Critical Construction Data: Comparison
Please note this is a comparison between Version 1 by Wei-Tong Max Chen and Version 2 by Rita Xu.

Construction projects are inherently complex and entail extensive information processing. Thus, they require effective information management, which, in turn, requires the preservation of critical construction data (CD).

  • data redundancy
  • refurbishing heritage buildings
  • network analysis

1. Introduction


Increases in the complexity and scope of a construction project increase the amount of project information that is generated and required [1]. Such information comes in various forms (e.g., checklists, manuals, codebooks, contracts, and drawings), and is generated through complex connections in the construction project’s life cycle. The effective management of such information is a difficult and frequently overlooked aspect of construction project management [2].
Interactive collaboration on an engineering project should be based on the consistent use of information by all members of the project team. Project information is often modified according to feedback from other groups (e.g., change orders or unexpected new discoveries in the subsequent phase of the project’s life cycle), requiring continuous revision and updating. During the construction phase, the original design drawings should also be adjusted in response to change orders. Interactions between project groups result in bilateral information flows, which shift across different life cycle stages [3]. As a result, common task data must be properly updated and easily accessible through a centralized database to ensure that every member obtains consistent and timely information [4]. However, the absence of a uniform and transparent system of construction project information undermines the assurance process and may lead to disputes during a construction project [5].
Poor data management can interfere with important construction tasks, including project monitoring and management. Engineers and managers frequently waste valuable time and effort collecting and processing construction data from engineering documents [6]. To manage document information, the engineering project team continuously updates specific learning databases to share information with all project team members [5][7][8][5,7,8]. The collected data must readily interrogatable by designers and engineers developing data process specifications. Effective data processing can ensure the quality (e.g., compressive, query-efficiency, original data tracking, and correct functioning) of engineering information [8][9][8,9]. Therefore, database systems still require significant project management efforts to integrate information and analyze relationships among datasets.
Building information modeling (BIM) is a database approach that is highly effective for knowledge sharing in architecture engineering and construction (AEC) projects [10][11][12][13][10,11,12,13]. However, BIM data storage needs to be integrated to maximize the efficiency of the database process [14]. Since BIM data are locked inside vendor-specific implementations and interfaces [8], nonspatial information remains difficult to query for BIM. Solihin et al. [8] proposed an integrated approach according to the concept of specialized construction data models. The research developed an interface of flexible and efficient queries, including BIM data, with a standard SQL which removed restrictions on predefined queries, effectively transforming the BIM data into an open and query-able database. Furthermore, there is another storage development aspect of data management. Blockchain technology has been adopted to preserve original data when information is offered to all members during a cooperative task of a construction project [5][15][16][5,15,16]. However, the costs of processing data and storage need to be evaluated when a large amount of data are stored as blockchains.
Engineering project documents combine multiple data sources from various stages of an engineering project’s life cycle. These engineering project data items, such as forms and handbooks, are created using the same basic design content and requirements, whereas the content in the engineering project document is cross-referenced. Additionally, the traditional database of a construction project is constructed according to the IFC standard [8]. Thus, combining these two practical aspects allows for a relatively large number of entities in the relational database of a construction project to be generated. The differences in the hierarchical natures of object-oriented data (e.g., BIM) and relational data involve performance issues [17], while data redundancy still exists in BIM databases with nonspatial information. Consequently, various databases need to deal with the issue of a relatively large number of entities in the relational database [8]. To enhance construction project data management, a relatively large number of entities need to be eliminated from the relational database, particularly those related to issues of query efficiency and the limited source allocation for data management. To facilitate data integration across different tables in a database, developers add redundant links. Unfortunately, these redundant table links have a negative impact on the database [18][19][20][21][18,19,20,21].
Data redundancy refers to circumstances in which the same data are included in multiple tables in a database system. Non-compressed databases typically lack data redundancy, which has a negative impact on the database’s operating performance. However, database developers typically provide multiple extra connections among database tables, which can also negatively impact the database [22]. Some data reduction methods can be used to ensure data integrity and non-repetitiveness [22][23][22,23]. Database frames are commonly associated with document-based meta-information, allowing for the reorganization of a database into integrated information structures for specific users or tasks. Such meta-information is reprocessed by extracting critical information from the original documents [24][25][24,25] to achieve effective data integration. However, the duplicate entities in a relational database are not removed. Therefore, this research focuses on the developing concept of a relational database structure with regard to the criterion of data elimination (extracting critical data) from a construction project. The foreign key can be used for evaluating table connections, while the prime key serves as the basic analysis material of a table in a relational database. To eliminate the negative effect of duplicate data in a database, the data need to be properly identified as prime or foreign keys of a relational database according to the criteria of data integration or data reference.
Database development focuses on balancing the information’s comprehensiveness and the data capture efficiency to maximize performance and reduce errors. Critical project engineering data can be used as foreign keys of relational databases for engineering projects. To be an appropriate foreign key of a database, critical data must be used in various processes during the project’s life cycle. Therefore, critical data must be integrated with many other data types through association relations using prime keys that correspond to foreign keys between different database tables. Screening methods for construction project data can effectively eliminate outdated and redundant data, which are less important for engineering. Such data should be decomposed from the construction project documents, and their utility must be evaluated using engineering practices that connect prime and foreign key databases. Additionally, the critical data need to be assigned with consideration of resource limitation (cost, time, and manpower) for data management, given that adding sources can requires additional data management (e.g., verifying the accuracy of the data and instantly updating data). The prioritized sources and types of data need to be diligently evaluated to achieve efficient management of information and resources.

