Decision Support for Test Trench Location Selection with 3D Semantic Subsurface Utility Models

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Subsurface utility construction work often involves repositioning of, and working between, existing buried networks. While the amount of utilities in modern cities grows, excavation work becomes more prone to incidents. To prevent such incidents, excavation workers request existing 2D utility maps, use detection equipment and dig test trenches to validate their accuracy and completeness. Although test trenches are of significant importance to reveal information about subsurface conditions, the process of determining their location, number and size is not explicated by experts to date. This study therefore aimed to explicate the reasoning and logic behind the selection of utility test trenches, and to formalize this in a semantically-rich utility model. To this end, we conducted interviews with experienced excavator operators. We then derived heuristics and rules that the experts used to determine trench locations. Such rules related to, for example, the layout of the excavation site, and the type of utilities, and accuracy of available data. Based on these rules, we integrated various incomplete sources of data, and generated a 3D utility model that could generate several alternative construction situations. We used queries to identify the most suitable location for a test trench. The resulting answers to queries helped optimize the test trench selection process. Our prototype demonstrates that the identified rules (1) facilitate the generation of semantically rich 3D utility models, and (2) support test trench decision making.
Original languageEnglish
Title of host publicationComputing in Civil Engineering 2017: Smart Safety, Sustainability, and Resilience
EditorsKen-Yu Lin, Nora El-Gohary, Pingbo Tang
Place of PublicationSeattle, Washington
Pages68-75
Number of pages8
Volume2017
ISBN (Electronic)9780784480847
DOIs
Publication statusPublished - Jun 2017

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Excavation
Semantics
Excavators
Decision making

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Racz, P., Syfuss, L., Schultz, C., van Buiten, M., olde Scholtenhuis, L. L., Vahdatikhaki, F., & Doree, A. G. (2017). Decision Support for Test Trench Location Selection with 3D Semantic Subsurface Utility Models. In K-Y. Lin, N. El-Gohary, & P. Tang (Eds.), Computing in Civil Engineering 2017: Smart Safety, Sustainability, and Resilience (Vol. 2017, pp. 68-75). Seattle, Washington. https://doi.org/10.1061/9780784480847
Racz, Paulina ; Syfuss, Lars ; Schultz, Carl ; van Buiten, Marinus ; olde Scholtenhuis, Léon Luc ; Vahdatikhaki, Faridaddin ; Doree, Andries G. / Decision Support for Test Trench Location Selection with 3D Semantic Subsurface Utility Models. Computing in Civil Engineering 2017: Smart Safety, Sustainability, and Resilience. editor / Ken-Yu Lin ; Nora El-Gohary ; Pingbo Tang. Vol. 2017 Seattle, Washington, 2017. pp. 68-75
@inproceedings{419473b07c70456eb7cc0fcb752ec4bb,
title = "Decision Support for Test Trench Location Selection with 3D Semantic Subsurface Utility Models",
abstract = "Subsurface utility construction work often involves repositioning of, and working between, existing buried networks. While the amount of utilities in modern cities grows, excavation work becomes more prone to incidents. To prevent such incidents, excavation workers request existing 2D utility maps, use detection equipment and dig test trenches to validate their accuracy and completeness. Although test trenches are of significant importance to reveal information about subsurface conditions, the process of determining their location, number and size is not explicated by experts to date. This study therefore aimed to explicate the reasoning and logic behind the selection of utility test trenches, and to formalize this in a semantically-rich utility model. To this end, we conducted interviews with experienced excavator operators. We then derived heuristics and rules that the experts used to determine trench locations. Such rules related to, for example, the layout of the excavation site, and the type of utilities, and accuracy of available data. Based on these rules, we integrated various incomplete sources of data, and generated a 3D utility model that could generate several alternative construction situations. We used queries to identify the most suitable location for a test trench. The resulting answers to queries helped optimize the test trench selection process. Our prototype demonstrates that the identified rules (1) facilitate the generation of semantically rich 3D utility models, and (2) support test trench decision making.",
author = "Paulina Racz and Lars Syfuss and Carl Schultz and {van Buiten}, Marinus and {olde Scholtenhuis}, {L{\'e}on Luc} and Faridaddin Vahdatikhaki and Doree, {Andries G.}",
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Racz, P, Syfuss, L, Schultz, C, van Buiten, M, olde Scholtenhuis, LL, Vahdatikhaki, F & Doree, AG 2017, Decision Support for Test Trench Location Selection with 3D Semantic Subsurface Utility Models. in K-Y Lin, N El-Gohary & P Tang (eds), Computing in Civil Engineering 2017: Smart Safety, Sustainability, and Resilience. vol. 2017, Seattle, Washington, pp. 68-75. https://doi.org/10.1061/9780784480847

