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Salman, A. Management of Facility Assets. Encyclopedia. Available online: https://encyclopedia.pub/entry/54702 (accessed on 20 May 2024).
Salman A. Management of Facility Assets. Encyclopedia. Available at: https://encyclopedia.pub/entry/54702. Accessed May 20, 2024.
Salman, Alaa. "Management of Facility Assets" Encyclopedia, https://encyclopedia.pub/entry/54702 (accessed May 20, 2024).
Salman, A. (2024, February 02). Management of Facility Assets. In Encyclopedia. https://encyclopedia.pub/entry/54702
Salman, Alaa. "Management of Facility Assets." Encyclopedia. Web. 02 February, 2024.
Management of Facility Assets
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This entry is adapted from the peer-reviewed paper 10.3390/buildings14020339

Effective facility asset management requires specific skills and tools to optimize the use of limited resources, making a decision support system essential.

facility management Analytical Hierarchy Process (AHP) Artificial Neural Network (ANN)

1. Introduction

Working in a facility for eight hours a day requires a healthy environment. A typical facility consists of numerous assets that provide specific services. Examples of such assets include elevators, HVAC, doors, water and wastewater systems, electrical systems, security systems, and others. A study conducted by [1] identified that 38% of variations in productivity are attributed to the workplace environment. Therefore, maintaining facility assets leads to a satisfactory experience for facility users and increases their productivity. Currently, there is no standardized system that provides an optimum maintenance program. In addition to limited resources, a major consideration is that a facility includes various asset types—mechanical, electrical, and structural—and each type encompasses several individual assets. These assets vary in sizes, shapes, capacities, and locations, necessitating different programs throughout their life cycles. The output of any developed system should provide a straightforward solution: maintain current and future assets to deliver the required services within the constraints of limited resources. On the other hand, decision-makers overseeing infrastructure assets request billions of dollars annually to maintain their infrastructure, aiming to achieve the required level of service while minimizing risks [2]. The primary challenge in this context is the limitation of available budgets. However, it is crucial to first identify the necessary rehabilitation actions and then prioritize these actions to effectively manage the available budget. The required rehabilitation actions, categorized into five types, no action, operational, minor maintenance, major maintenance, and replacement [3], are determined and prioritized based on the consequences of failure, specifically the criticality of an asset.

2. Facility Maintenance Practices

Many organizations (such as academic, medical, and municipal sectors) adhere to specific asset maintenance policies. These assets are categorized into groups and subgroups, with maintenance types delineated as planned versus unplanned. Moreover, there exists a prioritization of maintenance activities. A gap analysis based on a set of questionnaires is conducted to compare current practices with established standards [4]. It is determined that the differences between both are not big with respect to maintenance types, encompassing predictive, preventive, proactive, reactive, planned, program, improvement, corrective, breakdown, and breakdown emergency. Priorities of building maintenance from stakeholders’ perspectives were analyzed [5], and the maintenance programs are divided into structural, architectural, electrical, and mechanical. Maintenance programs are categorized by [6] into routine maintenance (HVAC, plumbing, electrical, painting, carpentry, lock/key, and general maintenance), preventive maintenance (structural elements), and deferred maintenance (it will be scheduled as needed). The prioritization of these maintenances is on emergency, urgent, priority, and routine schedules. A priority of rehabilitation matrix, which is 5 × 5, was developed by [7] based on criticality and likelihood. Additionally, inspection requirements of facilities have been identified according to a checklist form, which encompasses nine criteria: safe and orderly operating conditions, fire safety, earthquake safety, electrical safety, chemical storage, hazardous waste, compressed gases, building structures, and miscellaneous [8].

