Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins
Abstract
:1. Introduction
1.1. Background
1.1.1. Sustainability and Resilience of Rail and Road Networks
- Robustness: the ability of assets or asset systems to withstand a given level of stress/demand without suffering degradation or loss of function;
- Redundancy: the extent to which assets or asset systems are substitutable, capable of meeting functional needs in case of disruption, degradation, or loss of function;
- Resourcefulness: the ability to identify problems, establish priorities, and mobilise material (i.e., monetary, physical, technological, and informational) and human resources during asset recovery, to meet established priorities and achieve goals;
- Rapidity: the capacity to meet priorities and achieve goals in a timely manner to contain losses, maximise functionality recovery, and avoid future disruptions.
1.1.2. Digital Twin Concept
“A DT is an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin. (…). In addition to the backbone of high-fidelity physical models, the DT integrates sensor data from the vehicle’s on-board integrated vehicle health management (IVHM) system, maintenance history and all available historical/fleet data obtained using data mining and text mining. (…) the digital twin continuously forecasts the health of the vehicle/system, the remaining useful life and the probability of mission success. The systems on board the DT are also capable of mitigating damage or degradation by recommending changes in mission profile to increase both the life span and the probability of mission success.”
1.2. Significance of the Study and Motivation
1.3. Terms and Definitions
- Each DT serves a specific purpose in a given context, thus allowing the definition of the resources required to support it and to assess the benefits and value derived from it;
- A DT includes a digital representation of the physical asset or asset system and its context (the complexity and accuracy of the digital representation should suit the available resources and the DT purpose);
- Because real-time data alone do not add value to the decision-making process, the DT should have some form of data analytics (Artificial Intelligence, Big Data, etc.) to generate insights for the user (or the twin itself) and to support the asset management decision-making process. As stated by Shafto et al. [34], other information sources such as physical models and available records can be integrated into the DT. The DT might incorporate predictive or simulation capabilities, depending on the purpose of the DT;
- DTs might have different integration scales, from single asset or component level to asset system or network level [37]. Higher levels of asset aggregation in the DT imply higher potential benefits but also higher complexity (data security, interoperability, etc.);
- DTs might have different levels of development and complexity, but always include some sort of automated data transfer—i.e., take the form of “digital shadows” or “digital twins” according to the classification proposed by Kritzinger et al. [45]—at least from the physical asset to the digital asset. The data refresh rate needs to be adequate for the purpose.
1.4. Research Objectives and Paper Organization
- Identify knowledge gaps and research opportunities;
- Perceive how DT can impact the resilience and sustainability of rail and road infrastructures.
2. Methodology
2.1. Rationale
2.2. Protocol and Registration
2.3. Eligibility Criteria
2.4. Information Sources
2.5. Search
2.6. Study Selection
2.6.1. First Phase (“Screening”)
- articles without developments or contributions within the rail and road network scope (see the asset scope presented in Section 2.5);
- all manufacturing-focused papers, due to the reasons discussed in Section 2.5;
- all articles without available abstracts.
2.6.2. Second Phase (“Eligibility”)
2.6.3. Third Phase (“Snowballing”)
2.7. Data Collection Process
2.8. Data Items
2.9. Risk of Bias
3. Bibliometric Analysis Results
3.1. Annual Publications
3.2. Countries
3.3. Subject Areas
3.4. Paper Types and Networks
3.5. Lifecycle Delivery
4. Discussion
4.1. The Impact of DT on the Resilience of Rail and Road Networks
4.2. The Impact of DT on the Sustainability of Rail and Road Networks
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Domain | Operators and Keywords Used |
---|---|
Article elements | TITLE/KEY |
Digital Twin 1 | (“digital twin*” OR “as-is BIM” OR “virtual twin” OR “cyber*physical system*” OR “digital representation” OR “virtual representation” OR “digital counterpart” OR “digital replica”) |
Operator | AND |
Rail and road networks | (“rail*” OR “road*” OR “transport*” OR “asset management” OR “infrastructure” OR “track” OR “drainage” OR “culvert” OR “platform” OR “bridge” OR “tunnel” OR “overpass” OR “underpass” OR “retaining wall” OR “level crossing” OR “superstructure” OR “switches and crossings” OR “turnout” OR “access way” OR “signalling” OR “telecommunication” OR “electrical plant” OR “electric power” OR “*station” OR “catenary” OR “pavement” OR “highway” OR “traffic sign” OR “lighting” OR “toll” OR “building” OR “embankment” OR “escape ramp” OR “runaway*ramp” OR “automatic train protection”) |
