Development of expert systems for the prediction of scour depth under live-bed conditions at river confluences: Application of different types of ANNs and the M5P model tree

An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive networkbased fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods.

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“…After collision of bodies, their new positions and velocities are updated based on Eqs. (10)(11)(12)(13).…”

Section: Fig 2 Collision Of Bodiesmentioning

confidence: 99%

“…The results showed that the most important parameters affecting relative scour depth (d s /y) included ratio of pier width to flow depth (D/y) and ratio of pier length to flow depth (L/y). In addition, other researchers have performed a number of studies in this field as well, such as references [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

Hassanzadeh¹,

Jafari-Bavil-Olyaei²,

Taghi-Aalami³

et al. 2019

Period. Polytech. Civil Eng.

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“…The ability of soft computing methods in the analysis of the complex issues caused extensive use of these methods in various engineering sciences (Kim and Parnichkun 2017;Manu and Thalla 2017;Sanikhani et al 2018;Li et al 2019), especially river engineering (Ghorbani et al 2018;Yaseen et al 2018a, b) and water engineering such as sediment transport (Azamathulla et al 2012;Kumar et al 2014;Pektaş and Doğan 2015;Afan et al 2016;Ebtehaj and Bonakdari 2017), scour (Zahiri et al 2014;Balouchi et al 2015;Yousif et al 2019), hydraulic structures (Basser et al 2014;Bonakdari and Zaji 2018), rainfall and runoff (Kisi et al 2013;Sulaiman et al 2018;Tao et al 2018a, b), flood forecasting (Lohani et al 2014;Kasiviswanathan et al 2016;Solgi et al 2017;Diop et al 2018;Al Sudani et al 2019), groundwater level (Chang et al 2016;Li et al 2017), stable channel designing (Bonakdari and Gholami 2016;Gholami et al 2017aGholami et al , b, c, d, 2019a. In the fields of curved channel, in recent years the authors used various types of AI methods in prediction of different flow variables in some angles (60°, 90° and 120°) of open-channels bends.…”

Section: Introductionmentioning

confidence: 99%

An efficient classified radial basis neural network for prediction of flow variables in sharp open-channel bends

et al. 2019

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In this study, a comparative analysis is done to evaluate the ability of classified radial basis function neural network (CRB-FNN) model in estimation of flow variables in sharp open-channel bends with bend angles of 60° and 90°. Accordingly, a RBFNN model is integrated with classification method to design a novel CRBFNN model to predict two velocity and flow depth parameters in a 60° sharp bend. Furthermore, Gholami et al. (Neural Comput Appl 30:1-15, 2018a) pointed out to acceptable ability and more efficiency improvement of hybrid CRBFNN model in prediction of flow variables in 90° sharp open-channel bend compared to simple RBF model. On the other hand, the flow pattern in sharp bends is more complicated than in mild open-channel bends. Moreover, the behavior of flow and its variables is varied in 60° and 90° sharp bends. Therefore, the present paper is aimed to evaluate the performance of RBF and CRBF models in two 60° and 90° (Gholami et al. 2018a) sharp open-channel bends. Available experimental data for velocity and flow depth at six different hydraulic conditions are used to train and test the CRBFNN and simple radial basis function neural network (RBFNN) networks in 60° open-channel bend. Accordingly, efficiency of both RBFNN and CRBFNN models in different bend cross sections is evaluated and compared with each other. The results show that using classified model has improved the simple RBF model performance, as in the CRBFNN model, the error root mean square error and mean absolute error value, 18% and 15.3% for the flow depth prediction and 9% and 5% for the velocity prediction compared to the simple RBFNN model is reduced, respectively. Furthermore, the comparison of model performance in 60° and 90° bends represents that both RBFNN and CRBFNN models in all discharge values in velocity prediction have more ability in 60° bend so that the mean absolute relative error (MARE) value in 60° bend is equal to 0.080 and 0.082 which are lower than MARE values in 90° bend (0.125 and 0.131 for RBFNN and CRBFNN, respectively). Furthermore, both RBFNN and CRBFNN models with lower MARE values equal to 0.015 and 0.012 in 90° bend have more accuracy than the models in 60° bend (0.017 and 0.014). Therefore, the proposed classified models can be used in design and implementation of the curved channels with various bend angles.

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“…For this reason, the use of expert systems is increasing and also being used in a number of sectors of our social and technological life. In fact, in those the use of expert systems is becoming quite a significant aspect during problem solving and decision support [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Designing a Rule Based Expert Systems for Contact Lenses Patients

Aydilek¹,

Gümüşçü²

2018

IJITCS

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Abstract-Expert systems that bring facts and valuable experiences together and make some deductions possible. Expression of relevant knowledge and experience in these structures may be in a set of rules. Learning problems are valid with expert systems. Therefore, they cannot add new rules and information automatically by themselves. Rules are created by human experts on the way and added upon the system. Classification datasets are collections of data commonly used in machine learning that contain and classify the previously obtained experiences. In this study, rules were obtained by using Part, NNge, Prism rule classifier algorithms, and a knowledge base of expert systems was systematically created to achieve enrichment. Enrichment and rule deduction process needs careful and sensitive attention. A combined methodology and study was revealed during this sensitive process. In this context, studies were conducted on five widely used datasets. It was aimed to reduce the redundant, conflicting, subsumed and circular rules in order to create a consistent and complete knowledge base. In this way, a methodology was developed to establish more powerful and richer contents of knowledge base that have higher quality.

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Development of expert systems for the prediction of scour depth under live-bed conditions at river confluences: Application of different types of ANNs and the M5P model tree

Cited by 56 publications

References 44 publications

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

An efficient classified radial basis neural network for prediction of flow variables in sharp open-channel bends

Designing a Rule Based Expert Systems for Contact Lenses Patients

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