Prediction of vertical pile capacity of driven pile in cohesionless soil using artificial intelligence techniques

Mohanty, Ranajeet; Suman, Shakti; Das, Sarat Kumar

doi:10.1080/19386362.2016.1269043

Cited by 30 publications

(6 citation statements)

References 35 publications

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“…The above methods promote the progress of the prediction level of ultimate bearing capacity of monopiles, but do not sufficiently consider the influence of the pile's gravity. In addition, some researchers have also used artificial intelligence techniques to establish the ulti-mate bearing capacity prediction models [9][10][11][12][13][14], and these methods do not require specific formulas and thus have convenient features. However, the establishment and development of prediction models require the collection of a sufficient number of valid test data.…”

Section: Introductionmentioning

confidence: 99%

Prediction Method of Vertical Ultimate Compressive Bearing Capacity of Single Pile in Soft Soil considering the Influence of Gravity

et al. 2023

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Considering that the pile foundation is widely used in soft soil, in order to improve the prediction accuracy of the vertical ultimate compressive bearing capacity of a single pile, this paper puts forward a calculation model of the pile side friction resistance in homogeneous soil based on the previous research and further deduces the calculation method of pile body internal force in layered soil under the influence of gravity field. In this research, the transmission of load along the pile body is related to the previous research on the ultimate end resistance, and an analytical calculation for the vertical ultimate compressive capacity of a single pile is proposed. The novelty of the method is that the ultimate bearing capacity is solved from the perspective of force transfer while considering its own gravity, making the results more accurate. In order to verify the reliability of the method, four engineering test piles in the soft soil area of Suzhou, China, were selected, and it is indicated that the results of the static load test and finite element simulation are consistent with the proposed method, proving that the method proposed in this paper for predicting the vertical ultimate compressive bearing capacity of single pile in soft soil area has an excellent effect.

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Section: Introductionmentioning

confidence: 99%

Prediction Method of Vertical Ultimate Compressive Bearing Capacity of Single Pile in Soft Soil considering the Influence of Gravity

et al. 2023

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“…In the last decades, machine learning-based models have proved to be a helpful alternative to deal with the multivariate and complex nature of the phenomena in various disciplines of engineering [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. Optimized kernel logistic regression models were employed in [39] for landslide susceptibility assessment.…”

Section: Introductionmentioning

confidence: 99%

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Dinh

Nguyen

Tran

et al. 2021

Mathematical Problems in Engineering

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Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

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“…Nevertheless, the machine should first learn the effect of each parameter influencing the pile behavior. Therefore, each influencing factor should be investigated individually to be used in the machine learning process [33][34][35][36][37]. The parameters whose values are used to control the learning process are called hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

A Parametric Numerical Study for Diagnosing the Failure of Large Diameter Bored Piles Using Supervised Machine Learning Approach

2021

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The full-scale static pile loading test is without question the most reliable methodology for estimating the ultimate capacity of large diameter bored piles (LDBP). However, in most cases, the obtained load-settlement curves from LDBP loading tests tend to increase without reaching the failure point or an asymptote. Loading an LDBP until reaching apparent failure is seldom practical because of the significant amount of settlement usually required for the full shaft and base mobilizations. With that in mind, the supervised learning algorithm requires a huge labeled data set to train the machine properly, which makes it ideal for sensitivity analysis, forecasting, and predictions, among other unsupervised algorithms. However, providing such a huge dataset of LDBP loaded to failure tests might be very complicated. In this paper, a novel practice has been proposed to establish a labeled dataset needed to train supervised machine learning algorithms on accurately predicting the ultimate capacity of an LDBP. A comprehensive numerical parametric study was carried out to investigate the effect of both pile geometrical and soil geotechnical parameters on both the ultimate capacity and settlement of an LDBP. This study was based on field measurements of loaded to failure LDBP tests. Results of the 29 applied models were compared with the calibrated model results, and the variation in LDBP behavior due to change in any of the hyperparameters was discussed. Accordingly, three primary characteristics were identified to diagnose the failure of LDBPs. Those characteristics were utilized to establish a decision tree of a supervised machine learning algorithm that can be used to predict the ultimate capacity of an LDBP.

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Prediction of vertical pile capacity of driven pile in cohesionless soil using artificial intelligence techniques

Cited by 30 publications

References 35 publications

Prediction Method of Vertical Ultimate Compressive Bearing Capacity of Single Pile in Soft Soil considering the Influence of Gravity

Prediction Method of Vertical Ultimate Compressive Bearing Capacity of Single Pile in Soft Soil considering the Influence of Gravity

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

A Parametric Numerical Study for Diagnosing the Failure of Large Diameter Bored Piles Using Supervised Machine Learning Approach

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