Spatial Data Fusion Model Design and Research for an Underground Pipeline in Urban Environment Scene Modeling

Abstract

In the rapid development of urban construction, underground pipelines play a crucial role. However, the current underground pipelines have poor association with relevant management departments, and there are deficiencies in data completeness, accuracy, and information content. Managing and sharing information resources is relatively difficult, transforming the constructed 3D underground pipeline geographic information systems into an ‘Information silo’. This results in redundant construction and resource wastage of underground utilities. The complex distribution characteristics of underground utilities make rapid batch modeling and post-model maintenance challenging. Therefore, researching a 3D spatial data fusion model for urban underground utilities becomes particularly important. Given the above problem, this paper proposes a spatial data fusion model for underground pipeline scene modeling. It elaborates on the geometric, semantic, and temporal characteristics of underground pipelines, encapsulating these features. With underground pipeline objects as the core and pipeline characteristics as the foundation, a spatial data fusion model integrating multiple characteristics of underground pipelines has been constructed. Through software development, the data model designed in this paper facilitates rapid construction of underground pipeline scenes. This further enhances the consistency and integrity of underground pipeline data, enabling shared resources and comprehensive supervision of facility operations on a daily basis.

1. Introduction

Underground pipeline networks, serving as the “supply stations” for urban energy consumption, play a vital role in optimizing urban planning, construction, and management. Not only do they meet residents’ living needs, but they also reduce resource consumption, mitigate environmental pollution, and enhance ecological benefits, thus promoting urban sustainability and fostering comprehensive urban development and social progress. However, due to the concealed nature of underground pipeline projects and the difficulty in directly visualizing their benefits, the management methods and levels of underground pipeline networks have lagged behind urban planning and construction management. There is a prevalent tendency to prioritize surface engineering over underground facilities and to emphasize the approval process while neglecting subsequent supervision [1]. Facing various challenges in underground pipeline construction, including the efficient integration and management of vast underground pipeline data in cities and the accurate classification of the geometric morphology, semantic information, and temporal information of pipeline networks, scholars have been continuously exploring more effective data models to meet the complex spatial data representation and processing requirements [2]. Whether it is hybrid data models [3], relational data models [4], or object-oriented data models [5], all aim to find a universally applicable data model. Scholars such as Zlatanova [6,7,8,9] have studied how to construct a three-dimensional model of an urban underground pipe network. Song Chunfeng and other scholars [10,11,12] realized the rapid modeling of a 3D fine model of an underground pipe network by designing different rule models.

The underground pipeline network forms a complex urban underground network space, characterized by intersecting lines and intricate connections. It includes various types and sizes of points and accessories, as well as pipelines composed of different textures and materials. From a geometric perspective, understanding the expression of underground pipeline construction and management in three-dimensional space is crucial. This not only involves accurately reflecting the physical world in a virtual environment but also relates to the effectiveness of underground facility maintenance and planning. By abstracting the underground pipeline into nodes and polylines and embedding them into a three-dimensional coordinate system, we can clearly demonstrate the precise positions of each facility and their spatial relationships. This abstraction covers spatial location information for points and ancillary facilities such as manholes and valve chambers, as well as their attribute information, providing a foundation for the management and operation of the underground pipeline network [13]. In the three-dimensional model, underground pipelines are no longer simple line segments, but complex structures composed of multiple polyline segments. These polyline segments are connected in a predetermined order according to certain rules, forming a complex network structure. In this network, nodes and ancillary objects connect various polyline segments, serving as the core elements of the overall pipeline network. Through this structural design, the 3D model of the underground pipeline network not only reflects the extension and distribution of the pipelines in space but also clearly demonstrates the topological relationships and dynamic features such as facility changes.

