A cooperative expert based support vector regression (Co-ESVR) system to determine collar dimensions around bridge pier
Introduction
In many countries, bridges are built across canals and rivers as traffic volume increases due to economic development. Every year several bridges fail, not only for structural reasons, but owing to pier and abutment scouring [1]. Scouring is one of the most significant and destructive effects of floods on bridges. It occurs as a result of the erosive behavior of flowing water on the beds and banks of alluvial channels. Flow approaching a bridge pier or abutment is accompanied by enhanced sediment-carrying capacity. The scour phenomenon may cause catastrophic hazards accounting for reduction of pier support. A number of bridge failures ensuing from scouring have been reported over the past years. A Federal Highway Administration (FHWA) report states that 383 bridges collapsed due to catastrophic floods and scouring [2].
The complex vortex system in the vicinity of bridge piers is the key factor and main reason why scour holes develop. As flow affects the pier nose, a downward flow is formed in front of the pier. This impinges on the stream bed, causing scour-hole formation in front of the pier, and eventually a complex vortex system is formed. In addition, wake vortices are created due to downstream flow separation of the pier, which behave as small tornados instigating the bed material to lift and produce an independent scour hole downstream of the pier. Fig. 1a shows the scouring mechanism around a circular bridge pier and Fig. 1b depicts a bridge which experienced scour during a flood.
Numerous researchers have studied the mechanism of scour phenomenon around bridge and other hydraulic structures like; Link et al., Heidarpour et al., Muzzammil et al., Dey and Raikar, Kirkil et al., Hendrickson et al., and Karami et al. [3], [4], [5], [6], [7], [8], [9], [10]. Studying the mechanism of scour, researchers found out that scour phenomenon can be controlled and they proposed various countermeasure methods. The proposed methods are broadly grouped under two distinct categories: armoring and flow-altering countermeasures, which are also described as direct and indirect methods [11]. In the armoring technique, the structures are protected directly against scouring by covering the bridge pier area by riprap stones, reno-mattresses, cabled-tied blocks, gabions, tetrapods, dolos, concrete-filled mats or bags, and concrete aprons [12]. In flow-altering countermeasures, the flow pattern is modified by structures such as sacrificial piles and sills, collars, and slots to diminish scour [13].
A collar is a type of indirect countermeasure for controlling scour around piers by diverting the down-flow and acting as an obstacle in down-flow path to reduce the horseshoe vortex strength. Numerous researchers have examined the collar effect on reducing scour depth and its efficiency has been established in earlier studies [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. Despite previous efforts, optimizing collar size in order to reach maximum collar efficiency for protecting pier has not yet been determined. In this paper, the main objective was to find the optimum dimensions of a rectangular collar and several experiments were performed using several sizes of collar in order to find the optimum sizes. But the experiments are too time consuming and expensive then developing a computer-based method is necessary and unavoidable.
As mentioned before, due to expensive procedures of experimental and field work studies, numerical, mathematical and computer-based modeling methods have been considered recently in this scheme.
Soft computing techniques, such as artificial neural networks are employed for predicting scour depth [24], [25], [26], [27] and their performance was compared with various existing methods (i.e. fuzzy logic). The results of these studies suggest that the neural network approach performs better than empirical relations [28]. A neural network-based modeling algorithm requires setting up different learning parameters (e.g. learning rate and momentum), the optimal number of nodes in the hidden layer and the number of hidden layers. A large number of training iterations may force a neural network to over-train, which may affect the models’ predicting capabilities. The presence of local minima is a further problem when using a back-propagation neural network. Recent studies suggest the usefulness of neuro fuzzy in finding a neural network׳s optimal architecture for scour prediction. ANFIS was applied to estimate the current-induced scour depth around pile groups [29]. It has been reported [29] that a neuro fuzzy model was utilized to predict the scouring around an arch-shaped bed sill.
Within the last decade, several studies reported the adoption of generalized regression neural networks and support vector machines in civil engineering [30], [31], [32], and it was found that they function adequately in comparison to a back-propagation neural network and the neuro fuzzy approach. The advantages of generalized regression neural networks and support vector machines are that both methods require few user-defined parameters and they do not face the problem of local minima.
In view of the enhanced performance by support vector machine based regression in civil engineering, in this study a cooperative-based prediction method is proposed, which applies support vector regression and a multi-agent system. The predicted value of scour depth reduction percentage (re) through SVR cooperative agents is implemented to select the optimal collar dimensions around a bridge pier. The expert-based decision maker agent around the bridge scour gathers suitable data to send to the next layer. The multi agent-based SVR in the second layer adjusts its parameters to find the optimal cost function for predicting collar dimensions around the bridge pier to reduce scour around the bridge pier. The weighted sharing strategy selects the optimized cost function through the root mean square error (RMSE). The proposed Co-ESVR compares its performance with two empirical relations, a Polynomial-based (SVR_Poly) and RBF-based SVR (SVR_rbf) in predictions of collar dimensions around bridge piers. In addition, the performance of the proposed Co-ESVR is compared with that of non-cooperative SVR agents.
