Criticality-Based Management of Facility Assets

Abstract

Effective facility asset management requires specific skills and tools to optimize the use of limited resources, making a decision support system essential. This research introduces a comprehensive decision support system, which is a framework organized into three models: the criticality model, the rehabilitation model, and the optimum criticality model to manage the rehabilitation of facility assets. The criticality model utilizes the Analytical Hierarchy Process (AHP) to assess the group of assets. Emphasizing criticality as a central management factor, this model lays the foundation for subsequent decision-making. The rehabilitation model employs an Artificial Neural Network (ANN), integrating Customer Level of Service (CLoS), Technical Level of Service (TLoS), and asset criticality to determine appropriate rehabilitation actions. NeuralTools 7.5 is leveraged for precise predictions of rehabilitation strategies tailored to specific assets. The third model, optimum criticality, focuses on prioritizing rehabilitation activities within the constraints of limited budgets. Lingo 20.0 is utilized to optimize rehabilitation activities, considering budget limitations and other constraints, offering a strategic approach to maximize the impact of available resources. This integrated framework provides decision-makers with a systematic and data-driven approach to facility management, enhancing the efficiency and effectiveness of rehabilitation actions. An academic building was chosen as a hypothetical example to implement the three models and suggest the essential considerations for managing both the academic building itself and other infrastructure assets. The results obtained demonstrate that the principles and methodologies encapsulated in this project can be extrapolated and scaled up for application to large-scale infrastructure assets, ensuring the sustenance of the requisite level of service and the management of acceptable risk on a broader scale.

Analyzing Contingency Estimation for Residential Turnkey Projects in Saudi Arabia: A Neural Network Approach

“…Artificial Neural Networks (ANNs) are increasingly utilized in the construction industry, capitalizing on their capabilities in pattern recognition, predictive modeling, and decision-making support. Numerous researchers have employed ANNs to estimate project costs and facilitate budget allocation [15][16][17][18][19][20][21][22][23], as well as to enhance project scheduling [24][25][26][27][28]. The process of designing, training, and using Artificial Neural Networks (ANNs) involves several key stages, each contributing to the network's ability to perform tasks such as classification, regression, prediction, and more.…”