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Design parameters of sands from pressuremeter data : = Interpretation and application.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Design parameters of sands from pressuremeter data :/
Reminder of title:	Interpretation and application.
Author:	Chang, Nianchiu.
Description:	1 online resource (217 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 56-02, Section: B.
Contained By:	Dissertations Abstracts International56-02B.
Subject:	Civil engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798208204504

Design parameters of sands from pressuremeter data : = Interpretation and application.
Chang, Nianchiu.

Design parameters of sands from pressuremeter data :Interpretation and application. - 1 online resource (217 pages)

Source: Dissertations Abstracts International, Volume: 56-02, Section: B.

Thesis (Ph.D.)--Clemson University, 1993.

Includes bibliographical references

Existing methods for interpreting design parameters from pressuremeter data are examined in this research. Several procedures based on confidence interval analysis, statistical inference analysis, and fuzzy inference analysis are established. They are used to reduce model uncertainties associated with existing interpretation methods. Each procedure is then examined for its reliability in predicting design parameters through a series of sensitivity analyses and design verification. A total of thirty-one sets of pressuremeter data, collected from three well-documented sites, is used in this research. The design parameters derived from each pressuremeter test data based on different procedures are used in the design verification. In other words, the results of foundation design using the derived parameters are compared with the results of full scale load tests. From this comparison, the most reliable procedure is determined. The results of this study indicate that model uncertainty in the interpretation of design parameters can be reduced using the developed procedures. Among these procedures, the one based on the statistical inference analysis is shown to be the most reliable.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798208204504Subjects--Topical Terms:

561339
Civil engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Design parameters of sands from pressuremeter data : = Interpretation and application.
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100 1 $a Chang, Nianchiu. $3 1469354
245 1 0 $a Design parameters of sands from pressuremeter data : $b Interpretation and application.
264 0 $c 1993
300 $a 1 online resource (217 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Dissertations Abstracts International, Volume: 56-02, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Juang, C. Hsein.
502 $a Thesis (Ph.D.)--Clemson University, 1993.
504 $a Includes bibliographical references
520 $a Existing methods for interpreting design parameters from pressuremeter data are examined in this research. Several procedures based on confidence interval analysis, statistical inference analysis, and fuzzy inference analysis are established. They are used to reduce model uncertainties associated with existing interpretation methods. Each procedure is then examined for its reliability in predicting design parameters through a series of sensitivity analyses and design verification. A total of thirty-one sets of pressuremeter data, collected from three well-documented sites, is used in this research. The design parameters derived from each pressuremeter test data based on different procedures are used in the design verification. In other words, the results of foundation design using the derived parameters are compared with the results of full scale load tests. From this comparison, the most reliable procedure is determined. The results of this study indicate that model uncertainty in the interpretation of design parameters can be reduced using the developed procedures. Among these procedures, the one based on the statistical inference analysis is shown to be the most reliable.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Civil engineering. $3 561339
650 4 $a Geotechnology. $3 1179676
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0543
690 $a 0428
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Clemson University. $3 657972
773 0 $t Dissertations Abstracts International $g 56-02B.
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