•A new SPD method entitled cyclic extrusion compression angular pressing is presented.•The new process solve drawback of CEC method which needs backpressure.•Higher hydrostatic compressive stress is ...achieved compared to ECAP.•Significant grain refinement and excellent mechanical properties were achieved.•Higher strain per pass was achieved.
In the current study, a novel severe plastic deformation (SPD) technique entitled cyclic extrusion channel angular pressing (CECAP) is proposed appropriate for producing ultrafine grained cylindrical material. In this new method, cyclic extrusion compression (CEC) and equal channel angular pressing (ECAP) processes are combined which solves drawbacks of both processes. Advantageous of the new process may be considered in two views of points of processing and properties. This new process increases the hydrostatic compressive stress of ECAP as well as shear to the total strain of CEC. Also, CECAP solves needing back pressure problem of CEC process which is an important feature of the new process. The process treated an AZ91 alloy and remarkable grain refinement and significant improvement in strength, ductility and hardness were achieved because of higher hydrostatic compressive stresses.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Wayfinding is an issue in complex facilities—including hospitals, airports, and office buildings—and wayfinding difficulties are associated with negative psychological and physiological consequences. ...In addition, since finding one’s way in a building is a prerequisite for successfully using that building, wayfinding has attracted the attention of scholars and decision makers. The goal of this article is to review and synthesize the published literature on wayfinding in interior environments. A systematic search was conducted of four databases: PsychINFO, JSTOR, ProQuest, and EBSCO. A hand search was also conducted. From the initial harvest of 804 records, a total of 84 records met the inclusion criteria for full review. After several rounds of review, four broad domains were identified: (1) wayfinding cognition, (2) wayfinding behavior, (3) individual and group differences, and (4) environmental factors. These domains are used as a framework to organize the findings, and the review shows that the sub-domains most thoroughly addressed in the literature are spatial memories, floor plan configuration, landmarks, signs, and maps. This review can deepen the field’s understanding of factors that contribute to interior wayfinding and can serve as a resource for decision makers and designers.
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.
Current as-built Building Information Model (BIM) deliverables generated following design and construction generally do not fulfill facility management needs. To provide total value to the facility ...owner, as-built models should be configured to provide needed critical operation and maintenance information, solve operational challenges, and improve safety and efficiency in various ways. In addition to having complete and accurate data, as-built BIMs should be configured to be systems-centric enabling facility owners to visualize model graphics from a systems perspective. This is critical during a maintenance emergency when the model user needs to identify components belonging to the building systems that are contributing to or impacted by the emergency and be able to make well-informed timely decisions. This paper contributes to the existing body of knowledge by defining and documenting requirements and procedures for configuring systems-centric as-built BIM models. A multi-level building systems classification is developed, and fourteen specific properties are defined. The implementation is discussed using a case study approach analyzing the mechanical and plumbing systems for a science and classroom building of a 4-year academic institution. By developing systems-centric models, facility staff can respond to emergency maintenance and analyze the impact of the event by isolating the elements of a specific system related to the emergency and display these elements explicitly.
•Current as-built Building Information Model (BIM) do not fulfill facility management needs.•As-built BIM should be systems-centric to allow visualization and analysis of the building systems.•Requirements and procedures for configuring a systems-centric as-built BIM model is proposed in this paper.•A system classification as well as systems-centric properties are developed and identified in this paper.•Facility staff can respond to emergency maintenance and analyze their impact using the proposed models.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Purpose
The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of ...facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.
Design/methodology/approach
This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.
Findings
The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.
Originality/value
Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.
