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

• Published in: KSCE journal of civil engineering 2015, 19(1), , pp.98-106
• Main Authors: Baziar, Mohammad Hassan, Saeedi Azizkandi, Alireza, Kashkooli, Armin
• Format: Article
• Language: English
• Subjects: Analogies; Artificial neural networks; Civil Engineering; Cone penetration tests; Consistency; Engineering; Experimental methods; Field tests; Geotechnical Engineering; Geotechnical Engineering & Applied Earth Sciences; Industrial Pollution Prevention; Learning theory; Mathematical models; Network analysis; Neural networks; Parameters; Penetration; Pile settlement; Piles; Predictions; Samples; 토목공학
• ISSN: 1226-7988; 1976-3808
• DOI: 10.1007/s12205-012-0628-3

Several theoretical and experimental methods are available to estimate pile settlement. Due to difficulties for obtaining undisturbed samples, many of these methods have been focused on in-situ tests. The cone penetration test is one of the most effective in-situ tests because of its geometrical analogy with the piles as well as presenting continuous results along the depth. In this study, 1300 recorded settlement data from 101 pile loading tests with the CPT results were collected. Then Artificial Neural Network analyses (ANN) were conducted to obtain the best model for the prediction of pile settlement. The relative importance of input parameters has been evaluated using senility analysis. Accuracy predictions of the proposed model, along with other classic methods, were compared with the recorded values from the loading tests with the aid of different statistical parameters. This comparison indicated the superiority of the proposed model over previous methods. A parametric study has also been performed for the input parameters to study the consistency of the suggested model.