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The Development of Soil Compressibility Prediction Models and Application to Site Settlement

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Date Issued:
2018
Abstract/Description:
The magnitude of the overall settlement depends on several variables such as the Compression Index, Cc, and Recompression Index, Cr, which are determined by a consolidation test; however, the test is time consuming and labor intensive. Correlations have been developed to approximate these compressibility indexes. In this study, a data driven approach has been employed in order to estimate Cc and Cr. Support Vector Machines classification is used to determine the number of distinct models to be developed. The statistical models are built through a forward selection stepwise regression procedure. Ten variables were used, including the moisture content (w), initial void ratio (eo), dry unit weight (?dry), wet unit weight (?wet), automatic hammer SPT blow count (N), overburden stress (?), fines content (-200), liquid limit (LL), plasticity index (PI), and specific gravity (Gs). The results confirm the need for separate models for three out of four soil types, these being Coarse Grained, Fine Grained, and Organic Peat. The models for each classification have varying degrees of accuracy. The correlations were tested through a series of field tests, settlement analysis, and comparison to known site settlement. The first analysis incorporates developed correlations for Cr, and the second utilizes measured Cc and Cr for each soil layer. The predicted settlements from these two analyses were compared to the measured settlement taken in close proximity. Upon conclusion of the analyses, the results indicate that settlement predictions applying a rule of thumb equating Cc to Cr, accounting for elastic settlement, and using a conventional influence zone of settlement, compares more favorably to measured settlement than that of predictions using measured compressibility index(s). Accuracy of settlement predictions is contingent on a thorough field investigation.
Title: The Development of Soil Compressibility Prediction Models and Application to Site Settlement.
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Name(s): Kirts, Scott, Author
Nam, Boo Hyun, Committee Chair
Chopra, Manoj, Committee Member
Sallam, Amr, Committee Member
Xanthopoulos, Petros, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2018
Publisher: University of Central Florida
Language(s): English
Abstract/Description: The magnitude of the overall settlement depends on several variables such as the Compression Index, Cc, and Recompression Index, Cr, which are determined by a consolidation test; however, the test is time consuming and labor intensive. Correlations have been developed to approximate these compressibility indexes. In this study, a data driven approach has been employed in order to estimate Cc and Cr. Support Vector Machines classification is used to determine the number of distinct models to be developed. The statistical models are built through a forward selection stepwise regression procedure. Ten variables were used, including the moisture content (w), initial void ratio (eo), dry unit weight (?dry), wet unit weight (?wet), automatic hammer SPT blow count (N), overburden stress (?), fines content (-200), liquid limit (LL), plasticity index (PI), and specific gravity (Gs). The results confirm the need for separate models for three out of four soil types, these being Coarse Grained, Fine Grained, and Organic Peat. The models for each classification have varying degrees of accuracy. The correlations were tested through a series of field tests, settlement analysis, and comparison to known site settlement. The first analysis incorporates developed correlations for Cr, and the second utilizes measured Cc and Cr for each soil layer. The predicted settlements from these two analyses were compared to the measured settlement taken in close proximity. Upon conclusion of the analyses, the results indicate that settlement predictions applying a rule of thumb equating Cc to Cr, accounting for elastic settlement, and using a conventional influence zone of settlement, compares more favorably to measured settlement than that of predictions using measured compressibility index(s). Accuracy of settlement predictions is contingent on a thorough field investigation.
Identifier: CFE0007208 (IID), ucf:52284 (fedora)
Note(s): 2018-08-01
Ph.D.
Engineering and Computer Science, Civil, Environmental and Construction Engineering
Doctoral
This record was generated from author submitted information.
Subject(s): Consolidation -- soil compressibility -- support vector machine -- field verification -- settlement
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007208
Restrictions on Access: public 2018-08-15
Host Institution: UCF

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