Prediction of load bearing capacity of piles from Statnamic pile testing data using neural networks

Akbar Javadi; Michael Brown; Adrian F. L. Hyde

Prediction of load bearing capacity of piles from Statnamic pile testing data using neural networks

Akbar Javadi
, Michael Brown
, Adrian F. L. Hyde

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper explores the capabilities of neural networks to predict the static load bearing capacity of piles from rapid load pile testing data. A rapid load testing technique with the proprietary name of Statnamic testing is increasingly being used as an alternative to dynamic testing. It is less likely to cause damage to a pile than conventional dynamic methods, and removes the need for stress wave analysis and signal matching. Further-more, it is much faster and generally more economic than static tests. The main disad-vantages of rapid load tests are the problems associated with interpreting the test results in clay soils due to non-linear damping effects caused by variations in shear resistance with rate of shearing. A neural network is an information processing system, the archi-tecture of which essentially mimics the biological system of the brain. Artificial neural networks are ideally suited to assist engineers to interpret information from in-situ or laboratory measurements in complex problems. A back-propagation neural network has been trained using static and Statnamic load pile testing data. The trained network was then used to predict the static load-settlement behaviour from the rapid load data for other piles the data for which was not used in the training process.
The results of the prediction of the static load bearing capacity of piles using the neu-ral network have been compared with the field measurements. Comparison of the results show that artificial neural network can be used as an efficient engineering tool in predic-tion load bearing capacity of piles from rapid load testing data. These findings could en-able engineers to reduce the number of static load tests, thus saving time and cost.

Original language	English
Title of host publication	5th International Conference on Deep Foundation Practice
Place of Publication	Singapore
Publisher	CI-Premier Pte Ltd
Pages	235-242
Number of pages	8
ISBN (Print)	9789810425120
Publication status	Published - Apr 2001
Event	5th International Conference on Deep Foundation Practice incorporating Piletalk International 2001 - Singapore, Singapore Duration: 4 Apr 2001 → 6 Apr 2001

Conference

Conference	5th International Conference on Deep Foundation Practice incorporating Piletalk International 2001
Country/Territory	Singapore
City	Singapore
Period	4/04/01 → 6/04/01

Keywords

Statnamic load testing

6 Conference contribution
2 Article
1 Chapter (peer-reviewed)

Pile capacity testing
Brown, M., Ball, J., Burland, J., Chapman, T., Higgins, K., Skinner, H. & Toll, D., 10 Nov 2023, ICE Manual of Geotechnical Engineering, Second edition, Volume II: Geotechnical design, construction and verification. Brown, M., Burland, J., Chapman, T., Higgins, K., Skinner, H. & Toll, D. (eds.). 2nd ed. UK: Emerald Group Publishing Limited, Vol. 2. p. 1599-1620 22 p.
Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

Open Access
File
5 Link opens in a new tab Citations (Scopus)

709 Downloads (Pure)
Analysis of a rapid load test on an instrumented bored pile in clay
Brown, M. J., Hyde, A. F. L. & Anderson, W. F., 2006, In: Géotechnique. 56, 9, p. 627-638 12 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
45 Link opens in a new tab Citations (Scopus)

2219 Downloads (Pure)
Statnamic pile testing case studies
Brown, M., Jun 2006, Proceedings-DFI/EFFC of the 10th International Conference on Piling and Deep Foundations. United States: Deep Foundations Institute, p. 627-634 8 p. 1407
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cite this

@inproceedings{a1b2038353bc451eb041e7c0b3b8fdef,

title = "Prediction of load bearing capacity of piles from Statnamic pile testing data using neural networks",

abstract = "This paper explores the capabilities of neural networks to predict the static load bearing capacity of piles from rapid load pile testing data. A rapid load testing technique with the proprietary name of Statnamic testing is increasingly being used as an alternative to dynamic testing. It is less likely to cause damage to a pile than conventional dynamic methods, and removes the need for stress wave analysis and signal matching. Further-more, it is much faster and generally more economic than static tests. The main disad-vantages of rapid load tests are the problems associated with interpreting the test results in clay soils due to non-linear damping effects caused by variations in shear resistance with rate of shearing. A neural network is an information processing system, the archi-tecture of which essentially mimics the biological system of the brain. Artificial neural networks are ideally suited to assist engineers to interpret information from in-situ or laboratory measurements in complex problems. A back-propagation neural network has been trained using static and Statnamic load pile testing data. The trained network was then used to predict the static load-settlement behaviour from the rapid load data for other piles the data for which was not used in the training process. The results of the prediction of the static load bearing capacity of piles using the neu-ral network have been compared with the field measurements. Comparison of the results show that artificial neural network can be used as an efficient engineering tool in predic-tion load bearing capacity of piles from rapid load testing data. These findings could en-able engineers to reduce the number of static load tests, thus saving time and cost. ",

