GMDH-GEP to predict free span expansion rates below pipelines under waves

Najafzadeh, Mohammad; Saberi-Movahed, Farid

doi:10.1080/1064119x.2018.1443355

Cited by 76 publications

(33 citation statements)

References 35 publications

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“…Clearly, a tree of polynomials is developed by utilizing the quadratic type provided in Eq. (20) whose coefficients are achieved by the least-square method (Najafzadeh et al ., 2013; Najafzadeh and Saberi-Movahed, 2019). The coefficients of each quadratic function G i are obtained for an optimum fitting for that output associated with the total pairs of input–output data sets based on minimizing Eq.…”

Section: Methodsmentioning

confidence: 99%

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Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

Harandizadeh

2020

AIEDAM

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This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests, in situ field CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

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Section: Methodsmentioning

confidence: 99%

“…(20) for all range of M data set triples. It is observed that this process is repeated for every neuron of any succeeding hidden layer as outlined by the interconnection topology in this network (Najafzadeh and Tafarojnoruz, 2016; Najafzadeh et al ., 2018; Najafzadeh and Saberi-Movahed, 2019).…”

Section: Methodsmentioning

confidence: 99%

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

Harandizadeh

2020

AIEDAM

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“…The laboratory studies indicated that the scour rates rely on wave properties, physical properties of materials (such as soil (sandy or clay), erosive materials, Water 2020, 12, 902 9 of 20 and the size of bed materials), and the PL geometry (PL arrangement). The following equations are used to define the relationship between scour propagation and effective parameters [34]:…”

Section: Datasetsmentioning

confidence: 99%

“…The following equations have been proposed for the estimation of scour rates. They use the least square method to estimate scour rates [34].…”

Section: Comparison Analysismentioning

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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“…In recent years, with the application of machine learning in various fields of science and engineering [6][7][8][9][10][11], many researchers use data-driven methods to solve geological problems. Some intelligent systems [2,12,13] have been used to improve the prediction and accuracy of sonic wave velocity prediction when sonic logs have been lost due to poor storage, poor logging, failure of logging instruments, and bad hole conditions.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity

Liu

Zhao

Wang

2021

Mathematical Problems in Engineering

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The existing artificial intelligence model uses single-point logging data as the eigenvalue to predict shear wave travel times (DTS), which does not consider the longitudinal continuity of logging data along the reservoir and lacks the multiwell data processing method. Low prediction accuracy of shear wave travel time affects the accuracy of elastic parameters and results in inaccurate sand production prediction. This paper establishes the shear wave prediction model based on the standardization, normalization, and depth correction of conventional logging data with five artificial intelligence methods (linear regression, random forest, support vector regression, XGBoost, and ANN). The adjacent data points in depth are used as machine learning eigenvalues to improve the practicability of interwell and the accuracy of single-well prediction. The results show that the model built with XGBoost using five points outperforms other models in predicting. The R2 of 0.994 and 0.964 are obtained for the training set and testing set, respectively. Every model considering reservoir vertical geological continuity predicts test set DTS with higher accuracy than single-point prediction. The developed model provides a tool to determine geomechanical parameters and give a preliminary suggestion on the possibility of sand production where shear wave travel times are not available. The implementation of the model provides an economic and reliable alternative for the oil and gas industry.

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GMDH-GEP to predict free span expansion rates below pipelines under waves

Cited by 76 publications

References 35 publications

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

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

Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity

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