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Browsing by Author "Ensafi, Mahnaz"

Now showing 1 - 3 of 3
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    Linking BIM to Power BI and HoloLens 2 to Support Facility Management: A Case Study Approach
    Harode, Ashit; Ensafi, Mahnaz; Thabet, Walid (MDPI, 2022-06-18)
    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.
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    Real-Time and Remote Construction Progress Monitoring with a Quadruped Robot Using Augmented Reality
    Halder, Srijeet; Afsari, Kereshmeh; Serdakowski, John; DeVito, Stephen; Ensafi, Mahnaz; Thabet, Walid (MDPI, 2022-11-19)
    Construction progress monitoring involves a set of inspection tasks with repetitive in-person observations on the site. The current manual inspection process in construction is time-consuming, inefficient and inconsistent mainly due to human limitations in the ability to persistently and accurately walkthrough the job site and observe the as-built status of which robots are considerably better. Enabling the process of visual inspection with a real-time and remote inspection capability using robots can provide more frequent and accessible construction progress data for inspectors to improve the quality of inspection and monitoring. Also, integrating remote inspection with an Augmented Reality (AR) platform can help the inspector to verify as-planned BIM data with the as-built status. This paper proposes a new approach to perform remote monitoring of the construction progress in real-time using a quadruped robot and an AR solution. The proposed computational framework in this study uses a cloud-based solution to integrate the quadruped robot’s control for remote navigation through the construction site with 360° live-stream video of the construction status, as well as a real-time AR solution to visualize and compare the as-built status with as-planned BIM geometry. The implementation of the proposed framework is discussed, and the developed framework is evaluated in two use cases through experimental investigations.
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    Work Order Prioritization Using Neural Networks to Improve Building Operation
    Ensafi, Mahnaz (Virginia Tech, 2022-10-20)
    Facility management involves a variety of processes with a large amount of data for managing and maintaining facilities. Processing and prioritizing work orders constitute a big part of facility management, given the large number of work orders submitted daily. Current practices for prioritizing work orders are mainly user-driven and lack consistency in collecting, processing, and managing a large amount of data. Decision-making methods have been used to address challenges such as inconsistency. However, they have challenges, including variations between comparisons during the actual prioritization task as opposed to those outside of the maintenance context. Data-driven methods can help bridge the gap by extracting meaningful and valuable information and patterns to support future decision-makings. Through a review of the literature, interviews, and survey questionnaires, this research explored different industry practices in various facilities and identified challenges and gaps with existing practices. Challenges include inconsistency in data collection and prioritizing work orders, lack of data requirements, and coping strategies and biases. The collected data showed the list of criteria and their rankings for different facilities and demonstrated the possible impact of facility type, size, and years of experience on criteria selection and ranking. Based on the results, this research proposed a methodology to automate the process of prioritizing work orders using Neural Networks. The research analyzed the work order data obtained from an educational facility, explained data cleaning and preprocessing approaches, and provided insights. The data exploration and preprocessing revealed challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for work orders' execution, and lack of correlation between collected criteria and the schedule. Through hyperparameter tuning, the optimum neural network configuration was identified. The developed neural network predicts the schedule of new work orders based on the existing data. The outcome of this research can be used to develop requirements and guidelines for collecting and processing work order data, improve the accuracy of work order scheduling, and increase the efficiency of existing practices using data-driven approaches.