The temporal characteristics of underground pipelines refer to the changes that occur in the pipeline system over time during its usage and operation. These changes encompass various aspects such as aging, deterioration, maintenance, renewal, and expansion. In comparison to temporal characteristics, the changes in the geometric and semantic features are more independent. For instance, within different timeframes of usage, the semantic features of the network may remain unchanged, while its spatial positioning may have altered. Conversely, when semantic features undergo alteration, the spatial positioning may remain unchanged [14]. The holistic lifecycle management of underground pipelines primarily manifests the temporal characteristics and involves comprehensive management and planning of the pipeline system across its design, construction, operation, maintenance, renewal, and decommissioning stages. This is to ensure the safety and efficient operation of the pipeline system while maximizing its lifespan. Across various lifecycle stages, the managing entities, processes, data sources, types, and required information differ. With the passage of time, the temporal attributes exhibit more pronounced characteristic changes [15]. Therefore, when modeling underground pipelines, it is imperative to consider their temporal properties from multiple perspectives and construct datasets containing multi-temporal features. First, there is a dataset used in the planning and approval stage, which provides support for formulating planning requirements, evaluating site feasibility, and refining design proposals through comparisons and simulations with modern spatial network datasets. Next, there is a dataset applied in the stages of engineering construction and facility maintenance, typically obtained from pipeline surveys. This dataset includes static information about the pipeline network and its associated features. It plays a crucial role in providing important data support for pipeline construction, operation, and maintenance. It assists managers in designing construction plans, monitoring progress, and guiding network operations. Furthermore, there is a dataset that records the status and events of the underground pipeline network at different time points. Through analyzing historical data, it identifies performance degradation trends in certain regions or materials within the network, thereby prioritizing maintenance and updates in these areas or pipelines. Lastly, there is a dataset consisting of dynamic and changing data collected from the underground pipeline network system. These data change as inspections, maintenance, repair management, and risk assessments are conducted. The temporal attribute information of these monitoring data is crucial for the subsequent maintenance of the pipeline network model.

The semantic characteristics refer to the attributes of underground pipelines. These attributes include information such as pipeline material, diameter, depth of burial, purpose, departmental ownership, maintenance status, and pipeline connectivity. These semantic features can assist managers in better understanding the structure and functionality of pipelines, thereby enabling effective pipeline maintenance, planning, and decision-making. By analyzing the semantic characteristics of underground pipelines, comprehensive monitoring and management can be achieved, thereby enhancing the safety, reliability, and efficiency of the networks. The phenomenon of cognitive differences in describing underground pipeline network information across different fields is referred to as semantic heterogeneity [16]. Semantic heterogeneity may lead to data inconsistency, integration challenges, and analytical difficulties, thereby limiting the comprehensive understanding and effective management of the network system. To address the challenge of semantic heterogeneity in underground pipelines, it is necessary to adopt unified data standards and data processing techniques to facilitate interoperability and information sharing among different data sources, thereby improving management efficiency.

Based on the above research, this paper takes the underground pipeline network as the research object and designs a spatial data fusion model for underground pipeline networks that can consider geometric, temporal, and semantic aspects to meet the demand for refined modeling of these underground pipeline networks. This fusion modeling approach adopts a holistic, integrated mindset. Each pipeline, interface, or facility is regarded as a spatiotemporal object possessing a definite position and shape in space, but also as an object undergoing changes in its state over time. Integrating the concept of spatiotemporal objects means the model can record and track the historical states and future predictions of each object, such as installation time, maintenance history, and expected lifespan, thereby supporting intelligent decision-making [17]. At the same time, deeply integrating semantic information enables the model to not only store the physical and spatial attributes of each object but also understand and process their meanings and functional roles, supporting semantic-based queries and analyses, such as filtering pipelines by usage and analyzing the performance of specific networks. This approach not only meets the basic requirements of spatial data models but also incorporates advanced concepts like spatiotemporal objects, three-dimensional spatial relationships, and semantic information into the parametric modeling of underground pipeline networks. The purpose of this study is to integrate the geometric characteristics, semantic information, and temporal classifications of pipelines into three-dimensional modeling. It involves designing basic and composite parameters to calculate the rotation angles of fittings and other visual parameters based on their interface classifications, thereby laying the foundation for rapid matching of points and lines. By reorganizing pipeline data and establishing a multi-feature fusion underground pipeline model, this research aims to enhance the information carried by underground pipelines and analyze their associations comprehensively. This approach achieves effective representation of underground pipeline scenes, meeting the requirements for efficient management and comprehensive planning of underground pipelines in green urban development [18].

2. Materials and Methods

In this study, ArcGIS CityEngine 2023.0 was selected as the development software to construct the underground pipeline network scene. It is a commercial 3D modeling program developed by Esri. CityEngine supports rule-based modeling [19], which means that the designed fusion model can be used to adjust corresponding parameters and rules to automatically generate underground pipeline models. This approach is used to validate the constructed 3D spatial data fusion model of the underground pipeline network. The experiment utilized water supply, heating pipelines, and other types of pipelines as the subjects. In CityEngine, line data are based on road data form, and before importation, attributes such as pipeline outer diameter, pipeline wall thickness, pipeline type, and pipeline elevation need to be processed.

