Gaussian Process Regression Technique to Estimate the Pile Bearing Capacity

Momeni, Ehsan; Dowlatshahi, Mohammad Bagher; Omidinasab, Fereydoon; Maizir, Harnedi; Armaghani, Danial Jahed

doi:10.1007/s13369-020-04683-4

Cited by 93 publications

(23 citation statements)

References 73 publications

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“… Kernel function Kernel equation Rational quadratic Exponential Kernel Matern 3/2 Squared exponential Matern 5/2 ARD squared exponential ARD exponential ARD matern 3/2 ARD matern 5/2 ARD rational quadratic Where is the signal standard deviation (Std), is the characteristic length scale, r is the Euclidean distance between and which is defined by , is a positive-valued scale-mixture parameter, and is a separate length scale for each predictor , . The values of (hyper-parameters) is calculated by maximizing the marginal likelihood 61 , 62 . …”

Section: Methodsmentioning

confidence: 99%

“… Where is the signal standard deviation (Std), is the characteristic length scale, r is the Euclidean distance between and which is defined by , is a positive-valued scale-mixture parameter, and is a separate length scale for each predictor , . The values of (hyper-parameters) is calculated by maximizing the marginal likelihood 61 , 62 . …”

Section: Methodsmentioning

confidence: 99%

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Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

Karbasi

Jamei

Ahmadianfar³

et al. 2021

Sci Rep

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In the present study, two kernel-based data-intelligence paradigms, namely, Gaussian Process Regression (GPR) and Kernel Extreme Learning Machine (KELM) along with Generalized Regression Neural Network (GRNN) and Response Surface Methodology (RSM), as the validated schemes, employed to precisely estimate the elliptical side orifice discharge coefficient in rectangular channels. A total of 588 laboratory data in various geometric and hydraulic conditions were used to develop the models. The discharge coefficient was considered as a function of five dimensionless hydraulically and geometrical variables. The results showed that the machine learning models used in this study had shown good performance compared to the regression-based relationships. Comparison between machine learning models showed that GPR (RMSE = 0.0081, R = 0.958, MAPE = 1.3242) and KELM (RMSE = 0.0082, R = 0.9564, MAPE = 1.3499) models provide higher accuracy. Base on the RSM model, a new practical equation was developed to predict the discharge coefficient. Also, the sensitivity analysis of the input parameters showed that the main channel width to orifice height ratio (B/b) has the most significant effect on determining the discharge coefficient. The leveraged approach was applied to identify outlier data and applicability domain.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

Karbasi

Jamei

Ahmadianfar³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Furthermore, some other studies recommend the use of dynamic equations, which are based on the pile and hammer properties, to estimate the bearing capacity of piles; however, as stated by Milad et al (Milad et al 2015), there are many assumption and simplification in estimating the bearing capacity using the aforementioned equations. (Ehsan Momeni et al 2020; HAN Jiwei et al 2020).…”

Section: Introductionmentioning

confidence: 99%

An Optimization Algorithm for Exponential Curve Model of Single Pile Bearing Capacity

Peng

Jin-ying

et al. 2021

Geotech Geol Eng

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The ultimate bearing capacity of single pile is very important to engineering safety, so correctly predicting its value becomes an important part of engineering safety. Based on the traditional exponential curve model, a parameter optimization algorithm of the exponential curve model of single pile bearing capacity, which combines the golden section method and the linear least square method, is proposed. In order to verify the reliability of the proposed optimization algorithm, the measured data of the building engineering in the literature were optimized and calculated. Through comparison, it is found that the optimization algorithm is closer to the measured value than the traditional exponential curve model algorithm, which can better guide the engineering practice, and verify the effectiveness and superiority of the proposed parameter optimization method.

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“…With the advent of artificial neural networks (ANNs) and fuzzy systems, several civil engineering problems have been efficiently solved (e.g., Momeni et al 2015a, b;Sadrossadat et al 2016Sadrossadat et al , 2018Moayedi and Armaghani 2017;Jebur et al 2018;Dehghanbanadaki et al 2019;Khari et al 2019a;Moayedi et al 2020;Ardakani and Kordnaeij 2019;Jokar et al 2019;Moayedi et al 2020;Momeni et al 2020). In geotechnical engineering, a comprehensive review of the use of ANNs has been provided by Shahin et al (2001).…”

Section: Introductionmentioning

confidence: 99%

Estimation of ultimate bearing capacity of driven piles in c-φ soil using MLP-GWO and ANFIS-GWO models: a comparative study

et al. 2020

Self Cite

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A new forecast method is proposed in order to improve the estimation accuracy of the ultimate bearing capacity (UBC) of single driven piles. The performance of the adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models have been boosted using gray wolf optimization (GWO). To train the models, data were used from 100 driven piles in c-u soil in different locations. The input parameters were pile area (m 2 ), pile length (m), flap number, average cohesion (kN/m 2 ) and friction angle (°), average soil specific weight (kN/m 2 ) and average pile-soil friction angle (°), and the output was UBC. The results showed that both the MLP and ANFIS methods had good abilities to predict the UBC of the piles; however, the MLP-GWO model with a topology of 7 9 10 9 10 9 1 provided better results. The performance indices of this model were a RMSE of 1.86 kN and R 2 of 0.991 for the test data. The validity of the MLP-GWO model was tested using a new experimental dataset, and the difference between actual and estimated UBC was very low (about 2%) and confirmed the high accuracy of the model. It was observed that the theoretical methods underestimated the UBC significantly, while the proposed model estimated the UBC with high accuracy. Sensitivity analysis confirmed that the pile area and length were the most effective factors for estimation of UBC.Keywords Piles Á ANFIS Á Gray wolf optimization Á Ultimate bearing capacity Á Sandy soil Á Clay soil Communicated by V. Loia.

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Gaussian Process Regression Technique to Estimate the Pile Bearing Capacity

Cited by 93 publications

References 73 publications

Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

Toward the accurate estimation of elliptical side orifice discharge coefficient applying two rigorous kernel-based data-intelligence paradigms

An Optimization Algorithm for Exponential Curve Model of Single Pile Bearing Capacity

Estimation of ultimate bearing capacity of driven piles in c-φ soil using MLP-GWO and ANFIS-GWO models: a comparative study

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