Abutment scour depth modeling using neuro-fuzzy-embedded techniques

Moradi, Fatemeh; Bonakdari, Hossein; Kişi, Özgür; Ebtehaj, Isa; Shiri, Jalal; Gharabaghi, Bahram

doi:10.1080/1064119x.2017.1420113

Cited by 60 publications

(42 citation statements)

References 65 publications

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“…The optimized model [47] performed better than the classical model [46], as shown in Figure 7. The model of Azimi et al [47] provided excellent results in the training phase, but comparing the accuracy of this model with the ELM in the test phase, which indicated the generalizability of a model, demonstrated the superior performance of the developed ELM.…”

Section: Resultsmentioning

confidence: 94%

“…Consequently, the results indicated that neglecting the abutment geometry parameter in scour depth evaluation was significant, whereby ignoring it reduced d s /L performance and accuracy. To evaluate the proposed ELM model's accuracy in predicting the scour depth around abutments in clear water condition, the performance of the developed ELM-based model was compared with conventional regression-based models [22,23] and recently artificial intelligence (AI) based techniques [46,47] by combined accuracy (CA) index for training and testing phases. The lowest CA at both phases is related to Muzammil [23], who provided his model using conventional nonlinear repression.…”

Section: Resultsmentioning

confidence: 99%

“…However, there was a significant difference between the performance of regression methods and artificial intelligence methods. In addition to the developed ELM model, Moradi et al [46] and Azimi et al [47] models were also classified as AI-based models. Both of these methods were presented based on ANFIS so that the Moradi et al [46] was provided as classical ANFIS, while the model of Azimi et al [47] was introduced as a multi-objective evolutionary optimization of ANFIS.…”

Section: Resultsmentioning

confidence: 99%

“…The classic FFNN is trained with a backpropagation algorithm. The ANFIS, which is a combination of neural network and fuzzy logic, is employed by Moradi et al [46] to calculate the abutment scour depth. The authors compared different techniques for generating the fuzzy inference systems (FIS) (i.e., sub-clustering (SC), fuzzy c-means clustering (FCM), and grid partitioning (GP)).…”

Section: Introductionmentioning

confidence: 99%

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A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

Self Cite

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“…In this context, it could be concluded that the empirical models do not experience proper generalization ability for estimating the maximum scour below the PLs. In recent decades, soft computing models such as genetic programming (GP) [4][5][6][7], artificial neural network (ANN) [8][9][10][11], adaptive neuro fuzzy interface system (ANFIS) [12][13][14][15], and regression tree [16,17] were successfully utilized to forecast the scour depth below PLs. It should be noted that the scour rate is dependent on the flow direction, flow characteristics, PL diameter, and sediment properties.…”

mentioning

confidence: 99%

Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm

Ehteram

Ahmed

Ling

et al. 2020

Water

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In this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies’ optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions.

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ANFIS and Deep Learning based missing sensor data prediction in IoT

Guzel

Kök

Akay

et al. 2019

Concurrency and Computation

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Summary Internet of Things (IoT) consists of billions of devices that generate big data which is characterized by the large volume, velocity, and heterogeneity. In the heterogeneous IoT ecosystem, it is not so surprising that these sensor‐generated data are considered to be noisy, uncertain, erroneous, and missing due to the lack of battery power, communication errors, and malfunctioning devices. This paper presents Deep Learning (DL) and Adaptive‐Network based Fuzzy Inference System (ANFIS) based prediction models for missing sensor data problem in IoT ecosystem. First, we build ANFIS based models and optimize their parameters. Then, we construct DL based models by using Long Short Term Memory (LSTM) network structure and optimize its parameters by applying the grid search method. Finally, we evaluate all the proposed models with Intel Berkeley Lab dataset. Experimental results demonstrate that the proposed models can significantly improve the prediction accuracy and may be promising for missing sensor data prediction.

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Abutment scour depth modeling using neuro-fuzzy-embedded techniques

Cited by 60 publications

References 65 publications

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm

ANFIS and Deep Learning based missing sensor data prediction in IoT

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