3. Criticality

Criticality, or consequences of failure [3], pertains to the outcomes or impacts arising from the malfunction, breakdown, or failure of a system, component, process, or entity. Comprehending the consequences of failure is vital in various fields, such as engineering, risk management, and decision-making, as it aids in evaluating the potential risks and implications linked to a failure event. Criticality may be categorized into four types: economic, operational, social, and environmental [9]. Criticality is studied by several researchers as a decision tool for infrastructure maintenance programs. A criticality model was developed for the predictive maintenance of the bridge group [10]. The model includes deterioration, optimal maintenance, penalty cost function, group maintenance, and scheduling models, ultimately resulting in the ranking of bridges based on their criticalities. Criticality was employed to assess the impact of a rail component system [11]. In a separate study, a criticality model for a distribution water network was formulated [12]. The model relies on four integrated indices: water age degradation, pressure decrease, economic value loss, and supply shortage. Applying a risk framework conducted by [13], the criticality model for sewer pipes considers their conditions and prioritizes maintenance activities by taking into account economic, social, and environmental factors.

4. Rehabilitation Selection Methods

The selection of rehabilitation methods has been studied extensively by several researchers. MAUT is selected for the best rehabilitation methods of infrastructure assets [14], rehabilitation of the historic bridges [15], the optimal alternative of rehabilitation of a drainage channel [16]. A dynamic programming model was used by [17] in order to select the alternative rehabilitation of water networks. MINLP is utilized [8] to select the best rehabilitation methods for the water distribution network. Mechanistic Analysis and Field Diagnosis, applied by [18], were utilized to select the best rehabilitation methods for road pavement, while a holistic approach, as suggested by [19], was employed for sewer rehabilitation segments based on the cost-effectiveness of the method. The authors in [20] stated that the optimal combination of scheduled and unscheduled maintenance to ensure occupant contentment is an appropriate solution. The majority of the studies, such as the one estimating the early cost of a concrete bridge using an ANN model [21], and a study by [22] identifying the ANN as an effective method, focus on cost estimation, including predicting the Construction Cost Index (CCI). An integration model of an ANN and AHP, developed by [23], was employed to estimate the cost of road networks, and [24] utilized an ANN to predict the final cost of the construction project. The authors in [25] used an ANN to predict the cost budgeting in the auction process, while [26] carried out an investigation on the effect of communication on rework in construction projects. The estimation of annual maintenance for the infrastructure assets with the aid of an ANN was considered by [27]. The required cost for roof maintenance systems was predicted by [28]. Other researchers utilized an ANN for the scheduling process. The application of an ANN and neuro-fuzzy for construction scheduling was studied by [29]. The authors in [30] predicted the construction contract duration for construction projects. An optimum scheduling model using an ANN was developed by the authors in [31]. According to a study by [32], the prediction of project duration using an ANN was improved, and an optimal scheduling model for the rehabilitation of water pipes can be obtained with respect to different constraints, as mentioned by [33]. Other studies, such as [34], used artificial neural networks (ANNs) to address various construction topics, including the development of a mathematical model to estimate the condition of water distribution networks. The estimation of project effort was carried out by [35] using an ANN. A risk assessment model for infrastructure projects was developed by [36] using a BP-ANN algorithm. The sustainability index for water management was assessed with the utilization of an ANN [37]. A hybrid ANN was developed by [38] to evaluate the sustainability of construction projects.

