Publication informationAutomation in Construction, Publisher Elsevier Item record/more information http://hdl.handle.net/10197/4854
Publisher's statementThis is the author's version of a work that was accepted for publication in Automation in Construction. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Automation in Construction (30, , (2013)
ABSTRACT:More than one-fifth of the cost of major transport infrastructure projects relates to construction related damage prediction and mitigation. In tunnelling, recent attempts at using remote sensing as a less expensive alternative to traditional surveying for creating computational models of masonry buildings for better damage prediction raises fundamental questions as to the necessary quality of the input data, as there is a direct relationship between data quality and acquisition costs. Given the large quantity of potentially vulnerable buildings along a tunnel's route, features such as windows are typically considered only generally. To understand the implications of such choices and to better explore the viability of using remote sensing as input for computational models, 16 finite element models were devised to investigate the impact of window shape, brick orientation, window size, and the presence of lintels. Response was considered with respect to gravity loads and excavation-induced subsidence. Permutations of three common window shapes were modelled as representative of Georgian brick structures. The base model was benchmarked against large-scale Page 2 experimental work using a non-linear analysis. This study proves that a few simple assumptions can be used in reducing complexity of the building façades during reconstructing 3D building models for computation without causing major errors in structural response based on these models.