Current practices for prioritizing maintenance work orders are mainly user-driven and lack consistency in collecting, processing, and managing the large amount of data. While decision-making methods ...have been used to address some of the existing challenges such as inconsistency, they also have challenges including variation between comparison during the actual prioritization task as opposed to those outside of maintenance context. The data analytics and machine learning methods can help with extracting meaningful and valuable information, finding patterns, and drawing conclusions from the available data. Such methods have benefits including faster prioritization performance leading to less failure and downtimes, reduced impact of knowledge loss, decreased cognitive workload, identification of errors for adjusting the system, and determination of important factors impacting work order processing to support the development of data requirements. This paper summarizes the background on existing gaps in processing maintenance work orders and provides an overview of machine learning methods to support prioritizing work order. The paper then discusses the work order data of an educational facility as a case study, presents information on data exploration and data cleaning approach, and provides insights gained from their maintenance work order data. The insights gained present challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for addressing some of the work orders, and the correlation between criteria collected by the facility and the schedule. The paper continues by implementing artificial neural networks to benefit from work order data collected for automatically prioritizing the future work orders. The results present the optimum neural network structure based on mean squared error estimated and provides the best value for each parameter used for the development of the model. The accuracy and efficiency of the developed model was validated by the facility experts of the educational facility.
A vast amount of work orders is submitted daily which is a critical component of management for any facility. The process taken for prioritizing work orders, however, shows a high dependency on the ...extent of knowledge and experience of responsible staff available and is challenged by inconsistency in data collection, and uncertainty in decision-making. Making decisions and responding to a high number of requests demand intensive labor hours resulting in delays causing issues for facility managers. The high number of service requests, various work orders, and the required balance between cost and budget highlight the importance of the need for improving work order processing to optimize time and cost of buildings' operation. Through review of the literature, unstructured and semi-structured interviews, and a qualitative analysis approach, this paper identifies various challenges and gaps in user-driven decision-making for processing work orders and determines best practices. The challenges identified include inconsistency in prioritizing orders, lack of data requirements and knowledge management, cognitive workload and biases, and inconsistency in data collection. Using data-driven decision-making methods can address existing challenges, improve the process of prioritizing work orders and enhance the quality of the work performed by timely responding to submitted requests. This will improve the operation and maintenance of building facilities and increase occupants’ satisfaction.
•Prioritizing and processing work orders constitute a big part of facility management.•Data requirements need to be defined to achieve more consistent and accurate results.•The paper explores existing practices for processing work orders.•Outcome of the study can be used for developing data requirements.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
Doctor of Philosophy
Facility Management (FM) is a profession that integrates various disciplines to ensure the comfort and safety of the occupants, efficiency of the built environment, and functionality of the building while meeting the main objectives of the owners. It involves various functions, including space management, communication, contract management, inspection, etc. Among many of these FM functions, maintenance-related tasks occupy 79% of the facility managers' responsibilities and %60 of the building cost in its whole lifecycle (design, construction, and operation). Prioritizing and processing work orders constitute a big part of facility maintenance management and requires a large amount of information submitted with hundreds of orders that need to be prioritized and turned into actions on a daily basis.
Although vast amounts of work orders are submitted daily, the process of prioritizing orders has been done manually or partially through management systems rendering the process very challenging. The existing practices are highly dependent on the extent of knowledge, experience, and judgment of responsible staff available, are impacted by human cognitive workload and coping strategies and are challenged by inconsistency in data collection and uncertainty in decision-making. Delays in processing work orders can lead to asset downtimes and failure impacting occupants' comfort, health, and safety while increasing the cost of operation. Additionally, based on the results of previous studies, the alternative comparison for prioritizing work orders varies and is more realistic when performed during the actual work order prioritization task as opposed to outside of the maintenance context.
Artificial Intelligence (AI) and Machine Learning (ML) algorithms have provided opportunities to benefit from the historical data collected and stored by the facilities. Since a large number of work orders are generated and stored by facilities, such methods can be used to address the challenges with existing practices to reduce errors, downtimes, and asset failures and improve the operation of the buildings by supporting automation within the systems.
This dissertation first aims to explore the existing practices for processing and prioritizing work orders and identifying their gaps and challenges. Second, it investigates the implementation of Artificial Neural Networks (ANNs) to automate the prioritization of future work orders. The ANN is one type of machine learning model which reflects and mimics the behavior of the human brain to understand the relationship between a set of data allowing computer programs to solve complex problems. This research will improve the existing practices for processing work orders by allowing the automation of future work order prioritization. It also provides the basis for the development of data requirements to further support existing practices.
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