<p>Underground utilities must comply
with the requirements stipulated in utility regulations to ensure their
structural integrity and avoid interferences and disruptions of utility
services. Noncompliance with the regulations could cause disastrous consequences
such as pipeline explosion and pipeline contamination that can lead to hundreds
of deaths and huge financial loss. However, the current practice of utility compliance
checking relies on manual efforts to examine lengthy textual regulations,
interpret them subjectively, and check against massive and heterogeneous
utility data. It is time-consuming, costly, and error prone. There remains a
critical need for an effective mechanism to help identify the regulatory
non-compliances in new utility designs or existing pipelines to limit possible
negative impacts. Motivated by this critical need, this research aims to create
an intelligent, knowledge-based method to automate the compliance checking for
underground utilities. </p>
<p>The overarching goal is to build
semantic intelligence to enable knowledge-based, automated compliance checking
of underground utilities by integrating semantic web technologies, natural
language processing (NLP), and domain ontologies. Three specific objectives
are: (1) designing an ontology-based framework for integrating massive and heterogeneous
utility data for automated compliance checking, (2) creating a semi-automated
method for utility ontology development, and (3) devising a semantic NLP approach
for interpreting textual utility regulations. Objective 1 establishes the
knowledge-based skeleton for utility compliance checking. Objectives 2 and 3 build
semantic intelligence into the framework resulted from Objective 1 for improved
performance in utility compliance checking. </p>
<p>Utility compliance checking is
the action that examines geospatial data of utilities and their surroundings
against textual utility regulations. The integration of heterogeneous
geospatial data of utilities as well as textual data remains a big challenge. Objective
1 is dedicated to addressing this challenge. An ontology-based framework has
been designed to integrate heterogeneous data and automate compliance checking through
semantic, logic, and spatial reasoning. The framework consists of three key
components: (1) four interlinked ontologies that provide the semantic schema to
represent heterogeneous data, (2) two data convertors to transform data from
proprietary formats into a common and interoperable format, and (3) a reasoning
mechanism with spatial extensions for detecting non-compliances. The
ontology-based framework was tested on a sample utility database, and the
results proved its effectiveness.</p>
<p>Two supplementary methods were
devised to build the semantic intelligence in the ontology-based framework. The
first one is a novel method that integrates the top-down strategy and NLP to
address two semantic limitations in existing ontologies for utilities: lack of
compatibility with existing utility modeling initiatives and relatively small
vocabulary sizes. Specifically, a base ontology is first developed by
abstracting the modeling information in CityGML Utility Network ADE through a
series of semantic mappings. Then, a novel integrated NLP approach is devised
to automatically learn the semantics from domain glossaries. Finally, the
semantics learned from the glossaries are incorporated into the base ontology
to result in a domain ontology for utility infrastructure. For case
demonstration, a glossary of water terms was learned to enrich the base
ontology (formalized from the ADE) and the resulting ontology was evaluated to
be an accurate, sufficient, and shared conceptualization of the domain. </p>
<p>The second one is an ontology-
and rule-based NLP approach for automated interpretation of textual regulations
on utilities. The approach integrates ontologies to capture both domain and
spatial semantics from utility regulations that contain a variety of technical
jargons/terms and spatial constraints regarding the location and clearance of
utility infrastructure. The semantics are then encoded into pattern-matching
rules for extracting the requirements from the regulations. An ontology- and
deontic logic-based mechanism have also been integrated to facilitate the
semantic and logic-based formalization of utility-specific regulatory
knowledge. The proposed approach was tested in interpreting the spatial
configuration-related requirements in utility accommodation policies, and
results proved it to be an effective means for interpreting utility regulations
to ensure the compliance of underground utilities. </p>
<p>The main outcome of this research
is a novel knowledge-based computational platform with semantic intelligence
for regulatory compliance checking of underground utilities, which is also the
primary contribution of this research. The knowledge-based computational
platform provides a declarative way rather than the otherwise
procedural/hard-coding implementation approach to automate the overall process
of utility compliance checking, which is expected to replace the conventional
costly and time-consuming skill-based practice. Utilizing this computational
platform for utility compliance checking will help eliminate non-compliant
utility designs at the very early stage and identify non-compliances in
existing utility records for timely correction, thus leading to enhanced safety
and sustainability of the massive utility infrastructure in the U.S.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12736244 |
| Date | 30 July 2020 |
| Creators | Xin Xu (9183590) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/SEMANTIC_INTELLIGENCE_FOR_KNOWLEDGE-BASED_COMPLIANCE_CHECKING_OF_UNDERGROUND_UTILITIES/12736244 |
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