Asset Management Plan Research Articles

Public-school infrastructure ageing and current challenges in maintenance

PurposeSchool infrastructure is one of critical factors that significantly contribute to the educational outcomes, and therefore, maintaining the high quality of school infrastructure becomes of critical importance. Due to the ageing of school assets over time in combination with budget constraint and rapid growth of student enrolment, many public schools are currently struggling to maintain the required standard for long term. However, to date, the goal of providing the best maintenance practices to public schools has not been achieved.Design/methodology/approachThe present study focuses on studying the balance between the asset and maintenance management strategies and the funding model through conducting state-of-the-art literature review and qualitative analysis in the context of public schools in Australia and other developed countries around the world. Review of journal articles, different government reports and other available resources were used to collect and analyse the data in this study.FindingsAs part of this review, significant under investment in maintenance and asset renewals were identified as main challenges in asset management in public school facilities. Although different maintenance strategies were used in school infrastructure, adequate funding, adequate robust asset management plans (AMPs) and the involvement of private sectors have been identified as the key factors that govern the success in school infrastructure maintenance. It also shows that funding of approximately 2–3% of asset replacement value (ARV) on school infrastructure is required to maintain school facilities for long-term. Further, the procurement methods such as public private partnership including private finance initiatives (PFIs) have shown great improvements in maintenance process in school infrastructure.Originality/valueThe study provides a review of different AMPs and funding models in school infrastructure and their efficiencies and shortcoming in detail. Different states and countries use different maintenance models, and challenges associated with each model were also discussed. Further this study also provides some conclusive evidence for better maintenance performance for school buildings.

Sustainability assessment of strategic asset management decisions on municipal water infrastructure systems: Framework and application

AbstractDeveloping socially acceptable, environmentally friendly, and financially viable asset management plans (AMPs) is a major challenge for water and wastewater utilities and calls for a comprehensive Sustainability Assessment (SA). This paper proposes a novel framework that applies System Dynamics and Life Cycle Assessment tools to evaluate the long‐term sustainability of strategic asset management decisions for water and wastewater infrastructure systems. The merit of the proposed framework in dealing with the non‐linearity and dynamic behavior of the socio‐economic systems and its utility for SA of strategic asset management decisions are demonstrated in a case study of a Wastewater Collection (WWC) network system. Furthermore, the significant importance of population growth and urban densification policies in sustainability of AMP are highlighted by comparing four alternative scenarios of (1) 100% urban densification, (2) 0% urban densification, (3) 50% densification, and (4) no population growth in the case study. The results of the case study show that the greenhouse gas emission, operational and capital expenses, and bill burden of WWC and treatment services significantly reduce with urban densification.

Aligning financial and technical procedures for the determination of urban drainage assets’ current and replacement values

Abstract The paper presents a method for aligning the relevant financial and technical procedures for determining drainage assets’ current and replacement values. This alignment is especially pertinent when actual construction costs are unavailable and records in different utility departments (technical and accounting) do not correspond. The current asset value is grounded in estimated construction costs, considering accounting and technical useful lives. Asset portfolio considers the assets providing adequate service quality, regardless of their age. The methodology relies on the update of the assets’ registry (updating the assets’ value), the increase of the accounting useful life in line with technical practice (reducing annual depreciation), and the accumulated depreciation reversion (increasing net current value). In the case study, the need to update information exchange and align technical and financial procedures in Lisbon Municipality was triggered by internal policy requirements concerning the simultaneous development of the urban drainage asset management plan and the requirement to standardize the accounting system. The application of the methodology led to an increase of approximately five times the assets’ current value. The results, their implications, and replicability opportunities are discussed.