2.1. Geometric Data Model

Underground pipelines belong to concealed engineering, where specific information such as their exact locations, depths, and dimensions is not readily observable. Incomplete documentation leads to reduced accuracy of information. Within a given area, multiple types of underground pipelines may be overlaid and installed. Any modification to these pipelines may potentially impact the safety of surrounding pipelines [20]. Therefore, an accurate spatial relationship description of underground pipeline is fundamental to effective management of underground infrastructure.

Firstly, there is the geometric data model, which abstracts pipelines and associated features within underground network objects into lines and nodes. Detailed spatial relationships between elements are described within the model, along with corresponding attribute information, as illustrated in Figure 1. Through such abstraction and attribute assignment, precise models corresponding to actual underground network entities can be constructed in three-dimensional space. Building upon the analysis of this informational data, a further step involves constructing a three-dimensional model of the network to support subsequent planning, maintenance, and management tasks. The core of this process lies in simplifying the complex structure of the actual network while retaining its basic spatial relationships and geometric attribute information. Within the model, pipeline elements are abstracted into three main categories: nodes, attachment points, and lines. Nodes represent critical points within the network, such as pipe junctions, bends, or endpoints; attachment points represent accessory equipment within the pipeline network, such as valves and drain; while lines represent the pipelines themselves, traversing through various nodes. Through the aggregation and reorganization of the geometric properties and spatial relationships of these elements, a three-dimensional structure of the underground pipeline can be simulated. The geometric attributes of nodes encompass key information such as the spatial position, pipe type, and types of associated features. The geometric attributes of lines include dimensions of the pipelines (wall thickness, inner diameter, outer diameter), pipeline material, and the spatial position of the center point. The detailed description of these geometric attributes primarily serves for the visualization display and analysis of three-dimensional objects. The description of spatial relationships [21] covers sequential relationships [22], topological relationships, and metric relationships. Among these, topological relationships are particularly important in the design and implementation of geometric data models for underground pipelines, as they determine the basic connectivity and interaction modes between elements within the network. By accurately describing these topological relationships, the model can ensure not only visual accuracy but also functional authenticity in reflecting the actual operational conditions of the pipeline [23].

2.2. Semantic Data Model

The information content of an underground pipeline is rich and exhibits certain regularities. This enables the data of a pipeline to be hierarchically understood from basic to advanced levels. These levels include semantic aspects at the feature level (such as material and size), semantic aspects at the individual object level (covering temporal and spatial relationships), semantic aspects at the scene level, and semantic aspects at the event level.

The semantic data model is a specialized data model designed to describe the meaning, function, and interrelationships of the network system and its constituent elements. Its primary objective is to enhance the comprehensibility, interoperability, and maintainability of data when it comes to data sharing and application across multiple systems and organizations [24]. In the design of integrated models, the semantic data model serves as the core of the entire integrated three-dimensional spatial data model of an underground pipeline. The entire semantic data model is subdivided into the domain level, mission level, and application level, as depicted in Figure 2. The establishment of semantic models facilitates the realization of intelligent management of underground pipelines, serving as an indispensable component in the construction of smart cities, and promoting the modernization and digitization of urban management. Semantic models support the real-time monitoring and emergency responses of underground pipelines, enabling rapid identification of issues, assessment of impacts, and formulation of effective response measures during emergencies, thereby enhancing urban resilience to sudden events [25].

2.2.1. Domain Level

The design of the domain level primarily defines the core concepts, entities, and relationships of the model, as well as how they correspond to the real-world underground pipeline network. Considering factors such as the geometric characteristics, classifications, and spatial relationships of underground pipelines, the domain level defines three objects: control point, pipeline, and attachment objects. Control points here refer not only to common points with physical facilities in underground pipeline networks but also include some feature points without physical facilities. Pipeline segments refer to a basic unit of the pipeline system, representing a continuous section of pipeline within the network. The configuration of these two objects supports the network topology semantics generated by the interconnection of control points and pipelines within the pipeline system [26]. In the semantic data model, the rules mainly embody the combination of pipeline network three-dimensional entities through topological relationships, measurement relationships, semantic relationships, and temporal relationships. Therefore, the definition of rule objects is crucial.

