Editorial Note – DFI Journal, Vol. 19, Issue 2 (2025)-Article

Article

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DFI Journal Volume 19, Issue 2 (2025) features five studies addressing advances in deep foundation design, field performance, and emerging technologies.<br /><br />Salunke, Shuman, and Khan assess shortcomings in the FHWA Effective Stress Beta Method for driven piles. Using field-test data, they find substantial differences between predicted and measured capacities. They develop neural-network models to predict the method’s β and Nt coefficients; these outperform traditional correlations and inform a revised design approach.<br /><br />Oweis and Gao report field monitoring of nine large-diameter drilled displacement piles in soft to medium clays. Their data document ground movement, stress, and pore-pressure changes. Existing analytical and numerical methods predict displacement reasonably well but are less accurate for stress and pore pressure.<br /><br />Zhou and coauthors study drilled displacement and driven piles in Georgia’s stiff Miocene Marl. They propose improved CPT-based correlations and refined load-transfer curves to address the tendency of conventional methods to underestimate resistance in this material.<br /><br />Blanchard and colleagues introduce a freely accessible database of load-transfer responses from 68 instrumented driven piles. The resource includes raw and residual-load-corrected curves, along with pile, soil, and test details, to support design, research, and model development.<br /><br />Finally, Huntoon and Frost present an analytical and reliability-based framework for estimating actuation forces and pullout capacity of root-inspired ground anchors. Their work addresses a key design gap in this emerging bio-inspired technology and supports its potential use in practical ground anchoring.

Keywords

deep foundation design

driven piles

drilled displacement piles

effective stress beta method

neural network models

pore pressure monitoring

CPT-based correlations

load-transfer database

ground anchors

reliability-based framework