Concurrent 18. Presentation for: Minimising pipeline leaks and maximising operational life by application of machine learning at Cooper Basin

Presented on Wednesday 18 May: Session 18 The development of technologies in the last few decades has enabled operators to collect significantly more data than previously possible. Despite availability, making data-driven decisions on asset health, and developing efficient asset management strategies, is not common. This is mainly due to challenges with compilation, and alignment of all the data into a comprehensive picture of pipeline integrity, as it consumes significant resources deploying conventional methods. A critical advantage of modern data storage, analysis and visualisation techniques is the relative ease of performing statistical assessments of integrity data. Analysis of correlated data can be equally challenging as algorithms used can be overly simplistic and inaccurate. Machine learning algorithms parse, analyse and learn from data, enabling the operators to make an educated decision. This has been extensively deployed in other industries such as finance, healthcare and supply chain management but has never been fully developed and enhanced in pipeline integrity industry until very recently. This paper provides an overview of the development in machine learning tools in pipeline integrity, allowing enhancement of asset performance, through the application of machine learning and automation, to predict integrity threats, and prevent leaks and failures. It provides a case study where a tool was developed, and this technique was successfully implemented across a significant number of upstream pipelines in the Cooper Basin, enabling the Santos integrity engineering team to make the most effective decisions on asset condition and to develop a data-driven asset management plan. To access the presentation click the link on the right. To read the full paper click here

Application of Machine Learning Methods for Asset Management on Power Distribution Networks

This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. Also, it investigates the challenges behind asset management and its maintenance activities. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work. Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PDF

Managing stressors, stress and strain faced by facility managers

PurposeThis study aims to investigate how to manage the stressors that facility managers (FMs) face with the aim of reducing their stress and strain levels.Design/methodology/approachUsing the job demands-resources (JD-R) model, stressors are operationalized from job demands, job resources and personal resources constructs. A structured questionnaire was used to collect data from FMs based in Singapore.FindingsFMs have significantly high levels of stress, but they are able to manage this well. Job demands that cause stress and strain are those relating to insufficient time to complete the work and difficulties in handling the work. The lack of job resources in terms of lack of organizational support and inadequate stress mitigation programs are associated with high levels of stress and strain.Research limitations/implicationsThe limitations are the relatively small sample size and absence of a database of FMs in Singapore, and correlation is not causation when determining the association between stressors and stress and strain.Practical implicationsBased on the recommendations, employers and FMs may manage the specific stressors identified so that FMs’ stress and strain levels are under control to enable them to work optimally.Originality/valueThis research discovered that the JD-R model is moderated by certain features of facilities management and FMs. When these features are present, FMs are predisposed to more stressors, and higher stress and strain. For the facilities management profession, the discovery is that there are significantly more job demands: for in-house FMs compared to those working for outsourced firms; for FMs who need to carry out estate and asset management, landlord activities and facility planning compared to FMs who focus on operations and maintenance; and for FMs who work longer than five days compared to those who work a five-day week.

Creating an asset management plan for traffic signal structures through interactive explorations of wind induced fatigue damage

This paper presents an asset management framework for remote monitoring of traffic signal structures for fatigue damage under wind loads, using historical wind measurements from nearby weather stations. Estimate wind profiles developed from the wind data are used to calculate fatigue damage at welded connections for a wide variety of common traffic pole configurations. The analysis is embedded within a novel remote monitoring application that performs the fatigue life analysis in real time for a user defined location and traffic pole configuration. Such a framework is the first of its kind that allows operators to make real time asset management decisions though interactive explorations of the relationship between traffic pole configuration and fatigue life due to local wind conditions. Results are demonstrated using several traffic poles located around Logan, Utah, which has complex topography that creates drastic changes in wind conditions over short geographical distances. The results illustrate the ability of the remote monitoring application to detect small-scale differences in wind conditions that result in significant differences in the estimated fatigue life of traffic structures within communities. Such results highlight the need for, and feasibility of developing site-specific asset management frameworks at a much finer spatial resolution than previous attempts.