2.2.2. Mission Level

The mission level refers to the conceptual structure that describes and defines various pipeline tasks and their related relationships. Based on the lifecycle of the pipeline network and the business classification of various departments in the pipeline management platform, six aspects of pipeline facility elements, engineering planning elements, pipeline construction elements, online monitoring elements, operation and maintenance elements, and facility retirement elements are designed. Pipeline facility elements mainly involve the recording and updating of information data on pipeline points and pipelines during daily operation. Engineering planning elements entail the overall layout scheme before the construction of the pipeline network. Pipeline construction elements involve the specific tasks and content of constructing urban underground pipeline projects in accordance with the planning scheme. Online monitoring elements refer to the task of monitoring the operation status of the pipeline network online 24 h a day. Operation and maintenance elements involve ensuring the smooth operation of each part and its related tasks and content. Facility retirement elements involve evaluating the condition of pipeline facilities, as well as the tasks and content of recycling and processing old facilities.

2.2.3. Application Level

After the design of the structures and contents of the domain and mission levels is completed, the application level is divided based on specific work tasks. The division of the application level focuses on various aspects of implementing and managing underground pipeline facilities. Current and historical states are used to record information data in pipeline facilities. In terms of application, engineering planning includes overall, zoning, and detailed planning schemes, as well as planning the statuses of pipeline points and pipelines, realizing a preliminary layout plan for the overall pipeline network from various aspects. Pipeline construction includes environmental assessment and approval to minimize the impact of the project on natural and community environments while complying with regulatory requirements. Attention is given to the preparation of construction materials, focusing on material quality, durability, and environmental performance. Completion acceptance involves confirming that the project meets design standards and safety specifications, involving various performance tests and quality audits. The preliminary construction of underground pipeline projects is completed according to the layout plan. Online monitoring includes monitoring points for gas, drainage, and water supply, as well as monitoring points for various application categories of pipelines. Monitoring data collection includes key parameters such as pressure, flow rate, and temperature. Operation and maintenance include inspectors, inspection records (including pipeline integrity and the integrity of connection points), areas of concern, and repair records. During the daily operation of pipeline facilities, inspectors record data and focus on reporting areas of concern, identifying and recording them, and making repair work more targeted and timely. When underground pipeline facilities reach their design life or need to be retired for other reasons, a comprehensive assessment of the retirement pipeline facilities is conducted, including the health status, residual value, environmental impact, etc. The safety of personnel and the environment is ensured during the dismantling process and damage to surrounding buildings and underground facilities is avoided. Therefore, the design includes two parts: assessing facility conditions and safe dismantling.

2.3. Temporal Data Model

The temporal data model of underground pipeline networks is utilized to represent and manage the temporal information of underground pipeline systems [27]. It not only encompasses the static information such as spatial position, structure, and attributes of the pipeline network but also enables the recording and tracking of the states and changes of underground pipelines and their components at different time points. Given the distinct temporal characteristics of urban underground pipeline objects, comprehensive management and planning of the pipeline system are conducted throughout various stages including design, construction, operation, monitoring, updates, and retirement. These characteristics induce geometric and spatiotemporal semantic changes throughout the entire lifecycle of the pipeline network [28]. The temporal data model supports version management of underground pipeline data, enabling the preservation and retrieval of any historical states for comparative analysis and decision support. Based on the classification of temporal information of underground pipeline objects (pipeline network temporal states, engineering planning temporal states, pipeline construction temporal states, online monitoring temporal states, and facility decommissioning temporal states), the temporal data model of underground pipeline network objects, as illustrated in Figure 3, is designed.

2.3.1. Pipeline Network Temporal States

Pipeline network temporal states record the status of and historical changes in pipeline objects due to urban development, maintenance, and repair work. These changes in spatial and attribute spatial relationships need to be effectively recorded and managed through temporal data models for historical data queries, status analysis, trend prediction, etc. Therefore, Facility Point Spatiotemporal Temporal Objects (FacilityPSTO) and Facility Line Spatiotemporal Temporal Objects (FacilityLSTO) are established. The point object contains some basic information about the current status of the point, including spatial position, point type, laying time, etc. The line object contains current information such as line diameter, material, line code, and ownership unit. Underground pipeline objects undergo new construction and deletion activities over time. During the actual construction process of underground pipelines, the laying time and change time points of pipeline objects correspond to the creation and deletion time points of objects. The lifecycle of pipeline objects is between these time periods and is recorded in point and line temporal objects.

