Asee peer logo

Developing a Statistical Model for Building Settlement Prediction

Download Paper |

Conference

2014 ASEE Annual Conference & Exposition

Location

Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014

ISSN

2153-5965

Conference Session

Construction Materials and Technologies

Tagged Division

Construction

Page Count

13

Page Numbers

24.389.1 - 24.389.13

DOI

10.18260/1-2--20280

Permanent URL

https://peer.asee.org/20280

Download Count

211

Request a correction

Paper Authors

biography

Jing Du University of Texas at San Antonio

visit author page

Aug 2013 - Present
Assistant Professor/Department of Construction Science/UTSA

Jun 2011 - Aug 2013
Sr. Cost Analyst/Zachry Holdings, Inc.

Aug 2008 - May 2012
PhD in Construction Management/Michigan State University

visit author page

biography

Suat Gunhan University of Texas, San Antonio

visit author page

Dr. Gunhan received his PhD Degree in Civil Engineering from Illinois Institute of Technology. He is currently an Assistant Professor at the Department of Construction Science at the University of Texas at San Antonio.

visit author page

Download Paper |

Abstract

Developing A Statistical Model for Building Subsidence PredictionBuilding subsidence is the downward movement of the ground supporting the buildings. It isvery common in all types of construction especially high-rise buildings and once happened, isvery risky to the occupants. Most building codes have specific requirements on subsidencetolerance. As a result, construction managers, at the early phase of a project, should be able topredict the possible subsidence based on information available including design documents andsoil conditions, to calculate the risk. A variety of computational/empirical models can be appliedto predict building subsidence; however these models are either too complicated to non-engineering practitioners, or inaccurate in many cases.This paper aims to develop a statistical model to predict the subsidence of high-rise buildings. Inthe selection of a proper statistical method two criteria have been highlighted: (1) the methodshould be able to yield results that are accurate enough for practical applications; and (2) themethod should be simple enough for industry practitioners and construction students. This paperproposes the use of regression analysis, which is often utilized to explore the relationshipbetween a set of explanatory variables (X’s) and the observation, or response variable (Y). Inorder to build the model, a dataset was established which contains 33 actual high-rise buildingconstruction projects in China, built from year 2002 to 2008. The response variable (Y) is“Building final subsidence (cm)”. Thirteen explanatory variables (X’s) were initially selectedupon a survey to the project managers and relevant building codes including such as “Number oftower stories”, “ Height of tower”, and “ Type of ground soil” etc. The t-tests identified eight ofthem to be significant to the model and therefore were finally selected. The developed predictionmodel was tested against the empirical formulas and its better predictivity has been confirmedwith a R2 of 0.951.The modeling process has also been tailored into a lecture to teach construction students how toapply advanced statistical methods in construction management and engineering area. The stepscovered include preliminary analysis, model selection procedure, model transformation such asBox-Cox method and stepwise method, and model diagnosis and evaluation. Students were alsotaught how to use Excel to perform regression analysis for construction management andengineering problems, and how to interpret the results. It was found that the regression analysisis easy to understand and always has satisfactory performance in the construction managementand engineering area.

Du, J., & Gunhan, S. (2014, June), Developing a Statistical Model for Building Settlement Prediction Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20280

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2014 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015