Spatio-temporal data for asset management: a review on applications, challenges and future directions

While there is general knowledge about how transportation affects the environment in urban and rural areas, remote sensing, image processing and GIS technologies provide new opportunities for incorporating environmental factors in infrastructure asset management, such as the extent of erosion, wetland displacement and destruction, runoff discharge, loss of tree cover, and residential and business dislocation. These technologies can be used to develop data for strategic monitoring and analysis of the impacts of various civil infrastructure development alternatives on the environment. This paper discusses the combined use of remote sensing, image processing and GIS techniques for infrastructure and environmental capital asset management. The authors discuss important considerations for developing RS/GIS databases and analytic approaches, to enhance existing decision support systems, and present examples of the types of analysis that could be performed to integrate transportation infrastructure and environmental asset management. Integrated infrastructure and environmental capital asset management will address the inevitable interactions that occur between the built, natural and social environments, and aim to use the acquired knowledge for identifying better alternatives for civil infrastructure development.

Abstract. The integration of Building Information Modelling (BIM) and Geographical Information Systems (GIS) is gaining momentum in digital built Asset Management (AM), and has the potential to improve information management operations and provide advantages in process control and delivery of quality AM services, along with underlying data management benefits through entire life cycle of an asset. Work has been carried out relating GeoBIM/AM to buildings as well as infrastructure assets, where the potential financial savings are extensive. While information form BIM maybe be sufficient for building-AM; for infrastructure AM a combination of GIS and BIM is required. Scientific literature relating to this topic has been growing in recent years and has now reached a point where a systematic analysis of current and potential uses of GeoBIM in AM for Infrastructure is possible. Three specific areas form part of the analysis – a review of BIM and Infrastructure AM and GIS and Infrastructure AM leads to a better understanding of current practice. Combining the two, a review of GeoBIM and Infrastructure AM allows the benefits of, and issues relating to, GeoBIM to be clearly identified, both at technical and operational levels. A set of 54 journal articles was selected for in-depth contents analysis according to the AM function addressed and the managed asset class. The analysis enabled the identification of three categories of issues and opportunities: data management, interoperability and integration and AM process and service management. The identified knowledge gaps, in turn, underpin problem definition for the next phases of research into GeoBIM for infrastructure AM.

Many studies and technology companies highlight the actual or potential value of Digital Twins, but they often fail to demonstrate this value or how it can be realised. This gap constitutes a barrier for infrastructure asset management organisations in their attempt to innovate and incorporate digital twinning opportunities in their decision-making processes and their asset management planning activities. Asset management planning activities often make use of existing value-based decision-support tools to select and prioritise investments in physical assets. However, these tools were not originally designed to consider digital twinning investments that also compete for funding. This paper addresses this gap and proposes a value-based analysis for digital twinning opportunities in infrastructure asset management. The proposed analysis method is tested with three rail and road infrastructure case studies: (i) real-time monitoring of a power transformer; (ii) BIM for the design, construction, and maintenance of a new railway line; and (iii) infrastructure displacement monitoring using satellite data (InSAR). The study shows that the proposed method provides a conceptual construct and a common language that facilitates the communication of digital twinning opportunities in terms of their relevance in different contexts. The proposed method can be used to support the investment decision-making process for investments in both physical and non-physical assets and help derive maximum value from the limited available resources.

Asset management is data-intensive and new tools and processes are often necessary to collect, manage, analyse, and use asset data. The use of these tools can improve organisational knowledge and decision-making. Industry 4.0 tools are prompting the digital transformation of organisations and emerging innovation opportunities. Among these tools are those supporting the concept of Digital Twin (DT). The concept started being mentioned a few decades ago, but the discussion around its definition and potential applications still continues. This paper explores some of the interpretations of the concept of DT and its interrelation with some Industry 4.0 tools. Besides presenting some of the known benefits and opportunities related to DT applications in specific industries such as aerospace and manufacturing, namely in the early stage of asset lifecycle, the paper seeks to emphasise the vast exploratory potential of DT use in infrastructure asset management, especially in the operation and maintenance (O&M) phases. This presentation includes the description of an exploratory project for DT implementation in rail and road networks by the largest infrastructure management body in Portugal.