2.3.2. Engineering Planning Temporal States

Engineering planning temporal states refers to the time scheduling and management of various stages from the design, construction, operation, and maintenance to the renovation of pipeline networks. This process covers the entire cycle from project conception to project retirement. A planning scheme temporality object class is established, mainly composed of scheme version time objects (VersionTime) and scheme version attribute objects (VersionAO). The version time object is a time point object, mainly recording the time when the planning scheme was created. The version attribute object mainly records some basic attribute information of the scheme, including scheme name, handling department, planning standards, phased goals, etc.

It can be formally expressed as follows:

VersionTO = {ID, VersionTime, VersionAO}.

2.3.3. Pipeline Construction Temporal States

Pipeline construction temporal states refer to the status of underground pipeline objects during the construction and completion delivery stages of the project. Therefore, construction area spatiotemporal objects (PrjSTO) containing construction area temporal objects (PrjTO) and construction area spatial objects (PrjSO) are established. In order to abstractly express the spatial and temporal semantics of pipeline objects in construction, the completion delivery temporality object expresses some basic information about the completion and delivery of pipeline construction after completion, and only exists as a temporality object. The construction area spatial object and construction area temporal object are combined to form a construction area spatiotemporal object class (PrjSTOCls), formally defined as follows:

PrjSTOCls = {Pr jSO,{Pr jTO1, Pr jTO2,…, Pr jTOn}}

The construction area spatial object is abstractly defined as a polygonal object. It contains static engineering basic information such as the geometric features of pipeline construction, construction unit, construction project name, and construction period. The construction area temporal object refers to the engineering information and progress of each construction process during a specific time period. It allows personnel to quickly query historical information. It mainly includes dynamic engineering information such as construction phase name, phase start time, and end time. The completion delivery temporality object expresses the information changes that occur during the construction phase. It is a single time point object with a unit of “day”. It contains time and attribute information such as delivery date, delivery items, and a delivery-responsible person.

2.3.4. Online Monitoring Temporal States

Online monitoring temporal states refers to the monitoring and management of pipeline networks throughout their lifecycle to ensure safe, efficient, and continuous operation. A monitoring point space–time object class (MonitorSTOC). It includes a monitoring point spatial object (MonitorSO) and a monitoring point temporal object (MonitorTO). The purpose is to record the monitoring indicator values of a sensor at a certain time point, which can be formalized as follows:

MonitorSTO = {ID, MonitorSO, MonitorTO}.

The monitoring point temporal object is used to represent the time semantics of monitoring at a certain time point. It includes dynamic attribute information such as monitoring ID, monitoring indicator, and monitoring time. The monitoring point spatial object is used to represent the spatial semantics of monitoring. It includes static attribute information such as geometric features, ID, and place names.

2.3.5. Facility Decommissioning Temporal States

The facility decommissioning temporal states reflects the temporal correlation of the entire lifecycle of facilities from commissioning to final retirement. It reflects the changes in the status of facilities over time. A facility processing space–time object (ProcessSTO) is established, which is composed of facility processing spatial objects (ProcessSO) and facility processing temporal objects (ProcessTO). To evaluate the status of a facility during a certain period of time, it can be formalized as follows:

ProcessSTO = {ID, ProcessSO, ProcessTO}.

The facility management temporal object reflects the time semantics of facility evaluation during a certain period of time. It includes dynamic attribute information such as ID, evaluation indicators, and fault history. The facility processing spatial object is used to represent the spatial semantics of facility processing, including static attribute information such as ID, evaluation criteria, and material conditions.

Based on the characteristics of underground pipeline networks, with underground pipeline objects as the core, geometric data models, semantic data models, and temporal data models are designed separately. A fusion model suitable for pipeline data parameterized modeling and information data sharing and its use is established. The spatial data fusion model designed is shown in Figure 4.

