Assessment of Risk Potential due to Underground Box Structure Installation Employing ANN Model and Field Experimental Approaches
Publication: Journal of Performance of Constructed Facilities
Volume 34, Issue 4
Abstract
Construction of underground box structures that underpass existing infrastructures or facilities at shallow depths is being widely conducted in urban areas in order to avoid the interference of existing structures without the rerouting of traffic. Consequently, it is crucial to ensure safety during and after construction by monitoring the ground settlement induced by underground box installations that can be influential for the existing various structures. This paper provides a method to assess the risk potential around underground box structures with artificial neural networks (ANN), taking into account input variables that can be monitored in the field. By introducing the numerical methods and ANN, the probability of failures considering the variability of design parameters such as ground conditions, structure sizes and shapes, traffic loads, and presence of existing structures could be assessed and utilized for the safe construction of underground box structures at shallow depths. Experimental programs were also performed to investigate the effect of the umbrella method, which contributes to the decrease of risk potential in a practice. A limited field test evaluation using ground-penetrating radar (GPR) along with a pneumatic dynamic cone penetrometer (PDCP) was found to be promising in the assessment of risk potential at shallow depths.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work is part of a research project financially sponsored by the Korea Agency for Infrastructure Technology Advancement (Grant No. 18SCIP-B108153-04).
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Published In
Journal of Performance of Constructed Facilities
Volume 34 • Issue 4 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Jul 9, 2019
Accepted: Jan 27, 2020
Published online: Apr 30, 2020
Published in print: Aug 1, 2020
Discussion open until: Sep 30, 2020
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