No abstract

Purpose Organizations involved in facility management (FM) can use building information modeling (BIM) as a knowledge repository to document evolving facility information and to support decisions made by the facility managers during the operational life of a facility. Despite ongoing advances in FM technologies, FM practices in most facilities are still labor intensive, time consuming and often rely on unreliable and outdated information. To address these shortcomings, the purpose of this study is to propose an automated approach that demonstrates the potential of using BIM to develop algorithms that automate decision-making for FM applications. Design/methodology/approach A BIM plug-in tool is developed that uses a fault detection and diagnostics (FDD) algorithm to automate the process of detecting malfunctioning heating, ventilation, and air conditioning (HVAC) equipment. The algorithm connects to a complaint ticket database and automates BIM to determine potentially damaged HVAC system components and develops a plan of action for the facility inspectors accordingly. The approach has been implemented as a case study in an operating facility to improve the process of HVAC system diagnosis and repair. Findings By implementing the proposed application in a case study, the authors found that automated BIM approaches such as the one developed in this study, can be highly beneficial in FM practices by increasing productivity and lowering costs associated with decision-making. Originality/value This study introduces an innovative approach that leverages BIM for automated fault detection in operational buildings. FM personnel in charge of HVAC inspection and repair can highly benefit from the proposed approach, as it eliminates the time required to locate HVAC equipment at fault manually.

No abstract

Marker-based pose estimation, in which optical cameras monitor fiducial markers to determine the three-dimensional positioning and orientation of an articulated machine's end effector, has been identified as a potential low-cost alternative to currently available machine control and guidance systems. In an effort to develop such a marker-based pose estimation system for excavators, several iterations of prototypes were designed, fabricated, and tested. Performance was measured in terms of the system's ability to estimate bucket tooth position, with an acceptance criterion of 2.5 centimeters (1 inch) of absolute error. Although initial prototypes were found to possess practicality and performance issues, a fourth prototype offered encouraging experimental results suggesting the feasibility of marker-based sensor technology for excavator pose estimation. Further work needed to refine the technology for large-scale practical implementation was also identified.

No abstract