Model Uncertainties in Foundation Design-Article

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Naresh C. Samtani reviews <em>Model Uncertainties in Foundation Design</em> by Chong Tang and Kok-Kwang Phoon, a book that compiles design models and measured-data databases for foundations and other geostructures. The reviewer emphasizes that readers should study the first three chapters before turning to specific foundation types, since they establish key concepts and terminology.

The book evaluates model uncertainty by comparing measured and calculated values, especially through the “model factor”—the ratio of measured to predicted values—and its statistical properties, including mean and coefficient of variation. It proposes a consistent system for classifying models by conservativeness and dispersion. The quality and breadth of its databases are identified as the book’s principal contribution, enabling systematic assessment of uncertainty across shallow foundations, piles, drilled shafts, spudcans, and other systems.

The review discusses the book’s focus on limit-state and LRFD design, including calibration of resistance factors for ultimate limit states (ULS) and serviceability limit states (SLS). It notes an important distinction: the book’s SLS calibration approach differs fundamentally from that adopted in the 2020, ninth edition of AASHTO LRFD. The book primarily references the 2017 eighth edition, so it does not reflect the later AASHTO updates for foundation movements and their structural consequences. Nevertheless, its databases and discussion of load–movement behavior can support future research and refinement of design guidance.

Samtani concludes that the book is an excellent resource for researchers, practitioners, and advanced students. Together with the ninth edition of AASHTO LRFD, it can improve understanding of uncertainty and performance-based foundation design.

Keywords

foundation design

model uncertainty

model factor

geotechnical engineering

LRFD design

limit-state design

resistance factors

serviceability limit state

ultimate limit state

AASHTO LRFD