Use of machine learning techniques for predicting the bearing capacity of piles

Gomes, Yago Ferreira; Verri, Filipe Alves Neto; Ribeiro, Dimas Betioli

doi:10.28927/sr.2021.074921

Cited by 3 publications

(2 citation statements)

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“…Six machine learning algorithms with different biases were taught by Gomes et al (2021) and validated using a leaveone-out cross validation method. Using the Décourt-Quaresma dataset, random forest (RF) was the approach that performed the best.…”

Section: Machine Learning For Predicting Pile Capacitymentioning

confidence: 99%

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Predicting Geotechnical Axial Capacity of Reinforced Concrete Driven Pile Using Machine Learning Technique

Zi Xun,

Abdullah

2023

MJCE

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Modified Meyerhof method is a popular method to calculate pile geotechnical axial capacity in Malaysia currently. From past experience, pile design based on empirical and analytical method produce variability of predicted capacity, in which, there is a wide scatter of predicted capacities and tendency for the predictions to be conservative, i.e. to underestimate the load capacity. This study provides options of machine learning and statistical approach for prediction of pile capacity based on soil investigation and dynamic pile load test result. It serves as an additional checking for engineer during design of pile based on conventional empirical method. It also helps to provide deeper insights of non-linear variables related to pile capacity through machine learning and statistical approach. This study helps engineer to design pile foundation optimally, economically and safely. The prediction of pile geotechnical axial capacity with machine learning technique and statistical approach for local marine clay soil in Penang, Malaysia is proposed in this study. The information from soil investigation report and dynamic pile load test report are gathered from six projects at Batu Kawan and Nibong Tebal located in Penang state that contributed 439 numbers of data. The skin friction factor, end bearing factor and pile geotechnical axial capacity are computed and predicted using empirical method, machine learning model and statistical model. Support Vector Machine illustrate best fit model for predicting skin friction factor with R2 of 0.517 while Random Forest seems to be the best fit model for predicting end bearing factor with R2 of 0.264. Random Forest is found to be the best model in predicting the geotechnical pile axial capacity compare to other models as it explains 96.2% of the variability of pile capacity.

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Section: Machine Learning For Predicting Pile Capacitymentioning

confidence: 99%

“…Using the Décourt-Quaresma dataset, random forest (RF) was the approach that performed the best. The study also included a case study that demonstrated the top performing models outperformed semiempirical approaches in two of the three piles taken into consideration (Gomes et al, 2021).…”

Section: Machine Learning For Predicting Pile Capacitymentioning

confidence: 99%

Predicting Geotechnical Axial Capacity of Reinforced Concrete Driven Pile Using Machine Learning Technique

Zi Xun,

Abdullah

2023

MJCE

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Data-driven modeling of ultimate load capacity of closed- and open-ended piles using machine learning

Ülker,

Altınok,

Taşkın

2023

International Journal of Geotechnical Engineering

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Field pile load tests are fairly expensive experiments that can be applied to certain pile types required to be installed in full scale. Hence, it is neither practical nor efficient to perform a load test for every installed pile. While there exist many empirical relations for predicting pile capacities, such methods typically suffer from accuracy and generality. Therefore, current geotechnical practice still looks for methods to accommodate full-scale pile load testing to serve as both accurate and practical tools. In this study, load bearing capacities of closed and open-ended piles in cohesive and cohesionless soils are predicted using machine learning. Nine such methods are utilized in the analyses where CPT and pile data are considered as the learning features necessary to teach those methods the database gathered via a comprehensive search. Then, machine learning models are developed, and the databases are separated into five-folds according to the cross-validation-principle, which are used for both training and testing of the machine learning methods. Model predictions are validated with classical empirical equations.Results indicate that the Relevance Vector Regression and the Random Forest methods typically generate considerably better predictions than the other methods and empirical equations. Hence, machine learning methods are found as reliable tools to predict the pile load capacities of both openended and closed-ended piles provided that there is a large enough database and an appropriate method to use.

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Data-Driven Prediction of Stability of Rock Tunnel Heading: An Application of Machine Learning Models

et al. 2022

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In this paper, Artificial Neural Networks (ANN) have been utilized to predict the stability of a planar tunnel heading in rock mass based on the well-defined Hoek–Brown (HB) yield criterion. The HB model was developed to capture the failure criterion of rock masses. To provide the datasets for an ANN model, the numerical upper bound (UB) and lower bound (LB) solutions obtained from the finite element limit analysis (FELA) with the HB failure criterion for the problem of tunnel headings are derived. The sensitivity analysis of all influencing parameters on the stability of rock tunnel heading is then performed on the developed ANN model. The proposed solutions will enhance the dependability and preciseness of predicting the stability of rock tunnel heading. Note that the effect of the unlined length ratio has not been explored previously but has been found to be of critical importance and significantly contributes to the failure of rock tunnel heading. By utilizing the machine learning-aided prediction capability of the ANN approach, the numerical solutions of the stability of tunnel heading can be accurately predicted, which is better than the use of the classic linear regression approach. Thus, providing a better and much safer assessment of mining or relatively long-wall tunnels in rock masses.

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Use of machine learning techniques for predicting the bearing capacity of piles

Cited by 3 publications

References 0 publications

Predicting Geotechnical Axial Capacity of Reinforced Concrete Driven Pile Using Machine Learning Technique

Predicting Geotechnical Axial Capacity of Reinforced Concrete Driven Pile Using Machine Learning Technique

Data-driven modeling of ultimate load capacity of closed- and open-ended piles using machine learning

Data-Driven Prediction of Stability of Rock Tunnel Heading: An Application of Machine Learning Models

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