Decision Support for Test Trench Location Selection with 3D Semantic Subsurface Utility Models. / Racz, Paulina ; Syfuss, Lars; Schultz, Carl; van Buiten, Marinus ; olde Scholtenhuis, Léon Luc; Vahdatikhaki, Faridaddin ; Doree, Andries G.

Computing in Civil Engineering 2017: Smart Safety, Sustainability, and Resilience. ed. / Ken-Yu Lin; Nora El-Gohary; Pingbo Tang. Vol. 2017 Seattle, Washington, 2017. p. 68-75.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - olde Scholtenhuis, Léon Luc

AU - Vahdatikhaki, Faridaddin

AU - Doree, Andries G.

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N2 - Subsurface utility construction work often involves repositioning of, and working between, existing buried networks. While the amount of utilities in modern cities grows, excavation work becomes more prone to incidents. To prevent such incidents, excavation workers request existing 2D utility maps, use detection equipment and dig test trenches to validate their accuracy and completeness. Although test trenches are of significant importance to reveal information about subsurface conditions, the process of determining their location, number and size is not explicated by experts to date. This study therefore aimed to explicate the reasoning and logic behind the selection of utility test trenches, and to formalize this in a semantically-rich utility model. To this end, we conducted interviews with experienced excavator operators. We then derived heuristics and rules that the experts used to determine trench locations. Such rules related to, for example, the layout of the excavation site, and the type of utilities, and accuracy of available data. Based on these rules, we integrated various incomplete sources of data, and generated a 3D utility model that could generate several alternative construction situations. We used queries to identify the most suitable location for a test trench. The resulting answers to queries helped optimize the test trench selection process. Our prototype demonstrates that the identified rules (1) facilitate the generation of semantically rich 3D utility models, and (2) support test trench decision making.

AB - Subsurface utility construction work often involves repositioning of, and working between, existing buried networks. While the amount of utilities in modern cities grows, excavation work becomes more prone to incidents. To prevent such incidents, excavation workers request existing 2D utility maps, use detection equipment and dig test trenches to validate their accuracy and completeness. Although test trenches are of significant importance to reveal information about subsurface conditions, the process of determining their location, number and size is not explicated by experts to date. This study therefore aimed to explicate the reasoning and logic behind the selection of utility test trenches, and to formalize this in a semantically-rich utility model. To this end, we conducted interviews with experienced excavator operators. We then derived heuristics and rules that the experts used to determine trench locations. Such rules related to, for example, the layout of the excavation site, and the type of utilities, and accuracy of available data. Based on these rules, we integrated various incomplete sources of data, and generated a 3D utility model that could generate several alternative construction situations. We used queries to identify the most suitable location for a test trench. The resulting answers to queries helped optimize the test trench selection process. Our prototype demonstrates that the identified rules (1) facilitate the generation of semantically rich 3D utility models, and (2) support test trench decision making.

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M3 - Conference contribution

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BT - Computing in Civil Engineering 2017: Smart Safety, Sustainability, and Resilience

A2 - Lin, Ken-Yu

A2 - El-Gohary, Nora

A2 - Tang, Pingbo

CY - Seattle, Washington

ER -

Racz P, Syfuss L, Schultz C, van Buiten M, olde Scholtenhuis LL, Vahdatikhaki F et al. Decision Support for Test Trench Location Selection with 3D Semantic Subsurface Utility Models. In Lin K-Y, El-Gohary N, Tang P, editors, Computing in Civil Engineering 2017: Smart Safety, Sustainability, and Resilience. Vol. 2017. Seattle, Washington. 2017. p. 68-75 https://doi.org/10.1061/9780784480847