References

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  2. Acharyya, A. Public Investment in Household Water Infrastructure and Economic Development: An Achilles Heel for India? 2021. Available online: https://www.researchgate.net/publication/355427630 (accessed on 1 December 2023).
  3. National Asset Management Support Group (NAMS). International Infrastructure Management Manual (IIMM); National Asset Management Support Group (NAMS): Thames, New Zealand, 2011.
  4. Zawawi, Z.A.; Khalid, M.K.A.; Ahmad, N.A.; Zahari, N.F.; Salim, N.A.A. Operation and Maintenance in Facilities Management Practices: A Gap Analysis in Malaysia. In Proceedings of the 4th International Building Control Conference 2016 (IBCC 2016), Kuala Lumpur, Malaysia, 7–8 March 2016.
  5. Štrok, K.T.; Car-Pušić, D.; Marenjak, S. Priorities in Croatian School Building Maintenance: A Comparison of the Main Stakeholders’ Views. Sustainability 2023, 15, 11767.
  6. Williams & Associates. Facilities Maintenance Plan; Williams & Associates: Amador County, CA, USA, 2022.
  7. University of Alberta. Facilities Maintenance Plan; University of Alberta: Edmonton, AB, Canada, 2023.
  8. University of Alaska System. Building Blitz Manual; University of Alaska System: Juneau, AK, USA, 2018.
  9. Salman, A.; Moselhi, O.; Zayed, T. Scheduling Model for Rehabilitation of Distribution Networks Using MINLP. J. Constr. Eng. Manag. 2013, 139, 498–509.
  10. Hadjidemetriou, G.M.; Herrera, M.; Parlikad, A.K. Condition and criticality-based predictive maintenance prioritisation for networks of bridges. Struct. Infrastruct. Eng. 2022, 18, 1207–1221.
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  15. Jajac, N.; Rogulj, K.; Radnić, J. Selection of the Method for Rehabilitation of Historic Bridges—A Decision Support Concept for the Planning of Rehabilitation Projects. Int. J. Arch. Heritage 2017, 11, 261–277.
  16. Chowdhury, R.K.; Zaman, A.U. Selection of the optimal alternative: Rehabilitation of a regional drainage channel in Bangladesh. Urban Water J. 2009, 6, 395–405.
  17. Kleiner, Y.; Adams, B.J.; Rogers, J.S. Selection and scheduling of rehabilitation alternatives for water distribution systems. Water Resour. Res. 1998, 34, 2053–2061.
  18. Zaghloul, S.; Elfino, M. Pavement Rehabilitation Selection Based on Mechanistic Analysis and Field Diagnosis of Falling Weight Deflectometer Data: Virginia Experience. Transp. Res. Rec. 2000, 1730, 177–186.
  19. Hutchinson, R.E.; El-Sayegh, H.K.; Chambers, L. Advances and Experiences with Trenchless Pipeline Projects; ASCE Library: Reston, VA, USA, 2007.
  20. Weidner, T.J. Planned maintenance vs Unplanned maintenance and facility costs. IOP Conf. Ser. Earth Environ. Sci. 2023, 1176, 012037.
  21. Fernando, N.; Dilshan, T.A.K.; Zhang, H. An artificial neural network (ANN) approach for early cost estimation of concrete bridge systems in developing countries: The case of Sri Lanka. J. Financial Manag. Prop. Constr. 2004, 22, 595–602.
  22. Aslam, B.; Maqsoom, A.; Inam, H.; Basharat, M.U.; Ullah, F. Forecasting Construction Cost Index through Artificial Intelligence. Societies 2023, 13, 219.
  23. Chala, G.; Tadesse, F.; Teferi, H.; Chala, G.D. Integrated Approach of Cost Estimation for Road Projects by Using Artificial Neural Network (ANN) and Analytic Hierarchy Process (AHP). J. Xidian Univ. 2021, 15, 212–225.
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  26. Trach, R.; Trach, Y.; Lendo-Siwicka, M. Using ann to predict the impact of communication factors on the rework cost in construction projects. Energies 2021, 14, 4376.
  27. Woldemariam, W.; Murillo-Hoyos, J.; Labi, S. Estimating Annual Maintenance Expenditures for Infrastructure: Artificial Neural Network Approach. J. Infrastruct. Syst. 2016, 22, 04015025.
  28. Alashari, M.; El-Rayes, K.; Helaly, H. Machine-Learning Model for Predicting Maintenance Costs of EPDM Roofing Systems. J. Perform. Constr. Facil. 2023, 37, 04023048.
  29. Obianyo, J.I.; Udeala, R.C.; Alaneme, G.U. Application of neural networks and neuro-fuzzy models in construction scheduling. Sci. Rep. 2023, 13, 8199.
  30. Alsugair, A.M.; Al-Gahtani, K.S.; Alsanabani, N.M.; Alabduljabbar, A.A.; Almohsen, A.S. Artificial Neural Network Model to Predict Final Construction Contract Duration. Appl. Sci. 2023, 13, 8078.
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  32. Lishner, I.; Shtub, A. Using an Artificial Neural Network for Improving the Prediction of Project Duration. Mathematics 2022, 10, 4189.
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Subjects: Engineering, Civil
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