Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: An evolutionary approach

Shahnazari, Habib; Tutunchian, Mohammad Amin

doi:10.1007/s12205-012-1651-0

Cited by 28 publications

(8 citation statements)

References 26 publications

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“…ANN and fuzzy inference models. Similarly, Shahnazari and Tutunchian (2012) developed another GP model for predicting the ultimate capacity of shallow foundations on cohesionless soils. In this case, the GP model was calibrated and validated using an experimental database consisting of 100 load tests.…”

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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Section: Shahin Artificial Intelligence Applications In Shallow Foundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review of artificial intelligence applications in shallow foundations

Shahin

2014

International Journal of Geotechnical Engineering

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“…Numerous studies have leveraged ML models like artificial neural networks (ANNs), support vector machines (SVMs), evolutionary polynomial regression (EPR), neuro-fuzzy systems, and hybrid ANNs to predict the settlement of unreinforced soil foundations [27][28][29][30][31][32]. However, the research related to the application of ML techniques in RSFs is very limited.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

Raja,

Abdoun,

El-Sekelly

2024

Buildings

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This study introduces a novel application of gene expression programming (GEP) for the reliability analysis (RA) of reinforced soil foundations (RSFs) based on settlement criteria, addressing a critical gap in sustainable construction practices. Based on the principles of probability and statistics, the soil uncertainties were mapped using the first-order second-moment (FOSM) approach. The historical data generated via a parametric study on a validated finite element numerical model were used to train and validate the GEP models. Among the ten developed GEP frameworks, the best-performing model, abbreviated as GEP-M9 (R2 = 0.961 and RMSE = 0.049), in the testing phase was used to perform the RA of an RSF. This model’s effectiveness in RA was affirmed through a comprehensive evaluation, including parametric sensitivity analysis and validation against two independent case studies. The reliability index (β) and probability of failure (Pf) were determined across various coefficient of variation (COV) configurations, underscoring the model’s potential in civil engineering risk analysis. The newly developed GEP model has shown considerable potential for analyzing civil engineering construction risk, as shown by the experimental results of varying settlement values.

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“…Different forms of AI-based techniques have recently been used by different researchers to address the UBC problem of shallow foundations. Artificial neural networks (ANNs), fuzzy inference systems (FISs), adaptive neuro-fuzzy inference systems (ANFISs), ant colony optimization (ACO), genetic programming (GP), weighted genetic programming (WGP), soft-computing polynomials (SCP), support vector machine (SVM), random forest (RF), and relevance vector machine (RVM) have all been used to successfully estimate the UBC of shallow foundations on soil [11,[22][23][24][25][26]. Soft computing methodologies are more accurate than analytical formulas, according to all of these studies.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach

Ahmad

Wróblewski

et al. 2021

Applied Sciences

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This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.

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Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: An evolutionary approach

Cited by 28 publications

References 26 publications

A review of artificial intelligence applications in shallow foundations

A review of artificial intelligence applications in shallow foundations

Exploring the Potential of Machine Learning in Stochastic Reliability Modelling for Reinforced Soil Foundations

Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach

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