2. Relationship between Network Theory and Information Processes

Construction project information characteristics are cross-referenced and non-standardized using an information structure similar to network graph topology. Since the main goal of this study was to analyze construction project information using network theory, hithis section highlights how previous studies have used network theory to extract fuzzy information. ResearchersWe then briefly introduce construction project characteristics and summarize the corresponding relationships between the information network and network theory. Network theory is a methodology for studying the topology and objects of complex networks using quantitative and qualitative graph-based analysis. Network theory has been used to solve problems in social science, computer science, data mining, and other related fields [26][27][28][29][30][31][32][33][26,27,28,29,30,31,32,33]. These applications applied the same core concept to demonstrate and analyze relationships between characteristics and actions, using a visual medium to present the relationships between different actors and allowing for full and precise descriptions of authentic conditions without resorting to complex mathematical equations. Network theory includes four major components: graph theory, social networks, online social networks, and graph mining [26]. It has been applied to communities, influence and recommendation, model metrics and dynamics, behavior and relationships, and information diffusion. Social network analysis (SNA) is a commonly used application in network theory, focused on explicating complex social interactions. Social interactions can be observed in issues related to communication, trust, and consultation [30]. Social group agents are the nodes, while relations between agents are the edges, collectively representing interactions between social groups [28]. In addition, network metrics can be used to evaluate the status of agents in social groups and the state of the entire social group. The network density and the distance between the core agent and other nodes can be used to extract relevant knowledge from practice [31][32][31,32].

3. Basic Information Network Concept

3.1. Information Network Components

Since this research focuses on referencing the utility of information for construction projects, the data reference status needs to be indicated specifically. This research develops a unique network structure in which construction project data are the information network nodes (actors), and the corresponding processes between the project data are the network connections (edges). The network diagram can represent the relationships among the project data at both the micro (nodes connection) and macro (network structure) levels. The significance of critical data is determined based on the frequency with which they are referenced by other data. As a result, the critical data in this study use an information network graph to simulate information flows and interactions. In addition, data which contribute more utility to information networks are more important for construction projects. Thus, the critical data identification criteria suggest that the data have a high level of information utility, and the centrality metric of nodes is used to describe the critical data utility.

3.2. Correlation between Data Utilities and Metrics

Data contribution can be observed through the information flow that presents the data-referencing status. This study aims to evaluate the interactive reference between all data (nodes); thus, the information network metrics should focus on centrality and bridge the issues related to the network analysis metrics. The network analysis centrality issue metric is used to measure the level of influence for all network nodes. The centrality metrics of network nodes indicate their levels of status or power [32][34][32,34]. As previously noted, critical data are used as the database’s foreign keys, and their features must meet the functional requirements of the foreign key. As a result, the critical data features of a construction project must be linked synchronously to the other project management data. Critical data in construction engineering must be identified using information network centrality metrics, which indicate the referencing importance and direct citation frequency of data for the construction project database. Two centrality issue metrics for network analysis used in this study are degree and closeness. In a construction project, subsequent phase tasks are based on certain fundamental information from the previous phase. Several information network clusters are formed using the operating act in a single engineering project phase. To maintain the consistency of the information, critical data carry key information from one cluster to the next. Information delivery and indirect reference resemble the “bridge” action of network theory, while the evaluating metric for data bridge utility is the “betweenness.” Within the database’s comprehensive data set, critical data link to or represent the foreign and prime database keys. Critical data meet one of three metric criteria of an information network; thus, these metrics are not unique, and the result of critical data is the union of superior data from the three criteria. When one of the conditions is met, the data are cited as critical information network data if the condition has high metric value for the central or bridge issue.
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