formulae are able to provide reasonable estimates of the local scour depth. In this study, six commonly cited formulae based on laboratory data or field data were selected for validation using 180 laboratory data sets gathered from the literature and 446 field data sets collected from four countries. The six formulae validated in this paper are the Colorado State University (CSU), Neill, Froehlich, Breuser, Laursen, and simplified Chinese formulae. Comparisons between the predicted and measured depths were performed using scour from the laboratory and field data. An artificial neural network technique was also applied in order to compare the tendencies between the field and laboratory data sets.

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Validation of Some Bridge Pier Scour Formulae Using Field and Laboratory Data

Cited by 31 publications

References 8 publications

Genetic Programming to Predict Bridge Pier Scour

Genetic Programming to Predict Bridge Pier Scour

Prediction of local scour around bridge piers: artificial-intelligence-based modeling versus conventional regression methods

Evaluation of the applicability of pier local scour formulae using laboratory and field data

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