Operator | AND NOT |
Exclusions | (“manufactur*”) |
Operator | AND |
Limitations of scope | (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-TO (PUBSTAGE, “final”)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) |
Sector | Infrastructure | No. of Papers | % |
---|---|---|---|
Buildings | Building | 48 | 44 |
Transportation | Railway | 16 | 15 |
Bridge | 15 | 14 | |
Roadway | 10 | 9 | |
Tunnel | 9 | 8 | |
General | General | 6 | 6 |
Energy | Electricity | 3 | 3 |
Telecommunication | Telecommunication | 1 | 1 |
Total | 108 | 100 |
Ref. | Resilience Properties | Network | Observations | |||
---|---|---|---|---|---|---|
Robus. | Redun. | Resou. | Rapid. | |||
[17] | X | Road | Continuous monitoring of road infrastructure conditions provides for early warning and indication of potential distress, enabling early remedial action. | |||
[26] | X | X | X | X | General | Data collection through DTs helps to increase the efficiency, sustainability, and resilience of CISs, under normal conditions or following extreme events. Cybersecurity is an important and complex issue, especially in interconnected DTs. |
[38] | X | X | Rail/Road | DT timely returns maintenance information, provides inputs for mitigation plans, analyses the impact of extreme loads on bridge performance, and issues early warnings. | ||
[39] | X | X | - | DT can share information and focus on condition monitoring, asset performance management, or predictive maintenance. | ||
[43] | X | X | Rail | The DT-EA allows monitoring the system state, running application diagnostics, and simulating and predicting various operational and failure scenarios. | ||
[52] | X | X | Rail | The solution provides cyclic data for analysis and verification of the turnout’s condition. The solution might capture emergency conditions. | ||
[55] | X | Road | The visual information service of the DT provides reliable data for preventive maintenance, which improves the efficiency of prediction and decision-making. | |||
[56] | X | X | Rail | The sensor data and the DT simulations and predictions can capture the early fault, support track maintenance and deliver safe, reliable, and resilient service. DTs collaboration suffers from single failure due to attack and connection in a centralised manner, data interoperability, authentication, and scalability. | ||
[61] | X | X | X | Road | The decision analysis method can help O&M managers to quickly analyse the fault cause and identify maintenance measures for the tunnel jet fans. | |
[73] | X | X | Road | The DT continuously monitors the assets to support proactive maintenance and ensure mechanical stability, safety, economy, and environmental requirements. | ||
[87] | X | X | Road | Real-time condition analysis and life prediction of NBTs enables timely asset replacement and resource procurement, increasing maintenance sustainability. | ||
[90] | X | Electricity | Remote inspection of substation power switches avoids unnecessary operator travel and allows for quicker and cheaper reestablishment. | |||
[91] | X | Electricity | An autonomous system performs diagnostic on power lines. The timely elimination of defects reduces failures and improves the reliability of the power supply. | |||
[92] | X | X | Rail | The constant infrastructure monitoring improves the control of the HVAC system and the energy efficiency, while guaranteeing the comfort requirements. | ||
[93] | X | X | Electricity | The online measured data are used in the analysis of power cable displacement and may be applied for maximising power cable capacity. | ||
[94] | X | X | Rail | Condition is simulated with a bridge DT, which identifies structural damage before it becomes critical, enabling preventive actions and cost minimisation. | ||
[95] | X | X | Road | The hyper-connected pavement environment allows for a continuous understanding of infrastructure conditions, leading to timely decision making. | ||
[96] | X | X | - | IoT and a common data environment can reduce costs, improve maintenance productivity, and enhance the accuracy and quality of information. | ||
[97] | X | X | X | Telecomm. | A network of DT avoids negative consequences when sharp increases occur in traffic, especially in emergency and destructive events. | |
[98] | X | Rail/Road | The VT/IM environment provides interactive accessibility to information, which can help them identify and diagnose unusual bridge behaviours. |
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Vieira, J.; Poças Martins, J.; Marques de Almeida, N.; Patrício, H.; Gomes Morgado, J. Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins. Sustainability 2022, 14, 7060. https://doi.org/10.3390/su14127060
Vieira J, Poças Martins J, Marques de Almeida N, Patrício H, Gomes Morgado J. Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins. Sustainability. 2022; 14(12):7060. https://doi.org/10.3390/su14127060
Chicago/Turabian StyleVieira, João, João Poças Martins, Nuno Marques de Almeida, Hugo Patrício, and João Gomes Morgado. 2022. "Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins" Sustainability 14, no. 12: 7060. https://doi.org/10.3390/su14127060