Improving semi-empirical equations of ultimate bearing capacity of shallow foundations using soft computing polynomials

Pan, Chan-Ping; Tsai, Hsing‐Chih; Lin, Yong

doi:10.1016/j.engappai.2012.08.014

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…The developed GP-based formula was calibrated and validated using an experimental database, which was obtained from full-scale foundations load tests and from small-scale laboratory footing load tests. CHAN-PING PAN et al (2013) used weighted genetic programming (WGP) and soft computing polynomials (SCP) to provide accurate prediction and formulas for the ultimate bearing capacity. SHERVIN TAJERI et al (2015) and ALAVI & SADROSSADAT (2016) proposed a novel formulation for the ultimate bearing capacity of shallow foundations resting on/in rock masses, using a linear genetic programming.…”

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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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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“…The GP model was found to have a coefficient of determination R 2 of 0?988 and 0?984 in the training and validation sets, respectively, whereas these values were found to be equal to 0?984 and 0?987, respectively, for the EPR model. The derived formulae are as follows (Pan et al, 2013) …”

Section: Shahin Artificial Intelligence Applications In Shallow Foundmentioning

confidence: 99%

“…The results revealed that the GP model can predict the ultimate bearing capacity precisely with a high coefficient of correlation r of 98%. Pan et al (2013) introduced two different models, a GP model and an EPR model, to predict the ultimate bearing capacity of shallow foundations. The inputs of models included the footing width (denoted P 1 ), embedment depth (denoted P 2 ), footing length-to-width ratio (denoted P 3 ), unit weight of soil (denoted P 4 ), and angle of shearing resistance or friction angle of soil (denoted P 5 ).…”

Section: Shahin Artificial Intelligence Applications In Shallow Foundmentioning

confidence: 99%

“…In particular, ANNs have been used for shallow foundations including settlement estimation (Chen et al, 2009;Shahin et al, 2002bShahin et al, , 2003Sivakugan et al, 1998;Soleimanbeigi and Hataf, 2006) and prediction of ultimate bearing capacity (Behera et al, 2013a(Behera et al, , 2013bKalinli et al, 2011;Kuo et al, 2009;Padmini et al, 2008;Provenzano et al, 2004;Soleimanbeigi and Hataf, 2005). Likewise, GP and EPR have been investigated for settlement prediction of shallow foundations (Rezania and Javadi, 2007;Shahin, 2014;Shahnazari et al, 2014) as well as ultimate bearing capacity (Adarsh et al, 2012;Pan et al, 2013;Shahin, 2014;Shahnazari and Tutunchian, 2012;Tsai et al, 2013). The objective of this paper is to provide an overview of some of the popular AI techniques, present a review of the AI applications to date in shallow foundations, and discuss some of the current challenges and future directions.…”

Section: Introductionmentioning

confidence: 99%

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A review of artificial intelligence applications in shallow foundations

Shahin

2014

International Journal of Geotechnical Engineering

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Geotechnical engineering deals with materials (e.g. soil and rock) that, by their very nature, exhibit varied and uncertain behavior because of the imprecise physical processes associated with the formation of these materials. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically based engineering methods. Artificial intelligence (AI) is becoming more popular and particularly amenable to modeling the complex behavior of most geotechnical engineering applications, including foundations, because it has demonstrated superior predictive ability compared to traditional methods. The main aim of this paper is to review the AI applications in shallow foundations and present the salient features associated with the AI modeling development. The paper also discusses the strengths and limitations of AI techniques compared to other modeling approaches.

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Genetic Programming for Modelling of Geotechnical Engineering Systems

Shahin

2015

Handbook of Genetic Programming Applications

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Improving semi-empirical equations of ultimate bearing capacity of shallow foundations using soft computing polynomials

Cited by 5 publications

References 29 publications

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

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

A review of artificial intelligence applications in shallow foundations

Genetic Programming for Modelling of Geotechnical Engineering Systems

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