In the design section of the geometric model, the underground pipeline network objects are categorized into control points, pipelines, and attachments. This categorization comprises three components: geometric representation, attribute representation, and spatial relationships. They are abstractly described as nodes and lines, delineating the spatial relationships between elements and endowing them with the geometric information of the underground pipeline network, thereby forming corresponding entities in the three-dimensional geometric space of the real world. Each constituent part is associated with object IDs. In the design section of the semantic model, each three-dimensional underground pipeline object comprises semantic expression, semantic relationships, and semantic information. Based on the characteristics of underground pipeline data, the model is divided into three levels: domain level, mission level, and application level. This hierarchical approach, stemming from different perspectives and requirements, elaborates and applies the detailed semantic information of the underground pipeline network, collectively constituting a comprehensive, flexible, and efficient semantic data model. In the design section of the temporal model, each three-dimensional underground pipeline object features two components: temporal expression and attribute expression. Introducing a temporal hierarchy for each element and event of the underground pipeline network, each constituent part is associated with object IDs, forming a three-dimensional underground pipeline temporal data model [29].

3. Results

To better facilitate information storage for comprehensive management and retrieval using models, the underground pipeline scene is categorized into geometric, semantic, and temporal aspects. Through detailed research on the geometric, semantic, and temporal aspects of the data model, a spatial data fusion model for underground pipelines has been established, which integrates geometric, semantic, and temporal considerations. This model provides semantic constraints for all classes and objects within the underground pipeline network. The geometric data model accurately integrates underground pipeline objects with other entities in the spatial scene, facilitating future expansions for three-dimensional scene representation. The semantic–temporal parameters are designed based on the semantic and temporal components of the three-dimensional spatial data fusion model of the underground pipeline network mentioned above. The purpose is to reflect the different task contents of various parts of the underground pipeline system at different time points or periods, including pipeline network temporal states, engineering planning temporal states, pipeline construction temporal states, online monitoring temporal states, and facility decommissioning temporal states. These significant time points also encompass key milestones in the lifecycle of the pipeline network, such as construction dates, completion dates, maintenance periods, etc.

The rule file can be created by using the constructed three-dimensional spatial data fusion model of the underground pipeline network. The rule file can be dragged into the selected layer in CityEngine to enable the model to automatically generate [30]. Blue pipes represent water pipes, red pipes represent heating pipes, and gray pipes represent other pipes. This paper applies the pipeline model to develop a three-dimensional visualization management platform for underground pipelines. Based on the design of the pipeline spatial data fusion model, data accuracy and completeness have been enhanced, and efficient sharing of pipeline data information has been achieved, resulting in clearer and smoother browsing of the pipeline network. The spatial relationships between the control points, pipelines, and attachments in the constructed pipeline model are very apparent, and the model parameters can be modified at any time. The specific effects are illustrated in Figure 5. The method centers on underground pipeline objects, integrating geometric, temporal, and semantic characteristics by mapping and transforming these characteristics. Compared to other existing methods [12,31,32], it effectively integrates multi-feature information, enabling efficient sharing of pipeline data and enhancing data accuracy, completeness, and information content. It improves work efficiency in the rapid expansion of new scenarios and model maintenance, achieving rapid and refined modeling of underground pipelines.

4. Discussion

This study integrates the geometric, temporal, and semantic characteristics of underground pipeline networks and designs a spatial data model in three parts. Centered around underground pipeline objects, it visualizes object-oriented classes as pipeline features and encapsulates them. The research deeply investigates the geometric, temporal, and semantic characteristics of underground pipeline data. The geometric spatial data model focuses on expressing elements within the pipeline network, their attributes, and spatial relationships, providing a clearer representation of the pipeline layout and structure. The semantic data model is constructed in three levels: domain level, mission level, and application level, offering semantic constraints for underground pipeline objects. The temporal data model, based on the full lifecycle of the underground pipeline network, effectively expresses the spatiotemporal semantics generated during different temporal stages. Our results not only enrich the theoretical system of multi-feature integration and fine modeling of underground pipelines but also offer valuable references for the management and analysis of underground pipeline data and urban infrastructure planning and management in practice. However, it must be acknowledged that this study has certain limitations. For instance, it addresses only the physical representation of spatiotemporal objects during pipeline operation and maintenance and does not cover the professional requirements of underground pipeline networks, which may affect the generalizability of the results. Future research could further explore the comprehensive perception and integrated management of various types of pipeline data to address the shortcomings of this study.