Objective:
To develop methods, metrics, specifications, test cases, and a testbed for evaluating and extending protocols and standards for enabling the exchange, access, and integration of construction project data. Investigate the challenges and measurement science for improving construction productivity with the integration of information, communication, and automation technologies.

Background:
U.S. industries seek to improve the design, delivery and operation of constructed facilities through advanced uses of information technologies, e.g., CAD, CAE, CAM, ERP, SCM and eBusiness (computer-aided design, computer-aided engineering, computer-aided manufacturing, enterprise resource planning, supply chain management and internet enabled commercial transactions), and the integration of the information systems, e.g., automation of the exchange and sharing of information among systems. Although most architecture, engineering and construction organizations have adopted aspects of 3D modeling, project information systems, and information management technologies in the design and project documentation phase of constructed facilities projects, the capabilities and benefits of these technologies are not being exploited for the integration with supplier engineering systems and across the fabrication, inspection, delivery, construction and operation phases. Additionally, the integration of equipment and system engineering information for facility commissioning, maintenance, repair and operations is restricted by the lack of automated information sharing and re-use capabilities.

The digital exchange and integration of design models, equipment specifications, construction plans and related information is impeded, in most cases, by the absence of consistent vocabularies, identifiers and encoding formats for data used by the firms involved. Although the industry is making progress in developing common practices for the delivery of basic building information models, e.g., 3D models for clash detection and space management, automated information exchange and the integration of information from diverse disciplines and specialty contractors is a major challenge to improving construction productivity. . As a result, information is often manually interpreted or re-created by humans and re-entered into one or more different software systems. This practice, which is often repeated over and over again by each participant in the engineering, equipment specification, construction and operations work processes, is labor and time-intensive, error prone and costly. Due to the lack of the enabling measurement science for developing, testing and deploying robust data exchange protocols and the lack methods and resources for semantic harmonization and alignment across “intersecting information domains”, early efforts to solve these problems had minimal success.

The Construction industry has repeatedly identified the deterioration in construction productivity as a critical problem and the need to achieve Fully-Integrated and Automated Project Processes. The FIATECH Capital Projects Technology Roadmap and many of the Construction Industry Institute (CII) reports have conveyed these findings. The CII Project Team 180, eCommerce for Construction, reported that leading adopters of eCommerce for capital facilities projects have not succeeded in exploiting this technology for the design and delivery of equipment. The lack of enabling measurement science for the development and testing of interoperability specifications is a primary barrier.

Measurement science for semantic modeling and integration and metrics for measuring data model congruency are needed to address these barriers. This will require proven methods to determine:

Degrees of Congruency

• measure the similarity of scopes of two different semantic data models --- to what extent do they cover the same concepts and what are their differences

• measure the similarity of two different concepts, whether represented in the same model or between two different models, at any level of detail

• to what extent do they capture the same semantic details

Content and Structural Alignment

• measure the content of a semantic data model --- to what extent does it fulfill its intended scope

• determining how semantic data models can be combined or connected, e.g., data models for project information, building information, sensor information, control information and automation information

Completeness of Test Suites

• measure the completeness of a test suite against the scope and content of a model --- whether for verifying a data model or certifying a software implementation
• To test, evaluate and refine the needed advancements in semantic modeling and data integration methods and metrics requires a Virtual Project Data Integration Testbed. This will be essential for enabling the collaboration with other institutions on this research and to demonstrate to the industry the challenges and benefits of applying the research results.

Page created October 2007

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