keywords = "Statnamic load testing",

author = "Akbar Javadi and Michael Brown and Hyde, \{Adrian F. L.\}",

year = "2001",

month = apr,

language = "English",

isbn = "9789810425120",

pages = "235--242",

booktitle = "5th International Conference on Deep Foundation Practice",

publisher = "CI-Premier Pte Ltd",

address = "Singapore",

note = "5th International Conference on Deep Foundation Practice incorporating Piletalk International 2001 ; Conference date: 04-04-2001 Through 06-04-2001",

}

Javadi, A, Brown, M & Hyde, AFL 2001, Prediction of load bearing capacity of piles from Statnamic pile testing data using neural networks. in 5th International Conference on Deep Foundation Practice. CI-Premier Pte Ltd, Singapore, pp. 235-242, 5th International Conference on Deep Foundation Practice incorporating Piletalk International 2001, Singapore, Singapore, 4/04/01.

TY - GEN

T1 - Prediction of load bearing capacity of piles from Statnamic pile testing data using neural networks

AU - Javadi, Akbar

AU - Brown, Michael

AU - Hyde, Adrian F. L.

PY - 2001/4

Y1 - 2001/4

N2 - This paper explores the capabilities of neural networks to predict the static load bearing capacity of piles from rapid load pile testing data. A rapid load testing technique with the proprietary name of Statnamic testing is increasingly being used as an alternative to dynamic testing. It is less likely to cause damage to a pile than conventional dynamic methods, and removes the need for stress wave analysis and signal matching. Further-more, it is much faster and generally more economic than static tests. The main disad-vantages of rapid load tests are the problems associated with interpreting the test results in clay soils due to non-linear damping effects caused by variations in shear resistance with rate of shearing. A neural network is an information processing system, the archi-tecture of which essentially mimics the biological system of the brain. Artificial neural networks are ideally suited to assist engineers to interpret information from in-situ or laboratory measurements in complex problems. A back-propagation neural network has been trained using static and Statnamic load pile testing data. The trained network was then used to predict the static load-settlement behaviour from the rapid load data for other piles the data for which was not used in the training process. The results of the prediction of the static load bearing capacity of piles using the neu-ral network have been compared with the field measurements. Comparison of the results show that artificial neural network can be used as an efficient engineering tool in predic-tion load bearing capacity of piles from rapid load testing data. These findings could en-able engineers to reduce the number of static load tests, thus saving time and cost.

AB - This paper explores the capabilities of neural networks to predict the static load bearing capacity of piles from rapid load pile testing data. A rapid load testing technique with the proprietary name of Statnamic testing is increasingly being used as an alternative to dynamic testing. It is less likely to cause damage to a pile than conventional dynamic methods, and removes the need for stress wave analysis and signal matching. Further-more, it is much faster and generally more economic than static tests. The main disad-vantages of rapid load tests are the problems associated with interpreting the test results in clay soils due to non-linear damping effects caused by variations in shear resistance with rate of shearing. A neural network is an information processing system, the archi-tecture of which essentially mimics the biological system of the brain. Artificial neural networks are ideally suited to assist engineers to interpret information from in-situ or laboratory measurements in complex problems. A back-propagation neural network has been trained using static and Statnamic load pile testing data. The trained network was then used to predict the static load-settlement behaviour from the rapid load data for other piles the data for which was not used in the training process. The results of the prediction of the static load bearing capacity of piles using the neu-ral network have been compared with the field measurements. Comparison of the results show that artificial neural network can be used as an efficient engineering tool in predic-tion load bearing capacity of piles from rapid load testing data. These findings could en-able engineers to reduce the number of static load tests, thus saving time and cost.

KW - Statnamic load testing

M3 - Conference contribution

SN - 9789810425120

SP - 235

EP - 242

BT - 5th International Conference on Deep Foundation Practice

PB - CI-Premier Pte Ltd

CY - Singapore

T2 - 5th International Conference on Deep Foundation Practice incorporating Piletalk International 2001

Y2 - 4 April 2001 through 6 April 2001

ER -

Prediction of load bearing capacity of piles from Statnamic pile testing data using neural networks

Abstract

Conference

Keywords

Fingerprint

Research output

Pile capacity testing

Analysis of a rapid load test on an instrumented bored pile in clay

Statnamic pile testing case studies

Cite this