Asset Management in National and County Contexts

Asset management aims at realizing value from assets, meeting stakeholders needs and expectations by balancing financial, environmental, and social costs, risks, quality of service and performance of assets. Asset managers need to know the assets they manage, so that organizational knowledge and decision-making are improved, and the global value generated by the asset portfolio is maximized. Therefore, asset management is a data-intensive activity, and asset managers need tools and processes to efficiently collect, assemble, manage, analyze, and use asset data. In this regard, Industry 4.0-driven approaches, such as the Digital Twin (DT), promise to contribute to asset management decision-making and realize value from asset data. However, organizations that may consider DT application face different interpretations on the concept and varying expectations regarding its potential impacts. Based on the contributions of diverse information sources, this work aims to identify opportunities and challenges arising from its use and discuss them in the context of road and rail infrastructure asset management.

Digital Twins have emerged as a transformative technology with immense potential for enhancing asset data management in various industries. The transportation industry relies heavily on properly functioning and maintaining safety hardware assets, such as crash barriers, guiderails, and road signs, among others, to ensure the safety of road users. However, traditional asset data management practices for ancillary assets often fall short of providing real-time, comprehensive insights into the condition and performance of these critical assets. Conversely, Digital Twins can analyze data for predictive and preventive maintenance using cutting-edge technologies. This will enable asset managers to proactively address potential safety concerns, plan maintenance schedules efficiently, and optimize resource allocation. This can be achieved by leveraging historical asset data and real-time condition updates and providing the Digital Twin with an information-rich analytical model. The key steps to creating an information-rich analytical model are identifying the asset data that should be collected, understanding data architecture throughout the asset lifecycle, and generating asset data governance and stewardship frameworks. As such, this paper investigates the current state of practice for ancillary asset data collection and management and investigates the generation of information-rich analytical models for guiderails, a critical roadway safety hardware. Moreover, this study demonstrates how Digital Twins can be integrated and affect the development of transportation asset management systems. The findings of this article emphasize the significance of adopting Digital Twins in transportation asset data management. The case example showcases a compelling roadmap for other transportation agencies to consider integrating digital twins into their asset management strategies.

Organizations are increasingly looking to adopt the Internet of Things (IoT) to collect the data required for data‐driven decision‐making. IoT might yield many benefits for asset management organizations engaged in infrastructure asset management, yet not all organizations are equipped to handle this data. IoT data is collected, stored, and analyzed within data infrastructures and there are many changes over time, resulting in the evolution of the data infrastructure and the need to view data infrastructures as complex adaptive systems (CAS). Such data infrastructures represent information about physical reality, in this case about the underlying physical infrastructure. Physical infrastructures are often described and analyzed in literature as CASs, but their underlying data infrastructures are not yet systematically analyzed, whereas they can also be viewed as CAS. Current asset management data models tend to view the system from a static perspective, posing constraints on the extensibility of the system, and making it difficult to adopt new data sources such as IoT. The objective of the research is therefore to develop an extensible model of asset management data infrastructures which helps organizations implement data infrastructures which are capable of evolution and aids the successful adoption of IoT. Systematic literature review and an IoT case study in the infrastructure management domain are used as research methods. By adopting a CAS lens in the design, the resulting data infrastructure is extendable to deal with evolution of asset management data infrastructures in the face of new technologies and new requirements and to steadily exhibit new forms of emergent behavior. This paper concludes that asset management data infrastructures are inherently multilevel, consisting of subsystems, links, and nodes, all of which are interdependent in several ways.

The world of infrastructures for energy, transportation, water and communication is constantly changing. It is not only that the use is ever increasing, recent years have demonstrated that there is a move from the public domain to a more privatised setting. On the one side, customers become more critical and demand better quality and better service, whereas on the other side a need arises to limit the (public) costs of infrastructures. To cope with those conflicting demands many infrastructure organisations have introduced Asset Management, an integrated approach to balance costs, performance and risks. However, in discussions at the Asset Management Platform (a group of over 30 asset managers from all infrastructures in the Netherlands, established in 2007 by the Next Generation Infrastructures foundation) it appeared that many infrastructure asset managers had great difficulty in getting beyond the first promising step. As a first step in the formulation of a new research programme an interview round was conducted with a number of infrastructure asset managers to get a better feel for what the precise barriers and challenges were. In this paper the results of the interviews are presented. Key findings were that Asset management acquired a strong foothold, but that it was a bottom up process which did not reach the strategic level. There were still difficulties in convincing top management of the strategic value of asset management and in aligning organisational goals with technical and operational standards. Furthermore, it was recognized widely that asset management required a change in maintenance paradigms and that asset management should focus on life cycle costing. Nevertheless, asset management was generally considered to be an engineering discipline, even though lots of efforts were spent on creating support for the initiative. Some asset managers explicitly recognized the social and cultural issues and acted on this by training their asset managers in social skills like empathy and persuasiveness, however this was not common. Neither was the extensive use of asset performance models for decision support, as most asset managers preferred to rely on historical data. However, at the same moment, many asset managers reported problems in accessing those very historical data. Based on these results, a research programme was defined in which academics as well as practitioners will participate. The outlines of this programme will be introduced in the paper.

