Prediction of pile settlement based on cone penetration test results: An ANN approach

Baziar, Mohammad Hassan; Azizkandi, Alireza Saeedi; Kashkooli, Armin

doi:10.1007/s12205-012-0628-3

Cited by 35 publications

(13 citation statements)

References 11 publications

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“…However, in order to have a comprehensive and accurate model, especially in the case of CPRFs for high rise buildings with large number of piles, there will be a large number of structural elements and the calculation time makes the analysis so difficult. To cope with this difficulty, recently, some research works are focused on the application of soft computing techniques, as the speedy and powerful tool and alternative for other timeconsuming and expensive methods, to the problem of piles and piled raft foundations behaviors [14,15]. In the research of Armaghani et al (2015) [14], the ultimate bearing capacity is investigated, while Baziar et al (2014) [15] evaluated the static induced settlements.…”

Section: Introductionmentioning

confidence: 99%

“…To cope with this difficulty, recently, some research works are focused on the application of soft computing techniques, as the speedy and powerful tool and alternative for other timeconsuming and expensive methods, to the problem of piles and piled raft foundations behaviors [14,15]. In the research of Armaghani et al (2015) [14], the ultimate bearing capacity is investigated, while Baziar et al (2014) [15] evaluated the static induced settlements. Hence, the dynamically loaded induced settlements of piled rafts and the application of soft computing techniques to this problem have not been completely studied.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the application of neural network modeling and evolutionary polynomial regressions (EPRs), which are believed to be common ways to accurately and timely predict engineering complicated functions, can be examined. Different attempts to apply neural networks and EPRs to model different civil and geotechnical problems are presented in the literature [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. They are well-applied in a wide range of problems from deep soil stabilizations, concrete, and their related structures, compressive strength of soils, rocks, and stabilized samples, bearing capacity of shallow and deep foundations, lateral spreading, rock mechanics, rock engineering, and soil mechanics [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Ghorbani

Niavol

2017

Applied Computational Intelligence and Soft Computing

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Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using threedimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile's configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Ghorbani

Niavol

2017

Applied Computational Intelligence and Soft Computing

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“…Such factors can be determined from design charts recommended by Poulos and Davis (1980). Using this approach, regarding a rigid pile driven into a semiinfinite soil with a 0.5 Poisson' ratio, 0 is the only influencing parameter requiring consideration (Baziar et al, 2015). 's (1977) approach Vesic (1977) suggested that pile settlement can be determined from the summation of three components, S1, S2, and S3, using the following simplified formulas (Eqs.…”

Section: Polous and Davis' (1980) Methodologymentioning

confidence: 99%

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

et al. 2019

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This study was implemented to examine pile load-settlement response and to develop a rapid, highly efficient predictive intelligent model, using a new computational intelligence (CI) algorithm. To achieve this aim, a series of experimental pile load tests were performed on steel, closed-ended pile models consisting of three piles with aspect ratios of 25, 17, and 12 in an attempt to make site in-situ pile-load tests unnecessary. An optimised, evolutionary, supervised Levenberg-Marquardt (LM) training algorithm was used for this process due to its remarkably robust performance. The model piles were penetrated and tested in three sand relative densities; dense, medium, and loose. Applied load (P), pile effective length (lc), pile flexural rigidity (EA), pile slenderness ratio (lc/d) and interface friction angle (δ) were identified, based on a comprehensive statistical analysis, as these parameters play a key role in governing pile settlement. To evaluate the efficiency and the generalisation ability of the proposed algorithm, graphical comparisons were made between the proposed algorithm and the experimental results with further comparisons made with conventional prediction approaches. The results revealed outstanding agreement between the targeted and predicted pile-load settlement with a coefficient of correlation of 0.985 and a Pearson's correlation coefficient, P = 2.22 x10-32 and root mean square error (RMSE) of 0.059 respectively. This, in parallel with a non-significant mean square error level (MSE) of 0.002, validates the feasibility of the proposed method and its potential in future applications.

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“…Alternatively, artificial neural networks (ANNs) have been used recently to predict the ultimate capacity of driven piles based on in situ tests (e.g., Shahin 2014; Kordjazi et al 2014;Azizkandi et al 2014;Mohammad et al 2015). However, most of ANNs models available in the literature generally used a limited number of data sets and fewer models were developed based on more accurate measurements of soils properties from the SPT or CPT results.…”

Section: Introductionmentioning

confidence: 99%

Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

Benali

Boukhatem

Hussien

et al. 2017

Journal of Civil Engineering and Management

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Abstract. This paper presents an application of two advanced approaches, Artificial Neural Networks (ANN) and Principal Component Analysis (PCA) in predicting the axial pile capacity. The combination of these two approaches allowed the development of an ANN model that provides more accurate axial capacity predictions. The model makes use of Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian Regularization (BR), and it is established through the incorporation of approximately 415 data sets obtained from data published in the literature for a wide range of uncemented soils and pile configurations. The compiled database includes, respectively 247 and 168 loading tests on largeand low-displacement driven piles. The contributions of the soil above and below pile toe to the pile base resistance are pre-evaluated using separate finite element (FE) analyses. The assessment of the predictive performance of the new method against a number of traditional SPT-based approaches indicates that the developed model has attractive capabilities and advantages that render it a promising tool. To facilitate its use, the developed model is translated into simple design equations based on statistical approaches.

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Prediction of pile settlement based on cone penetration test results: An ANN approach

Cited by 35 publications

References 11 publications

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

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