Prediction of time-varying maximum scour depth around short abutments using soft computing methodologies - A comparative study

Maximum depth of scouring around bridge abutments is a significant criterion in design of safe depth for abutment foundation. Many studies done on maximum scouring depth are limited to specific shape of abutment and there is no general equation in the estimation of time varying scour depth at short abutments. In this research, maximum depth and also the temporal variation of local scour at a vertical-wall, wing-wall and semicircular abutments, and spur dike as well, were investigated experimentally. About 1400 sets of experimental data were collected. The results indicated that 70-90% of the equilibrium scouring depths occurred during the first 20% of overall time of scouring tests. According to the collected data, Multiple Nonlinear Regression (MNLR), Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) were adopted to predict the time variation of scour depth around abutments. The computer models were compared to the other empirical equations presented in the literature. The study showed that the conducted regression model is rather precise and practical (R2 = 0.88). Also, the results of the numerical modeling indicated that ANFIS model produced the best results (R2 = 0.98) in comparison with ANN models using feed forward back propagation (R2=0.96) and radial basis function (R2 = 0.94).

[1]  W. Hager,et al.  Temporal Evolution of Clear-Water Pier and Abutment Scour , 2002 .

[2]  Bruce W. Melville,et al.  RIPRAP PROTECTION AT BRIDGE PIERS , 2001 .

[3]  Tarek Sayed,et al.  Comparison of Adaptive Network Based Fuzzy Inference Systems and B-spline Neuro-Fuzzy Mode Choice Models , 2003 .

[4]  A. Melih Yanmaz,et al.  Time-wise variation of scouring at bridge abutments , 2007 .

[5]  Siow-Yong Lim,et al.  Equilibrium Clear-Water Scour around an Abutment , 1997 .

[6]  Abdul Karim Barbhuiya,et al.  Time Variation of Scour at Abutments , 2005 .

[7]  Shahin Ara Begum,et al.  Radial basis function to predict scour depth around bridge abutment , 2011, 2011 2nd National Conference on Emerging Trends and Applications in Computer Science.

[8]  Elena Toth,et al.  Prediction of local scour depth at bridge piers under clear-water and live-bed conditions: comparison of literature formulae and artificial neural networks , 2011 .

[9]  E. Orsi,et al.  TIME EVOLUTION OF SCOUR AROUND BRIDGE ABUTMENTS , 2001 .

[10]  N Şarlak,et al.  Analysis of experimental data sets for local scour depth around bridge abutments using artificial neural networks , 2011 .

[11]  Cameron Tropea,et al.  Experimental and Numerical Investigations , 2006 .

[12]  T F Kwan,et al.  A STUDY OF ABUTMENT SCOUR , 1988 .

[13]  Terry W. Sturm,et al.  Clear‐Water Scour around Abutments in Floodplains , 1994 .

[14]  Kittur G. Ranga Raju,et al.  TEMPORAL, VARIATION OF SCOUR AROUND CIRCULAR BRIDGE PIERS , 1992 .

[15]  António H. Cardoso,et al.  Effects of Time and Channel Geometry on Scour at Bridge Abutments , 1999 .

[16]  Shahaboddin Shamshirband,et al.  A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier , 2014, Neurocomputing.

[17]  Mohammad Muzzammil,et al.  ANFIS approach to the scour depth prediction at a bridge abutment , 2010 .

[18]  P. B. Deolalikar,et al.  Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket , 2005 .

[19]  Giuliano Di Baldassarre,et al.  BRIDGE PIER SCOUR: A REVIEW OF PROCESSES, MEASUREMENTS AND ESTIMATES , 2012 .

[20]  Javed Alam,et al.  ANFIS-based approach to scour depth prediction at abutments in armored beds , 2011 .

[21]  Shahaboddin Shamshirband,et al.  Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups , 2014, Journal of Coastal Conservation.

[22]  Shahaboddin Shamshirband,et al.  Adaptive neuro-fuzzy estimation of bridge girder forces with perforations under tsunami wave loading , 2014, Journal of Coastal Conservation.

[23]  Shahaboddin Shamshirband,et al.  Soft-Computing Methodologies for Precipitation Estimation: A Case Study , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[24]  Abdollah Ardeshir,et al.  Prediction of time variation of scour depth around spur dikes using neural networks , 2012 .

[25]  Robert Hecht-Nielsen,et al.  Applications of counterpropagation networks , 1988, Neural Networks.

[26]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[27]  B. Melville.  Local Scour at Bridge Abutments , 1992 .

[28]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[29]  D. Jeng,et al.  Estimation of pile group scour using adaptive neuro-fuzzy approach , 2007 .

[30]  Shahaboddin Shamshirband,et al.  Adaptive neuro-fuzzy selection of the optimal parameters of protective spur dike , 2014, Natural Hazards.

[31]  Bruce W. Melville,et al.  Clear-water scour development at bridge abutments , 2003 .

[32]  Shahaboddin Shamshirband,et al.  Experimental and Numerical Investigation of the Effect of Different Shapes of Collars on the Reduction of Scour around a Single Bridge Pier , 2014, PloS one.

[33]  Bruce W. Melville,et al.  Local scour and flow measurements at bridge abutments , 1994 .

[34]  António H. Cardoso,et al.  Flow Intensity Parameter in Pier Scour Experiments , 2007 .

[35]  Dong-Sheng Jeng,et al.  Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers , 2007, Adv. Eng. Softw..