Minimising pipeline leaks and maximising operational life by application of machine learning at Cooper Basin

The development of technologies in the last few decades has enabled operators to collect significantly more data than previously possible. Despite availability, making data-driven decisions on asset health, and developing efficient asset management strategies, is not common. This is mainly due to challenges with compilation, and alignment of all the data into a comprehensive picture of pipeline integrity, as it consumes significant resources deploying conventional methods. A critical advantage of modern data storage, analysis and visualisation techniques is the relative ease of performing statistical assessments of integrity data. Analysis of correlated data can be equally challenging as algorithms used can be overly simplistic and inaccurate. Machine learning algorithms parse, analyse and learn from data, enabling the operators to make an educated decision. This has been extensively deployed in other industries such as finance, healthcare and supply chain management but has never been fully developed and enhanced in pipeline integrity industry until very recently. This paper provides an overview of the development in machine learning tools in pipeline integrity, allowing enhancement of asset performance, through the application of machine learning and automation, to predict integrity threats, and prevent leaks and failures. It provides a case study where a tool was developed, and this technique was successfully implemented across a significant number of upstream pipelines in the Cooper Basin, enabling the Santos integrity engineering team to make the most effective decisions on asset condition and to develop a data-driven asset management plan.

BIM-integrated portfolio-based strategic asset data quality management

A building's strategic asset management (SAM) capability has traditionally been limited by its site-based management. With the emergence of needs from clients about delivering a long-term portfolio-based building asset management plan that minimizes the asset risk and optimizes the value of their asset portfolios, SAM Units have emerged as a new business form to provide various SAM services to their clients. However, the quality of their current data model is still hindered by many issues, such as missing important attributes and the lack of customized information flow guidance. In addition, there is a gap in integrating their existing data collection with various data sources and Building Information Modeling (BIM) to enhance their data quality. By evaluating a SAM Unit's portfolio case study, this paper identifies the factors limiting the quality of SAM Units' data model and develops a guide to integrating various data sources better. We develop a BIM-integrated portfolio-based SAM information flow framework and a detailed hierarchical portfolio-based non-geometric data structure. The proposed framework and data structure will help SAM professionals, building asset owners, and other facilities management professionals embrace the benefits of managing the portfolio-based SAM data.

A comprehensive framework to efficiently plan short and long-term investments in water supply and sewer networks

Water supply and sewer networks are critical infrastructures that provide a basic service to society. However, these systems constantly age and degrade over time. In addition, since network infrastructures are so extensive in length, they require a significant investment in maintenance tasks. Hence, within the context of infrastructure asset management (IAM), accurately defining the most efficient investment planning possible is essential to ensure their long-term sustainability. This paper presents an original five-step comprehensive framework to successfully implement an infrastructure asset management strategy and plan long-term investments. Moreover, this methodology integrates innovative and relevant operational and convenience factors that, while provide the problem both with realism and practicality, have not been addressed so far. To illustrate the usefulness and applicability of this methodology, the case study of a large water company in Spain is presented.

Implementation Of Road Asset Management In Poland

Road asset management (RAM) is a crucial aspect in the development of transportation network. RAM framework should be understood as a rational approach to business model for road authority, which dictates its business processes in a systematic and objective-based manner to ensure that strategic goals of an agency are reached. In a case of road network, such objectives are typically related to travel safety, time and comfort that should be maintained throughout the life cycle of the road assets at optimal costs. As such RAM enables road agencies to meet expectations of its customers to provide safe and reliable road network in effective and efficient way. Nowadays it becomes even more important to adopt performance-based and data-driven approach for road agencies considering current trends in transportation like connected, cooperative and automated mobility.There are many aspects of RAM including legal, economic, technical and managing aspects. These aspects are inter-connected and nowadays agencies build and incorporate RAM system to establish a common denominator for all agency activities at all organizational levels. This paper discusses selected aspects of RAM in Poland. Since national road network in Poland is relatively new, it is particularly important to go over well designed and detailed implementation process. It should comprise numerous items including self-assessment and gap analysis, change strategy and transportation asset management plan (TAMP). This paper present update on these activities as well as presents future plans in order to establish modern, effective and sustainable RAM framework in Poland.

