Effects of Time and Channel Geometry on Scour at Bridge Abutments

Cardoso, António H.; Bettess, R.

doi:10.1061/(asce)0733-9429(1999)125:4(388)

Cited by 125 publications

(67 citation statements)

References 6 publications

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“…Figure 6 represents the comparison between the data reported by Cardoso and Bettes [5] and Coleman et al [10] and those of the present work for the M5 model tree. As can be seen, M5 model predict the scour depth more accurately than empirical relations.…”

Section: Discussionsupporting

confidence: 54%

“…The other reason for the inaccuracy of the presented data is the lack of attention to scour time development. That is why in recent decades several studies have been conducted in this field and researchers presented some relations for scour time development, such as Gill [3], Cunha [4], Cardoso and Bettes [5], Kothyari and Ranga Raju [6], Ballio and Orsi [7], Radice et al [8], Oliveto and Hager [9], Coleman et al [10].…”

mentioning

confidence: 99%

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M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

Biabani¹,

Halaghi²,

Ghorbani³

2016

OJG

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Scour is a natural phenomenon that is created by the rivers streams or the flood which brings about transferring or eroding of bed materials. To have accurate and safe erosion control structures design, maximum scour depth in downstream of the structures gains specific significance. In the current study, M5 model tree as remedy data mining approaches is suggested to estimate the scour depth around the abutments. To do this, Kayaturk laboratory data (2005), with different hydraulic conditions, are used. Then, the results of M5 model were also compared with genetic programming (GP) and pervious empirical results to investigate the applicability, ability, and accuracy of these procedures. To examine the accuracy of the results yielded from the M5 and GP procedures, two performance indicators (determination coefficient (R2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of M5 technique sounds satisfactory regarding the performance indicators (R 2 = 0.944 and RMSE = 0.126) with less deviation from the numerical values. In addition, M5 tree model, by presenting relationships based on liner regression, has good capability to estimate the depth of scour abutment for engineers in practical terms.

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Section: Discussionsupporting

confidence: 54%

mentioning

confidence: 99%

M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

Biabani¹,

Halaghi²,

Ghorbani³

2016

OJG

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show abstract

“…The inspection of this figure reveals that the predictor proposed by Oliveto and Hager (2005) does not match the data. The plots do not either display any horizontal plateau, a denoting factor of the equilibrium phase as proposed by Cardoso and Bettess (1999) for vertical wall abutments. The applicability of the time-factor model of Melville and Sheppard as quoted at NCHRP (2011) was also not supported by the data [see Fig.…”

Section: Data Characterizationmentioning

confidence: 72%

“…Breusers and Raudkivi (1991) suggested that contraction scour is negligible for values of B=D p as low as 2.0 to 2.5. Cardoso and Bettess (1999) have shown that, shortly after the beginning of the scouring process, the flow field remains practically unaffected over the flat bed out of the scour hole since the intercepted discharge tends to be conveyed through the scour holes. Thus, it seems safe to conclude that, if eventually present, contraction effects were minor in the present study.…”

Section: Data Characterizationmentioning

confidence: 99%

Clear-Water Scour at Comparatively Large Cylindrical Piers

et al. 2013

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New long-duration clear-water scour data were collected at single cylindrical piers with the objective of investigating the effect of sediment coarseness, D p =D 50 (D p = pier diameter; D 50 = median grain size) on the equilibrium scour depth and improving the scour depth time evolution modeling by making use of the exponential function suggested in the literature. Experiments were carried out for the flow intensity close to the threshold condition of initiation of sediment motion, imposing wide changes of sediment coarseness and flow shallowness, d=D p (d = approach flow depth). The effect of sediment coarseness on the equilibrium scour depth was identified; existing predictors were modified to incorporate this effect for U=U c ≈ 1.0; D p =D 50 > ≈60 and d=D p ≥ 0.5; the complete characterization of a known scour depth time evolution model was achieved for U=U c ≈ 1.0, 60 < D p =D 50 < 500 and 0.5 ≤ d=D p ≤ 5.0.

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“…Local scour at foundations has long been of concern for engineers (Cardoso & Bettes 1999). In the safety evaluation of bridges, local scouring of bridge foundation material near piers/abutments is, therefore, an important issue (Huber 1991;Dey & Barbhuya 2004a).…”

Section: Introductionmentioning

confidence: 99%

ANFIS-based approach to scour depth prediction at abutments in armored beds

Muzzammil

Alam

2010

Journal of Hydroinformatics

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An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour countermeasures to be implemented against excessive scouring. Most of the scour depth prediction formulae available in the literature have been developed based on the analysis of laboratory and field data using statistical methods such as the regression method (RM).The alternative approaches, such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), are generally preferred to provide better solutions in cases where the available data is incomplete or ambiguous in nature. In the present study, an attempt has, therefore, been made to develop the ANFIS model for the prediction of scour depth at the bridge abutments embedded in an armored bed and make the comparative study for the performance of ANFIS over RM and ANN in modeling the scour depth. It has been found that the ANFIS model performed best amongst all of these methods. The causative variables in raw form result in a more accurate prediction of the scour depth than that of their grouped form.Key words 9 9 9 9 ANFIS, artificial neural network, bridge abutments, local scour, regression analysis

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Effects of Time and Channel Geometry on Scour at Bridge Abutments

Cited by 125 publications

References 6 publications

M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

M5 Model Tree to Predict Temporal Evolution of Clear-Water Abutment Scour

Clear-Water Scour at Comparatively Large Cylindrical Piers

ANFIS-based approach to scour depth prediction at abutments in armored beds

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