Optimized selection of axial pile bearing capacity predictive methods based on multi-criteria decision-making (MCDM) models and database approach
Tóm tắt
As prevalent substructure systems in geotechnical engineering, piles are susceptible to underlying uncertainties and complex interactions to surrounding soils, associated with their geometry, pattern, and the project cost influences. The spatial variability of surrounding soils and diversity of knowledge in pile engineering highlight selecting an appropriate predictive method(s) for design. Various methods, including static analyses, in situ-based methods, static pile load testing (SPLT), dynamic methods, and numerical analyses, have been developed to predict the axial pile bearing capacity. The cone penetration test (CPT) is one of the in situ tests that provides continuous and reliable records with depth. Due to the similarities between CPT and pile, different CPT-based methods have been advanced. However, these methods result in a wide range of predictions. In this regard, several statistical, probabilistic, and reliability-based criteria are used to assess the accuracy, precision, and error embedded in each method. Therefore, multi-criteria decision-making (MCDM) models are introduced and implemented to distinguish superseding methods. Accordingly, a database of 60 driven piles with adjacent CPT records has been compiled. About twelve static analyses and in situ-based methods were implemented to predict the pile axial bearing capacity for the investigated database. Moreover, aggregative methods, such as Copeland or Borda count, are employed to evaluate and revisit these various decision-making models and designate more critical approaches for the current database. Regarding the collected database and among twelve considered methods, the Meyerhof (CPT-based) and the UniCone (CPTu-based) methods have outperformed the others considering the studied criteria and decision models.
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