Feasibility of indirect measurement of bearing capacity of driven piles based on a computational intelligence technique. (May 2020)

• Record Type: Journal Article
• Authors: Sun, Guoquan; Hasanipanah, Mahdi; Bakhshandeh Amnieh, Hassan; Foong, Loke Kok
• Is Part Of: Measurement. Volume 156(2020)
• Publication Date: 2020-05
• Subjects: Driven piles -- ANFIS -- Firefly algorithm -- Hybrid prediction model; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107577
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Indirect measurement of driven piles capacity is presented in this study. Developing an FA-ANFIS model for prediction of driven piles capacity. Results signify effectiveness of FA-ANFIS to predict driven piles capacity. Abstract: Parametric studies of driven piles response during dynamic and static load experiments, especially in the field, are generally too costly to measure. This study constructs and verifies a computational intelligence technique for predicting the bearing capacity of driven piles. The proposed technique is a hybridization of adaptive neuro-fuzzy inference system (ANFIS) and firefly algorithm (FA). It is worth mentioning that this is the first work that explores the ability of FA-ANFIS in the field of piles capacity prediction. In modeling, different FA-ANFIS and ANFIS models were constructed based on different combination of input parameters, and finally, the performances of all models were compared using statistical functions. The results demonstrated that the proposed FA-ANFIS model with coefficient of determination (R 2 ) of 0.997 was more effective than the ANFIS in predicting the pile capacity, and thereby the effectiveness of FA to optimize ANFIS was proved.