Semester of Graduation

Fall 2018

Degree

Master of Civil Engineering (MCE)

Department

Department of Civil and Environmental Engineering

Document Type

Thesis

Abstract

In order to incorporate the influence of collected in-situ data, the spatial correlation between the data and the foundation needs to be explored. However, risk and uncertainty are the characteristics of the soil that cannot be eliminated. Statistical information of the soil property can be estimated from available field data obtained from testing at discrete locations across the site. In this research, several well-established spatial interpolation methods like ordinary kriging (OK), simple kriging (SK), inverse distance weight (IDW), spline, natural neighbor (NaN) and universal kriging (UK) were incorporated to evaluate the best method. Six CPT (Cone penetration test) (Tip Resistance data) cases (Case 1, 3, 4, 5, 6 and 9) and four soil boring (SU and SPT data) cases (Case 2, 7 ,8 and 10) were investigated in this research. According to the results, for Case 1, 2, 3, 4, 7, 9 and 10, if the first priority is given to bias factor followed by coefficient of variation (COV) and root mean square error (RMSE), the best three spatial interpolation techniques are IDW, OK and SK sequentially, based on their performance. For Case 5 (CPT data), the best three spatial interpolation techniques are OK, IDW and SK sequentially. For Case 6 (CPT data), the best three spatial interpolation techniques are SK, IDW and OK sequentially. For Case 8 (Soil Boring data), the best three spatial interpolation techniques are IDW, SK and OK sequentially. It can be concluded that the average COV of bias factor λ (for qc, SU and SPT data) for different spatial interpolation methods are less than the average measured COV of predicted average tip resistance and the measured COV of undrained shear strength and SPT (standard penetration test).

Date

10-16-2018

Committee Chair

Abu-Farsakh, Murad

DOI

10.31390/gradschool_theses.4808

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