Risk-based facility management approach for building components using a discrete Markov process - predicting condition, reliability, and remaining service life

The U.S. building stock is large, diverse, and of critical importance to the economic and social well-being of the country. A proactive facility asset management approach is required to ensure these buildings support the purposes, functions, and missions for which they were built and continue to be used. For federal organizations, particularly those with large building portfolios, the goal is to deliver an acceptable level of performance while minimizing life cycle cost and risk. This research presents a framework that explicitly measures the risk and uncertainty associated with building conditions, and uses this framework to support better decisions for facility investment and resource allocation. The end result of this research provides a model for optimizing the selection and application of building work activities ranging from inspection to repair to replacement and recapitalization. Realizing the importance of physical condition in the determination of a building’s performance, a major objective of this research was to improve the statistical accuracy of building component condition prediction models by using a probabilistic approach. To do this, a discrete Markov chain model was proposed and developed. The result of this work is a robust process for developing Markov transition probabilities to model the condition degradation process using existing condition assessment data that has been acquired and continues to be collected for large portfolios of facilities. It solves the problems with data quality issues, effects from major repair interventions, and variable inspection observation times. It also provides a direct means of measuring uncertainty, reliability, and risk of component failure. Finally, it supports an unbiased process of determining expected service life for components by using the Markov chain model to compute the average number of time cycles to reach the failure state. This probabilistic Markov chain prediction model provides a foundation towards a risk-based framework for facility management decision making. A Value of Inspection Information (VOII) model was developed by combining the probability distribution from the Markov prediction model with the decision tree logic from a value of information approach to calculate the benefit of inspection at a point in time using the last inspection results and the cost of component repair, replacement, and potential failure. In addition, the Markov prediction model was also applied to the work activity selection process, where the objective is the selection of the best activity to perform against a building component such that life cycle costs are minimized yet performance constraints are still satisfied. Traditionally these constraints have been condition based, but the proposed model also allows for risk based reliability performance measures as well. Including risk more explicitly in the decision framework has the potential to change the selection optimization process. The overall framework provides a logical approach that utilizes historic data to develop a more realistic model for building component condition and reliability. The approach analyzes component re-inspection information from large building assessment datasets (multiple inspections over time for a single component), to determine how past observed conditions correlate with future observed conditions to predict future reliability and service life. This model provides a stronger correlation to future condition and reliability estimates compared to an age-based deterministic model, and helps to counteract the situations where the recorded age of a component is not representative or expected design life is unknown. This allows a facility manager to proactively manage facility requirements using real-time risk-based metrics aligned with a data-driven probabilistic process

Improving Data-Driven Infrastructure Degradation Forecast Skill with Stepwise Asset Condition Prediction Models

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) Nearest Neighbor. Model performance was evaluated against BUILDER SMS, the industry-standard asset management database, using data for five roof types on 8549 facilities across 61 U.S. military bases within the United States. The stepwise Weighted Slope model more accurately predicted asset degradation 92% of the time, as compared to the industry standard’s continuous self-correcting prediction model. These results suggest that using historical condition data, alongside or in-place of manufacturer expected service life, may increase the accuracy of degradation and failure prediction models. Additionally, as data quantity increases over time, the models presented are expected to improve prediction skills. The resulting improvements in forecasting enable decision makers to manage facility assets more proactively and achieve better returns on facility investments. © 2022 by the authors

Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs

Partially Observable Markov Decision Processes (POMDPs) provide an efficient way to model real-world sequential decision making processes. Motivated by the problem of maintenance and inspection of a group of infrastructure components with independent dynamics, this paper presents an algorithm to find the optimal policy for a multi-component budget-constrained POMDP. We first introduce a budgeted-POMDP model (b-POMDP) which enables us to find the optimal policy for a POMDP while adhering to budget constraints. Next, we prove that the value function or maximal collected reward for a b-POMDP is a concave function of the budget for the finite horizon case. Our second contribution is an algorithm to calculate the optimal policy for a multi-component budget-constrained POMDP by finding the optimal budget split among the individual component POMDPs. The optimal budget split is posed as a welfare maximization problem and the solution is computed by exploiting the concave nature of the value function. We illustrate the effectiveness of the proposed algorithm by proposing a maintenance and inspection policy for a group of real-world infrastructure components with different deterioration dynamics, inspection and maintenance costs. We show that the proposed algorithm vastly outperforms the policy currently used in practice

Improving Data-Driven Infrastructure Degradation Forecast Skill with Stepwise Asset Condition Prediction Models

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) Nearest Neighbor. Model performance was evaluated against BUILDER SMS, the industry-standard asset management database, using data for five roof types on 8549 facilities across 61 U.S. military bases within the United States. The stepwise Weighted Slope model more accurately predicted asset degradation 92% of the time, as compared to the industry standard’s continuous self-correcting prediction model. These results suggest that using historical condition data, alongside or in-place of manufacturer expected service life, may increase the accuracy of degradation and failure prediction models. Additionally, as data quantity increases over time, the models presented are expected to improve prediction skills. The resulting improvements in forecasting enable decision makers to manage facility assets more proactively and achieve better returns on facility investments

Evaluating Climatic Influences on the Technical Performance of Built Infrastructure Assets

Asset performance evaluations serve as a benchmark that can be used to make asset manufacturer selection decisions, i.e., choosing the brand of manufacturer that provides the highest performance among all brand competitors. Understanding the environmental conditions to which assets are subjected provides facility managers another data point to understand asset performance. This research builds upon a previously published performance-based manufacturer selection metric by investigating the linkage between asset performance and exposure to local climate, using chiller and air handler data from 20 United States Air Force installations. The link between environmental factors such as Heating Degree Days (HDD); Cooling Degree Days (CDD); Solar Irradiance; and a variable that accounts for relative humidity, and asset performance is investigated using analysis of variance (ANOVA) testing and correlation coefficients. The results reveal that most assets, regardless of location, (1) possess a moderate to strong performance; and (2) cumulative climate exposure and asset manufacturer selection influence asset performance. This work highlights the need for facility managers to consider the influence of climate on technical performance and use it as a decision criterion in manufacturer selection

Performance-Based Building System Manufacturer Selection Decision Framework for Integration into Total Cost of Ownership Evaluations

Facility managers are often faced with building system procurement or replacement decisions, requiring them to select a system from among competitive manufacturers. Total cost of ownership (TCO) criteria, informed by built assets in operation in the manager’s portfolio, provides some information to select the right asset manufacturer. However, managers must also consider technical performance to complete a more comprehensive analysis. Performance can be calculated using asset parameters like condition, age, and variation in condition to aid in TCO assessments. Leveraging past research and approaches, performance is calculated using an additive model that scales each parameter using a standard normalization technique and employs weighting factors to account for decision-maker input. Data from 20 Air Force installations across the US and two asset types are analyzed, showing the utility of a performance metric. This analysis shows that as manufacturer diversity in portfolios decreases, performance increases for most of the asset types modeled. This paper presents a new performance metric that can be used as an additional criterion in TCO models to build a more robust decision framework