Section snippets
Experimental setup and procedure
The experiments were conducted in the hydraulic laboratory of the hydraulic engineering division at University of Malaya. The experimental flume in the laboratory is 12 m long, 30 cm wide and 45 cm high and has a slope of 0.0004. At the end of the flume there is a basin in which a triangular weir was placed to measure flow discharge with an accuracy of 0.1 l/s. Fig. 2 shows a schematic plan of the experimental flume in laboratory and all the items which are related to scouring process around a
System model: Cooperative expert-based support vector regression (Co-ESVR)
The designed architecture is called Cooperative Expert-based Support Vector Regression system (Co-ESVR), and it predicts the collar dimensions around a bridge scour. In the first layer of the proposed Co-ESVR architecture, Expert System (ES) agents proceed to audit bridge scour records. In the ES scheme, data collector agents included for each rectangular collar subsystem collect the values of features, after which a normal profile is created with the defined rules and the desired scenario is
Results and discussion
In the experiments, rectangular collars were employed in order to estimate the most effective upstream collar length (Luc), downstream collar length (Ldc), and width collar length (Lw) (Fig. 8a). In this empirical study, three series of experiments to optimize three mentioned dimensions were conducted. In each experiment series 10 experiments were done. Fig. 8b shows the scouring around bridge pier in laboratory. Fig. 8c shows one of the installed collars in flume which could
Conclusions
In this paper, the application of two multi agent-based support vector regression types, namely Polynomial-based (SVR_Poly) and RBF-based SVR (SVR_rbf), in the estimation of equilibrium and time-dependent scour depth around piers has been outlined. The study includes the manipulation of collected laboratory data to train and validate the networks. It was shown that the agent-based SVR_poly approaches predict scour depth much more accurately than the agent-based SVR_rbf methods in different
Acknowledgments
The corresponding author would like to acknowledge the Bright Sparks Program at University of Malaya. Financial support by the high impact research grants of the University of Malaya (UM.C/625/1/HIR/61, account number: H-16001-00-D000061) is gratefully acknowledged. Also authors would like to thank the support of IPPP grant, number PV058-2012A. This work is also funded by the University of Malaya, Malaysia, under grant CG027-2013.
Afshin Jahangirzadeh obtained his M.Sc. in civil engineering-Hydraulic from faculty of Civil and Environmental Engineering of Amirkabir University of Technology, Iran. He started his career as a civil Engineer at Mahab Ghodss consulting company from 2005 to 2007. He also has work as a University lecturer in Azad University of Tonakabon, Iran form 2007 to 2011. Currently, he is his PhD Candidate and research assistant on civil engineering-hydraulic structure in University of Malaya, Malaysia.
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Afshin Jahangirzadeh obtained his M.Sc. in civil engineering-Hydraulic from faculty of Civil and Environmental Engineering of Amirkabir University of Technology, Iran. He started his career as a civil Engineer at Mahab Ghodss consulting company from 2005 to 2007. He also has work as a University lecturer in Azad University of Tonakabon, Iran form 2007 to 2011. Currently, he is his PhD Candidate and research assistant on civil engineering-hydraulic structure in University of Malaya, Malaysia. His main research covers hydraulic structure, Hydraulic, Bridge engineering, River Engineering and Scour Phenomenon.
Shahaboddin Shamshirband received his M.Sc. degree in Computer Science from Islamic Azad University of Mashhad (IAUM), Iran in 2006. He joined the Faculty of Computer Science, Islamic Azad University, Iran for seven years. Currently, he is pursuing his PhD in University of Malaya, Malaysia. His main research covers networking, security, computational intelligence, and cloud computing.
Saeed Aghabozorgi received his B.Sc. in Computer Engineering and Software Discipline from University of Isfahan, Iran, in 2002. He received his M.Sc. from Islamic Azad University, Iran, in 2005, and his Ph.D. from University of Malaya in 2013. Currently, he is a lecturer at the Department of Information System, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia. His current research area is data mining.
Shatirah M Akib received her M.Sc. in Civil Engineering from University of Wales, Cardiff, United Kingdom, in 2003 and her Ph.D. in Hydraulic Structure Engineering from University of Malaya, Malaysia in 2009. Currently, she is a Senior lecturer at the Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia. Her current research areas are Hydrology and Hydraulic Engineering, Water Resources and Coastal and Offshore Engineering.
Hossein Basser received his B.Sc. in Civil Engineering from University of Tabriz, Iran, in 2008. He received his M.Sc. from Amirkabir University of Technology, Iran, in 2011. Currently, he is pursuing his Ph.D. in University of Malaya, Malaysia. His main research covers Sediment transport, Scour countermeasures, Computational Fluid Dynamics, Flow pattern and Scour and Flow Monitoring.
Nor Badrul Anuar obtained his Ph.D. in Information Security from Centre for Security, Communications and Network Research (CSCAN), Plymouth University, UK in 2012 and Master of Computer Science from University of Malaya, Malaysia in 2003. He is a senior lecturer at the Faculty of Computer Science and Information Technology in University of Malaya, Kuala Lumpur. He has published a number of conference and journal papers locally and internationally. His research interests include information security (i.e. intrusion detection systems), artificial intelligence and library information systems.
M.L. Mat Kiah received her M.Sc. in 1998 and PhD in 2007 from Royal Holloway, University of London, UK. She joined the Faculty of Computer Science & Information Technology, UM as a tutor in 1997. Her current research interests include key management, secure group communication and wireless mobile security. She is also interested in routing protocols and mobile Ad-Hoc networks. She has published 30 papers and authored books.