Transportation infrastructure asset management modeling using Markov decision process under epistemic uncertainties

Purpose Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure type, the framework incorporates an optimization model for optimizing maintenance, repair, rehabilitation (MR&R) and replacement decisions in a finite planning horizon. Design/methodology/approach The analytical framework is further developed through a series of model variations, scenario and sensitivity analysis, simulation processes and numerical experiments to show the impacts of various parameters/factors and draw managerial insights. One notable analysis is to explicitly model the epistemic uncertainties of infrastructure deterioration models, which have been overlooked in previous research. The proposed methodology can be adapted to different types of assets for solving general asset management and capital planning problems. Findings The experiments and case studies revealed several findings. First, the authors showed the importance of the deterioration model parameter (i.e. Markov transition probability). Inaccurate information of p will lead to suboptimal solutions and results in excessive total cost. Second, both agency cost and user cost of a single facility will have significant impacts on the system cost and correlation between them also influences the system cost. Third, the optimal budget can be found and the system cost is tolerant to budge variations within a certain range. Four, the model minimizes the total cost by optimizing the allocation of funds to bridges weighing the trade-off between user and agency costs. Originality/value On the path forward to develop the next generation of bridge management systems methodologies, the authors make an exploration of incorporating the epistemic uncertainties of the stochastic deterioration models into bridge MR&R capital planning and decision-making. The authors propose an optimization approach that does not only incorporate the inherent stochasticity of bridge deterioration but also considers the epistemic uncertainties and variances of the model parameters of Markovian transition probabilities due to data errors or modeling processes.

Optimizing the Life Cycle of Physical Assets through an Integrated Life Cycle Assessment Method

The purpose of this study was to apply new methods of econometric models to the Life Cycle Assessment (LCA) of physical assets, by integrating investments such as maintenance, technology, sustainability, and technological upgrades, and to propose a means to evaluate the Life Cycle Investment (LCI), with emphasis on sustainability. Sustainability is a recurrent theme of existing studies and will be a concern in coming decades. As a result, equipment with a smaller environmental footprint is being continually developed. This paper presents a method to evaluate asset depreciation with an emphasis on the maintenance investment, technology depreciation, sustainability depreciation, and technological upgrade investment. To demonstrate the value added of the proposed model, it was compared with existing models that do not take the previously mentioned aspects into consideration. The econometric model is consistent with asset life cycle plans as part of the Strategic Asset Management Plan of the Asset Management System. It is clearly demonstrated that the proposed approach is new and the results are conclusive, as demonstrated by the presented models and their results. This research aims to introduce new methods that integrate the factors of technology upgrades and sustainability for the evaluation of assets’ LCA and replacement time. Despite the increase in investment in technology upgrades and sustainability, the results of the Integrated Life Cycle Assessment First Method (ILCAM1), which represents an improved approach for the analyzed data, show that the asset life is extended, thus increasing sustainability and promoting the circular economy. By comparison, the Integrated Life Cycle Investment Assessment Method (ILCIAM) shows improved results due to the investment in technology upgrades and sustainability. Therefore, this study presents an integrated approach that may offer a valid tool for decision makers.

Forecasting Federal Transportation Performance Management Bridge Condition Measures for Bridge Management

Many common processes of bridge management can benefit from network-level analysis of long-term costs and condition, on a time frame of about 10 years. Such processes include development and implementation of Transportation Asset Management Plans, long-range needs analysis, capital budgeting and programming, and policy analysis. The ability to forecast federal Transportation Performance Management (TPM) condition measures would provide managers with a way of evaluating the possible outcomes of funding, programming, and policy decisions. A model for this purpose has been developed as a part of StruPlan, an open-source spreadsheet for long-range renewal planning for transportation structures. Element condition state data are found to be highly exponential in distribution, while the federal measures “Percent Good” and “Percent Poor” are categorical when applied to specific bridges. Element data, providing more detail about the type, severity, and extent of defects, are valuable for deterioration modeling, while the TPM measures are simpler for reporting to stakeholders. A set of models was developed to bridge the gap between these measures. Thus far, the models have been calibrated and pilot tested using Idaho, South Dakota, and Kentucky data. The model is a novel approach that has not been attempted elsewhere, that may simplify important parts of bridge management and provide some valuable new ideas for researchers and developers.

