Artificial Intelligence Applications for TunnelingSource: GEOSTRATA Magazine:;2022:;Volume ( 026 ):;issue: 001::page 40-47DOI: 10.1061/geosek.0000397Publisher: ASCE
Abstract: Geotechnical uncertainty is the governing source of risk on urban tunnel projects. A typical preconstruction geotechnical site investigation for a tunnel project involves vertical and/or inclined boreholes spaced at 50-m to 75-m intervals along alignments often extending for kilometers. This leads to a sampling rate of less than 0.1 percent of the ground. The result is a very high level of geotechnical and geological uncertainty, with reliance on desktop study and understanding of geological processes to help fill in very large information gaps.
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contributor author | Michael A. Mooney | |
contributor author | Hongjie Yu | |
date accessioned | 2022-12-27T20:45:33Z | |
date available | 2022-12-27T20:45:33Z | |
date issued | 2022/02/01 | |
identifier other | geosek.0000397.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4287939 | |
description abstract | Geotechnical uncertainty is the governing source of risk on urban tunnel projects. A typical preconstruction geotechnical site investigation for a tunnel project involves vertical and/or inclined boreholes spaced at 50-m to 75-m intervals along alignments often extending for kilometers. This leads to a sampling rate of less than 0.1 percent of the ground. The result is a very high level of geotechnical and geological uncertainty, with reliance on desktop study and understanding of geological processes to help fill in very large information gaps. | |
publisher | ASCE | |
title | Artificial Intelligence Applications for Tunneling | |
type | Journal Article | |
journal volume | 26 | |
journal issue | 1 | |
journal title | GEOSTRATA Magazine | |
identifier doi | 10.1061/geosek.0000397 | |
journal fristpage | 40-47 | |
journal lastpage | 40-47_1 | |
page | 1 | |
tree | GEOSTRATA Magazine:;2022:;Volume ( 026 ):;issue: 001 | |
contenttype | Fulltext |