Facility lifecycle data captured in BIM during design and construction are very valuable for effective facility operations and maintenance. Traditionally, model authoring and analysis tools have been used to search and query model information. These tools are not well designed to search and display needed data and they require a steep learning curve. In this paper, the authors propose the use of Power BI dashboards to facilitate easy access and display of lifecycle data embedded in the model. The implementation and use of dashboards for facility management are discussed using a case study. The effectiveness and usability of the dashboards are validated using a focus group of six industry experts that were first interviewed then asked to complete a questionnaire. Feedback from interviews indicated that customized dashboards are effective tools to view, analyze and draw insights on data from various sources and can improve facility operations and management. Numerical results from the PSSUQ using fourteen questions indicated positive responses overall with an average score of four or five from the majority of respondents. Finally, the authors tested integration of the Power BI dashboards with the HoloLens 2 to deliver relevant up-to-date facility lifecycle data in near real-time to field staff.
To automate clash resolution tasks, it is important to capture domain knowledge for the Machine Learning (ML). One way to add domain knowledge is by training data that divides tasks into input and output variables. The selection of input variables that are most relevant to a task is an important step towards automation. In this paper, the authors detail framework that uses literature review, industry interviews, and Modified Delphi to capture domain knowledge for clash resolution. The features identified through this paper can in future be processed through Feature Selection, that can provide empirical evidence of why the selected features or set of features are important to ML algorithm. Data collection processes discussed in this paper is not finalized and is discussed to help provide readers with framework of the proposed systematic method. Factors considered when resolving clashes were identified through literature review (22 factors) and industry interviews (16 factors). 14 factors identified from the interviews had a similar matching factor in the literature reviewed, the other 2 factors were not mentioned in any publications found during the initial literature review. After comparing results from literature review and interviews, 13 factors were considered critical for automating clash resolution.
Various research work has recently investigated the utilization of Machine Learning for automating the process of clash resolution during design review and coordination of BIM models on construction projects. Literature review shows that current research work focuses on using Supervised Learning for automation of clash resolution. Individual implementation of Supervised Learning has its drawbacks. The automated model trained through Supervised Learning will only be able to resolve clashes similar in nature to those clashes used to train the model. This paper proposes a new approach that integrates Supervised and Reinforcement Learning to overcome these limitations. Reinforcement Learning will assist in overcoming the dependency of Supervised Learning on training data, while Supervised Learning will reduce the time for Reinforcement Learning by eliminating iteration with low rewards or illogical solution. The proposed approach will be able to assist industry practitioners in resolving clashes efficiently and effectively.
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