Bridge-Level Optimization Module for Planning and Programming

The paper briefly introduces an element-based multi-objective optimization (EB-MOO) methodology to support state departments of transportation with their decision-making process, asset management, and performance-based transportation planning and programming. The methodology focuses on the bridge asset class and consists of five modules: (i) data processing, (ii) improvement, (iii) element-level optimization (ELO), (iv) bridge-level optimization (BLO), and (v) network-level optimization (NLO) modules. These five modules jointly produce short- and long-term intervention strategies detailed at the bridge element level for planning and programming. The paper focuses on the BLO module, specifically: the basic framework of underlying processes and concepts, the optimization problem types and mathematical formulations, and the heuristic algorithm to solve the BLO problems. A prototyping tool is developed to implement these five modules of the EB-MOO methodology, test concepts, prove effectiveness, and demonstrate potential benefits. The paper also includes an illustrative example using the prototyping tool. The example consists of the BLO problems under different budget and/or performance scenarios. The implementation proves the module’s capability in producing a diverse set of Pareto optimal or near-optimal solutions, recommending set of element intervention actions and timings, predicting performance, and determining budget requirements for the entire program period. The BLO results associated with the recommended solutions serve as the fundamental inputs for the NLO module. Nevertheless, the BLO module can be used independently, providing a systematic process for the development of bridge improvement/preservation programs detailed at the element level.

Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming

The U.S. Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and the use of a performance-based approach in transportation planning and programming. This paper introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to improve bridge management and support state departments of transportation with their transition efforts to comply with these MAP-21 requirements. The methodology focuses on the bridge asset class and is structured around five modules: data processing, improvement, element-level optimization, bridge-level optimization, and network-level optimization modules. It relies on a leading-edge forecasting model, three separate screening processes (i.e., the element deficiency, alternative feasibility, and solution superiority screening processes) to overcome computer memory and processing time limitations, and a simulation arrangement to generate life-cycle alternatives (series of improvement actions). Additionally, the EB-MOO methodology consists of three levels of optimization assessment based on the Pareto optimality concept: element-level, bridge-level, and network-level (following either a top-down or bottom-up approach). A robust metaheuristic genetic algorithm handles the different multi-objective optimization problems. A prototyping tool was developed for the implementation of the methodology through several examples of unconstrained and constrained (by budget, performance, or both) scenarios. Results reveal the capability of the methodology to generate Pareto optimal or near-optimal solutions, predict performance, and determine funding requirements and short- and long-term intervention strategies detailed at the bridge-element level for planning and programming. The EB-MOO methodology can also be expanded to accommodate other asset classes or modes.

A level of service framework for service-centric asset management planning

PurposeThe purpose is to develop and implement a systematic approach to define the level of service (LoS) consistently and efficiently. The framework will assist professionals to make decisions based on service performance and transform to a service-centric decision-making paradigm in the domain of asset management.Design/methodology/approachA three-step approach is used to develop the framework. A comprehensive literature review is conducted (Step 1), which is followed by a discussion with experts (Step 2). Based on the desktop review and field discovery, the framework is developed and implemented (Step 3).FindingsThe framework represents key components in four phases. The first phase identifies the potential service customers. The second phase defines the customer values and LoS measures at three levels: organisation, customer and technical. Implementation is done in the third phase, whereas monitoring and evaluation as part of continuous improvement are accomplished in the fourth phase.Research limitations/implicationsThe challenges of this research work are categorised as follows. Strategic challenges focus on the awareness and alignment of strategic goals. Informational and financial challenges discuss non-availability of related information and cost-effectiveness of data collection, respectively. Human challenges discuss the availability of adequate human resources with an adequate skill set. Others discuss the non-availability of defined processes and procedures.Practical implicationsTheoretically, the framework represents key performance management concepts to develop an integrated approach to define the acceptable LoS effectively. Practically, professionals can use the framework to define the LoS consistently and efficiently.Originality/valueExisting guides and frameworks represent a set of performance management concepts; however, these do not specify an integrated approach to define, align and integrate the LoS or performance goals. The proposed framework is developed to fill this gap.

