New method for predicting the ultimate bearing capacity of driven piles by using Flap number

Fatehnia, Milad; Kamal, Tawfiq; Hataf, Nader; Erman, Ozguven Eren

doi:10.1007/s12205-013-0315-z

Cited by 37 publications

(15 citation statements)

References 16 publications

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“…(6) and (8), respectively. These two criteria were used in previous researches [41][42][43]. The mentioned values for the best obtained models in each method for counter regenerative, perforated counter and cross flow M-cycles were given in Table (4a) and …”

Section: Model For Predicting the Product Air Temperature Of The Secomentioning

confidence: 99%

“…These optimal values could be determined, for example, in a multi-objective optimization approach considering economic and thermal comfort as objective functions. 42 of 73 Fig. (14): Effects of the inlet temperature of the working air on (a) product air temperature; (b) inlet/outlet temperature difference; (c) product air absolute humidity; (d) outlet/inlet absolute humidity difference; (e)…”

Section: Effects Of the Length Of Product Channelmentioning

confidence: 99%

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A novel approach using predictive models for performance analysis of desiccant enhanced evaporative cooling systems

Sohani

Sayyaadi

Balyani

et al. 2016

Applied Thermal Engineering

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Section: Model For Predicting the Product Air Temperature Of The Secomentioning

confidence: 99%

Section: Effects Of the Length Of Product Channelmentioning

confidence: 99%

A novel approach using predictive models for performance analysis of desiccant enhanced evaporative cooling systems

Sohani

Sayyaadi

Balyani

et al. 2016

Applied Thermal Engineering

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“…It is known that, in numerical modeling, uncertainty in the input parameters for simulation can reduce the accuracy of the method (Harandizadeh et al 2018). Because empirical methods use linear and nonlinear regression techniques, these studies have shown significant differences between their results and those of the experimental method (Milad et al 2015;Ebrahimian and Movahed 2017;Harandizadeh et al 2018Harandizadeh et al , 2019. It can be seen that approaches that do not require prior assumptions, such as artificial intelligence models, would provide better solutions.…”

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

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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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“…A similar issue was considered by POOYA NEJAD & JAKSA (2017) -the application of ANN generated a series of charts for predicting pile behavior which can be useful in pile design problems. MILAD et al (2015) used ANN to predict pile capacity with respect to the flap number (number of hammer strikes), soil parameters, and pile geometry.…”

Section: Introductionmentioning

confidence: 99%

The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations trained on Scarce Data

Bagińska

Srokosz

2018

KSCE J Civ Eng

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This study is focused on determining the potential of using deep neural networks (DNNs) to predict the ultimate bearing capacity of shallow foundation in situations when the experimental data which may be used to train networks is scarce. Two experiments involving testing over 17000 networks were conducted. The first experiment was aimed at comparing the accuracy of shallow neural networks and DNNs predictions. It shows that when the experimental dataset used for preparing models is small then DNNs have a significant advantage over shallow networks. The second experiment was conducted to compare the performance of DNNs consisting of different number of neurons and layers.Obtained results indicate that the optimal number of layers varies between 5 to 7. Networks with less and -surprisingly -more layers obtain lower accuracy. Moreover, the number of neurons in DNN has a lower impact on the prediction accuracy than the number of DNN's layers. DNNs perform very well, even when trained with only 6 samples. Basing on the results it seems that when predicting the ultimate bearing capacity with ANN models obtaining small but high-quality experimental training datasets instead of large training datasets affected by a higher error is an advisable approach.

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New method for predicting the ultimate bearing capacity of driven piles by using Flap number

Cited by 37 publications

References 16 publications

A novel approach using predictive models for performance analysis of desiccant enhanced evaporative cooling systems

A novel approach using predictive models for performance analysis of desiccant enhanced evaporative cooling systems

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

The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations trained on Scarce Data

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