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Machine Learning Models to Evaluate the Load-Settlement Behavior of Piles from Cone Penetration Test Data

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Abstract

The evaluation of the load-settlement behavior of piles is crucial in meeting the strength and serviceability criteria for pile analysis and design. The most reliable approach for estimating this behavior is by conducting pile load tests. However, due to the considerable expense and time requirements of these tests, the load-transfer methods were used routinely in practice. The objective of this study is to explore the potential application of several machine learning (ML) algorithms to evaluate the load-settlement behavior of axially loaded single square precast prestressed concrete from cone penetration test (CPT) data. Several ML models such as artificial neural network (ANN), random forest (RF), and gradient boosted tree (GBT), were developed to estimate the load-settlement behavior from CPT data (corrected cone tip resistance, qt, and sleeve friction, fs). A database of load-settlement curves of 64 static pile load tests and corresponding CPT data were compiled and used for the development of these ML models. The developed ANN, RF, and GBT models are evaluated based on several statistical criteria. The load-settlement curves predicted using the developed ML models were compared with the measured curves from pile load tests and the load-settlement curves predicted using the conventional load-transfer methods. The results of this study demonstrated the great potential of using ML models to predict the load-settlement behavior of axially loaded piles from CPT data. The comparison clearly shows that ML models outperformed the load-transfer methods. The results showed that both the GBT and ANN algorithms demonstrated to be the best-performing ML models.

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Data Availability

Some or all data, or models, used during the study are available from the corresponding author by request.

Abbreviations

ANN:

Artificial neural network

B:

Pile width

COV:

Coefficient of variation

CPT:

Cone penetration test

DT:

Decision tree

FFNN:

Feed-forward neural network

FHWA:

Federal Higway Administration

fs :

Sleeve friction

fs , Avg :

Average sleeve friction

FTNN:

Focused time-delay neural network

GBT:

Gradient boosted tree

HON:

High-order neural network

Le :

Embedded length of pile

ML:

Machine learning

MLP:

Multilayer perception

PPC:

Precast prestressed concrete

Pfit :

Best fit normalized load

Pm :

Measured normalized load

Pp :

Predicted normalized load

PUNN:

Product-unit neural network

qt :

Corrected cone tip resistance

qt , Avg :

Average cone tip resistance

qt-tip , 4B above :

Average cone tip resistance within 4 width above pile tip

qt-tip , 8B :

Average cone tip resistance within 8 width above pile tip

qt-tip , 4B below :

Average cone tip resistance within 4 width below pile tip

RNN:

Recurrent neural network

R2 :

Coefficient of determination

RF:

Random forest

RMSE:

Root mean squared error

si :

Normalized settlement

SGD:

Stochastic gradient descent

SPT:

Standard penetration test

zb :

Base settlement

µ:

Arithmetic mean

σ:

Standard deviation

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Acknowledgements

The authors would like to express gratitude to the Louisiana Department of Transportation and Development (LA DOTD) engineers for their continuous support and help throughout the study.

Funding

This research project is funded by the Louisiana Transportation Research Center (LTRC Project No. 17-2GT) and the Louisiana Department of Transportation and Development (State Project No. DOTLT1000165).

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Authors and Affiliations

  1. Louisiana Transportation Research Center, Louisiana State University, Baton Rouge, LA, 70808, USA

    Murad Y. Abu-Farsakh

  2. Ardaman and Associates, Inc., Baton Rouge, LA, 70810, USA

    Mohammad Moontakim Shoaib

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  1. Murad Y. Abu-Farsakh
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Contributions

All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by the two authors. The first draft of the manuscript was written by MS and was reviewed and revised by MA-F. All authors read and approved the final manuscript.

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Correspondence to Murad Y. Abu-Farsakh.

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Abu-Farsakh, M.Y., Shoaib, M.M. Machine Learning Models to Evaluate the Load-Settlement Behavior of Piles from Cone Penetration Test Data. Geotech Geol Eng (2024). https://doi.org/10.1007/s10706-023-02737-6

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