Integrating Natural Language Processing and Spatial Reasoning for Utility Compliance Checking
Publication: Journal of Construction Engineering and Management
Volume 142, Issue 12
Abstract
Underground utility incidents, such as utility conflicts and utility strikes, result in time and cost overruns in construction projects, property damages, environmental pollution, personnel injuries, and fatalities. A main cause of recurrent utility incidents is the noncompliance with the spatial configurations between utilities and their surroundings. Utility specifications usually contain textual descriptions of the spatial configurations. However, detection of spatial defects, according to the textual descriptions, is difficult and time consuming. This deficiency is because of the lack of spatial cognition in many rule-checking systems to process massive amounts of data. This study aims to automate utility compliance checking by integrating natural language processing (NLP) and spatial reasoning. NLP algorithm translates the textual descriptions of spatial configurations into computer-processable spatial rules. Spatial reasoning executes the extracted spatial rules following a logical order in a geographical information system (GIS) to identify noncompliance. The intellectual contribution of this study is twofold. First, complex spatial rules are retrieved automatically from textual data with their hierarchies classified, which provides the inputs and indicates the sequence of rule execution in spatial reasoning. Second, semantic spatial relations are modeled on the basis of their metric and topological implications, enabling the automatic execution of multiple spatial rules. Experiments were conducted to test this framework. The average precision, recall, and combination of the two (F-measure) achieved by the NLP algorithm for extracting spatial rules are 87.88%, 79.09%, and 83.25%, respectively. In addition, the spatial reasoning mechanism also was found to be a powerful tool for compliance checking under various scenarios.
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Acknowledgments
This research was funded by the National Science Foundation (NSF) via Grant CMMI-1265895 and Grant CMMI-1462638. The authors gratefully acknowledge NSF’s support. Any opinions, findings, conclusions, and recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF, Purdue University, or University of Michigan.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 142 • Issue 12 • December 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: May 29, 2015
Accepted: May 11, 2016
Published online: Jul 12, 2016
Published in print: Dec 1, 2016
Discussion open until: Dec 12, 2016
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