Texas cone penetrometer foundation design method: Qualitative and quantitative assessment

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This paper presents a qualitative and quantitative evaluation of the predictive validity of the Texas Cone Penetration (TCP) foundation design method. Allowable loads were determined using both strength-based and serviceability-based models and were further compared to predicted allowable loads using the TCP foundation design charts. The predictive validity of the TCP method was evaluated using a final dataset consisting of 60 full-scale load tests comprising 33 driven piles and 27 drilled shafts, all founded in soil materials. The qualitative evaluation consisted of a visual assessment of the scatterplot compared to the equal prediction line. In the case of the quantitative assessment, regression models were fitted to the dataset, and the accuracy and precision of the models were evaluated based on statistical analyses. Results show that the predictive validity of the TCP-based foundation design method is accurate with low precision. The qualitative evaluation of the strength-based data showed slight data scatter around the equal prediction line. In the case of the serviceability-based model, data points indicated the same slight scatter with major concentration above the equal prediction line in the conservative prediction region. With a p-value <.05, results from the quantitative analyses showed a statistically significant relationship between the proposed models and the allowable loads predicted using the TCP. The R-square value for the models was between 0.776 and 0.814.

Original languageEnglish
JournalDFI Journal
StateAccepted/In press - 2018


  • Allowable stress design
  • Deep foundations
  • Full-scale load test
  • Predictive validity
  • Serviceability
  • Texas cone penetration test

Fingerprint Dive into the research topics of 'Texas cone penetrometer foundation design method: Qualitative and quantitative assessment'. Together they form a unique fingerprint.

Cite this