Traditional infrastructure asset management is about maintaining the status quo of service levels in a resource-restricted, sometimes risk-increasing environment. Infrastructure asset management (IAM) is effective in addressing resource-deprived situations and in maximizing the benefits of the utility in these contexts. This makes IAM a very appropriate and useful approach for developing countries. Hence, this paper focuses on developing a fit-for-purpose integrated asset management (IAM) framework that is suitable for situations where there are risks to assets, significant uncertainties, and resource deficits, and where improvements to the current service levels are needed. To be comprehensive in the application in these contexts, there is a need to supplement IAM with a new perspective—critical necessities, next to the risks to the status quo (current levels of service). This gap was evident during application of IAM principles to the drinking water system of Al-Mafraq, Jordan. It was overcome by framing questions on adaptation deficits and future needs that are to be answered together with risk matrix-based prioritization of asset management actions. The fit-for-purpose IAM framework comprising asset management, adaptation deficit, and future needs can ensure the continuity of service levels in emerging cities when supported through expert inputs and stakeholder consultations.

Organisations owning and managing infrastructure asset are constantly striving to obtain the greatest lifetime value from their infrastructure assets. Many such organisations have adopted the concept of “asset management” with the aim of improving the performance of their infrastructure assets. This paper evaluates the adoption of asset management to improve performance in the context of organisations managing infrastructure assets. Relevant previous research studies on main barriers to the adoption of asset management are reviewed. Analysis of these findings, together with deductive reasoning, leads to the development of the proposed improvement strategies. Three issues were identified as barrier to the advancement of the concept of asset management. They are (1) lack of recognition (2) fragmentation; and (3) growing complexity. To overcome these issues, this paper suggests that the organisations manage infrastructure assets must (1) adopt a more strategic approach in the management of infrastructure assets (2) develop a framework of strategic infrastructure asset management processes, and (3) identify the core capabilities needed in the management of infrastructure assets. This paper presents the direction for further research to advance the concept of asset management in the management of infrastructure asset.

Risk assessment for asset management is a process to analyse the potential risk that could affect the efficiency of operating assets. An effective risk assessment strategy depends on high-quality data and effective data analysis. To do so, risk assessment should take into consideration the spatial and temporal aspects of risks of physical assets in asset management for a data-driven decision-making. Failure to incorporate the spatial and temporal aspects in risk, when the risk possess spatio-temporal correlation, could lead to a defective estimation of assets risks. GIS can be used to analyse multidimensional information which includes 3D GIS, and spatio-temporal data (2D+t and 3D+t). Due to its superiority in handling multidimensional data, GIS is often a preferable tool to collect, manage, manipulate, and visualize spatio-temporal data to allow an accurate and informed analysis. The aim of this study is to uncover the use of GIS for risk assessment in asset management when spatial and temporal data are concerned. This study conduct a thorough review of previous research to look into the current development of asset management that utilizes a spatio-temporal approach for data collection, analysis, and 3D visualization for risk assessment. At the end of this study, we found that physical assets are affected by risk that possess spatio-temporal correlation. Several GIS techniques for data collection, analysis and visualization are found to provide a more effective analysis for assessing spatio-temporal risks. This paper concludes by providing a workflow that can be used by asset managers to assess risk spatio-temporally based on the findings of this study.

Strategy on sustainable infrastructure asset management: Focus on Korea׳s future policy directivity

Mitigating infrastructure disaster losses through asset management practices in the Middle East and North Africa region

The management of infrastructure assets is often challenged by uncertain factors such as changing weather trends and usage patterns. To reliably maintain the assets above a specified level of service, the extent of this uncertainty should be identified, and adequate methods for analyzing the risk factors should be implemented. The analysis of risk is well-defined in the field of strategic management. In the current study, four levels of uncertainty that are widely discussed in strategic management were used as a benchmark to determine the levels of uncertainty in infrastructure asset management. These levels range from known issues, to statistically predictable factors, to complete indeterminacy. The current methods of treating uncertainty in infrastructure asset management were reviewed to determine how they overlap with the four levels of uncertainty and whether or not asset managers handle this uncertainty in an optimal fashion. The current approaches in asset management were found to be primarily deterministic and probabilistic. A shortcoming was found in regard to the incorporation of risk analysis into asset management planning for large-scale networks. The researchers concluded that the level of uncertainty present in asset management at the network planning level can be best described as a range and addressed through the use of representative scenarios within that range.