A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Modelling of Pile Drivability Using Soft Computing Methods
Abstract Driven piles are commonly used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical theory or understanding of the phenomena involved. In situations where measured or numerical data are available, various soft computing methods have shown to offer great promise for mapping the nonlinear interactions between the system’s inputs and outputs. In this study, two soft computing methods, the Back propagation neural network (BPNN) and Multivariate adaptive regression splines (MARS) algorithms were used to assess pile drivability in terms of the Maximum compressive stresses, Maximum tensile stresses, and Blow per foot. A database of more than four thousand piles is utilized for model development and comparative performance of the predictions between BPNN and MARS.
Modelling of Pile Drivability Using Soft Computing Methods
Abstract Driven piles are commonly used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical theory or understanding of the phenomena involved. In situations where measured or numerical data are available, various soft computing methods have shown to offer great promise for mapping the nonlinear interactions between the system’s inputs and outputs. In this study, two soft computing methods, the Back propagation neural network (BPNN) and Multivariate adaptive regression splines (MARS) algorithms were used to assess pile drivability in terms of the Maximum compressive stresses, Maximum tensile stresses, and Blow per foot. A database of more than four thousand piles is utilized for model development and comparative performance of the predictions between BPNN and MARS.
Modelling of Pile Drivability Using Soft Computing Methods
Zhang, Wengang (author) / Goh, Anthony T. C. (author)
2018-01-01
23 pages
Article/Chapter (Book)
Electronic Resource
English
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