Assessing asset management competency with focus on levels of service and climate change

This paper aims at exploring the practice of municipal asset management (AM) planning in Ontario, Canada, and discovering the needs of municipalities. The study is based on conducting a survey of 58 municipalities, studying their AM plans and interviewing municipalities and experts. The findings show that the state of awareness and practice of AM in Ontario has progressed well. Almost all municipalities in Ontario are involved or working on AM systems, and some have reached advanced levels of AM practices. However, several issues persist. Capacity building is at the core of the gaps. There is a need for training professionals on AM concepts and tools. Also, providing guidelines and support for change management in decision-making practices is needed. Smaller municipalities are still facing issues defining the levels of service and linking them to decision making. The next need for AM in Ontario is to link it to climate change strategies and programmes. While some of the municipalities are aware of climate change, they mostly have taken no practical steps regarding adaptation or mitigation. Among key challenges to the success of the Ontario AM strategy is the management of data. There is inconsistency in the specifications for data and limited quality assessment or interoperability guidelines.

A Risk Management Database Framework Implementation for Transportation Asset Management

A 2017 survey of the state of practice on how agencies are developing their risk-based asset management plan shows that state highway agencies are increasingly adapting the way they do business to include explicit considerations of risks. At the moment, this consideration of risk is not linked to data. Hence, there is a lack of integration of risk management in driving strategic cross-asset programming and decision-making. This paper proposes and implements a risk management database framework as the missing piece in the full implementation of a risk-based transportation asset management program. This risk management database framework utilizes Geographic Information Systems (GIS) and Application Programming Interface (API) to implement a risk management database of all the relevant variables an agency needs for risk modeling to improve risk monitoring, risk register updates, and decision-making. This approach allows the use of existing enterprise as well as legacy data collection systems, which eliminates the need for any capital-intensive implementation cost. Furthermore, it provides transportation agencies with the ability to track risk in quantitative terms, a framework for prioritizing risk, and the development of an actionable plan for risk mitigation. In this paper, the implementation of the fully integrated GIS-enabled risk management database employs the Iowa department of transportation (DOT) data and risk register.

Asset life span in a government funded CPAP device program

To determine the life span of devices in a government long-term continuous positive airway pressure (CPAP) device loan program.Retrospective review of CPAP devices provided under the Queensland Health Sleep Disorders program in Queensland, Australia, from data recorded in an in-house database that has collected data since 1995. Primary outcomes were hours of use and age of device at end-of-life. Device survival analysis was performed collectively for all devices and for different models. Reasons for device end-of-life were compared between models.There were 9,222 CPAP devices provided on long-term loan over this period, with asset end-of-life date available in 90%. Median life span was 15,178 hours (interquartile range 8,167-20,296 hours) and 12.4 (interquartile range 7.6-18.8) years. Five percent of devices were condemned in the first 3 years, and 13% of devices were condemned in the first 5 years. There were significant differences in survival patterns between different models, but after correction for run hours, only one model differed (related to manufacturer policy to replace not repair equipment). Reasons for end-of-life differed between models (P < .001) with manufacturer recall, excessive noise and faulty buttons the most common reasons.Government CPAP loan programs can develop asset management plans with an anticipated average asset life span of 15,178 hours or 12.4 years; however, they should also plan for the need to replace equipment where earlier failure occurs. Early equipment failures are seen with variability between models, and appropriate warranty periods to cover these early failures should be negotiated with manufacturers.