Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure
- inability of computerised maintenance management systems or, in other words, facility management systems to automatically schedule maintenance work orders
- difficulty in accessing accurate information for the facility management staff.
- Data exchange: One of the costly shortcomings of the existing processes is data exchange during inspection planning, implementation and reporting, as most likely each of these phases is conducted by a different party (i.e., planners, inspectors, maintainers). In existing practices, data transfer from inspections, planning and maintenance needs to be either manually entered or converted (to a compatible format), which carries the risk of errors and data loss.
- Interoperability: This issue is an important function in data management . Currently produced digital data are in different formats and not necessarily compatible between different software, which makes their lifespan limited and with little guarantee that the data will make it to later stages of asset utilisation. According to Smith and Domer , the model-based application outputs’ interoperability challenges costs the U.S. facility owners USD 15.8 billion as a result of lost efficiency. Of this amount more than 65% belongs to the operation and maintenance stage.
- Data management: 80% of the time spent on managing a facility is to look for appropriate and useful information. Previous research has revealed that new technologies such as Building Information Modelling.
- (BIM) enables a reduction of 98% in the time spent to update the database while managing facilities .
- Data loss along asset lifecycle phases as a result of (1) incoherent naming conventions (2) lack of a universal standard for the required asset information (3) insufficient categorisation of data in different model-based applications (4) ill synchronisation of information (5) lack of a structured model for capturing existing assets and their details .
- Being labour intensive and therefore not cost effective.
- Reactive (firefighting/corrective) also known as failure-based.
- Preventative—also known as time-based. DT approach (further explained in Section 4) can be utilised in preventative strategies to predict the state of an asset and therefore reduce the number of unnecessary preventative maintenance activities providing longer time intervals between them.
- Condition-based (based on asset condition)—also known as diagnosis-based maintenance (efficient detection of anomalies in the existing condition of an asset and fast data processing is made possible thanks to the developed state of technologies such as IOT and cloud computing ). Artificial intelligence algorithms can enhance this maintenance approach in diagnosing and acquiring detailed status data .
- Predictive (prognosis)—this approach can use a data driven (data driven techniques (clustering, neural networks, Bayesian networks, support vector machine (SVM) and principal components analysis (PCA)) are data-oriented, which can be obtained with appropriate sensor deployment) or model driven (the model driven technique uses mathematical methods (analytical, physical, or numerical models) to describe an asset) technique to predict maintenance.
- Prescriptive (knowledge-based) maintenance—in addition to predicting the status of the asset this approach aims to optimise maintenance via prescribing action to maintain an asset by using historical and real-time data.
2. Advancements in Structural Health Monitoring (SHM) Methods
- BIM—to visualise the infrastructure performance
- sensors—to collect real-time data and
- structural analysis methods—to simulate and study structural performance (e.g., finite element analysis (FEA), which is a simulation method to objectively define and predict the performance of infrastructures)
3. DT and Intelligent Infrastructure Maintenance
- Structural classifications (annotate elements of structure)
- Localising damage from structural inspections over time
- Localising installed sensors
- Contextualising generated data (damages to structural components etc.).
3.1. DT Application in Improving Maintenance Practices
3.2. Roadblocks, Alternatives and Research Questions
- A detailed maintenance procedure to structure the processes
- Data availability, integrity, security, and quality
- Regulatory barriers (regulatory bodies have a timely process in adopting new technologies)
- Reluctance to change traditional methods (making asset owners aware of DT’s benefits is fundamental in gradual incremental deployment of the DT)
- Alternatives to generate required data to address data availability challenges include:
- IoT/industry internet of things (IIoT) technologies
- FEM (generating synthetic data via simulation though defining the scenarios to simulate and generate data is a challenge) and
- BIM as a source of data
- Is DT application for infrastructure SHM using FEM models feasible?
- What are the challenges in terms of interoperability? How to make different software/platforms communicate with each other?
- What sensor configuration is needed to collect the required data? How to determine sensor locations?
4. Designing DT Architecture for Infrastructure SHM
- State: Current value/condition of either of the entities (physical or virtual objects)
- Metrology: Measuring/gauging state of either of the entities (physical or virtual)
- Twining: As the name implies this is the process of communicating the entities’ state to each other
- Data processing: this is related to analysis and process of the telemetry data from real object collected from sensors
- Realisation: this is after on boarding the result of the data processing in the previous step which results in changing the state of the physical/virtual objects
- Twining rate: is related to the frequency of creating the connection between the two entities
- Physical to virtual and virtual to physical connection: relate to the connection flow direction between the two entities
4.1. Semantic Modelling
4.2. Processing Monitoring Data
4.3. Integration Levels
4.4. DT Architecture Design
- a visualised digital replica containing geometric and semantic data in terms of materials, stresses, and strains
- utilising data spanning along the infrastructure whole life cycle, i.e., planning, design, constructing, operation and maintenance, and disposal
- physical connection (i.e., to monitor existing state in real time)
- ability to inform future projects and maintenance practices through generating valuable informative data from processing real-time data and/or simulating what-if scenarios
- Building appropriate model of an infrastructure (multi-domain, multi-disciplinary)
- Establishing the connection between the infrastructure and its DT to support seamless monitoring
- Integrating different services
- Consolidating data from physical model and its DT and developing data aggregation and interpretation methods/algorithms (key to generating consumable information) and
- Developing maintenance measure decision-making methods
- Which parts/components need to be monitored?
- What domain needs to be measured (e.g., displacement, temperature, deflection etc.)?
- Which sensors (and configuration/arrangement) are adopted to monitor and measure infrastructure responses?
- What are the thresholds for fault warnings? (e.g., the predefined allowable tolerance for the measured strain by a strain gauge before a fault alarm is triggered)
- What is the frequency of data collection and simulation to make the system efficient?
- What analysis processes are needed to drive and interpret the data (modelling and simulation)?
Advantages of the Proposed Architecture Compared to Existing Methods
5. Case Study
Implementing the DT Concept on DBCT
- Wind and cyclonic pressures as the structure is located in a tropical coastal area and prone to cyclones and strong winds (e.g., as per AS 1170.2 the cyclonic wind speed can be as strong as 55 m/s)
- Live loads on belts and their fluctuations over time to monitor the structural responses to these loads including belt floodings and encrustation.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
|Time||2315 (X-Axis)||2315 (Y-Axis)||2315 (Z-Axis)|
|4 July 2021 16:05:29.000||46.56||2.69||43.01|
|4 July 2021 16:11:29.000||46.6||2.71||43.03|
|4 July 2021 16:17:29.000||46.6||2.76||43.04|
|4 July 2021 16:23:29.000||46.58||2.76||43.02|
|4 July 2021 16:29:29.000||46.57||2.76||43.04|
|4 July 2021 16:35:29.000||46.58||2.76||43.03|
|4 July 2021 16:41:29.000||46.58||2.72||43.04|
|4 July 2021 16:47:29.000||46.58||2.76||43.04|
|4 July 2021 16:53:29.000||46.58||2.78||43.07|
|4 July 2021 16:59:29.000||46.6||2.8||43.07|
|4 July 2021 17:05:29.000||46.58||2.66||43.05|
Appendix A.4. The Sequel Codes to Generate a Thing in Thingworx
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|Static data||Static and dynamic data|
|Formed by data||Includes both data and algorithms to explain the behaviour of physical object|
|Cannot be networked||Networkable|
|Cannot be updated without manual intervention||Continuously linked to the physical object and gets updated|
|Is not designed for real-time operational monitoring||Is fed by real-time data|
|Relates performance data with prior physics-based model predictions (from FEMs).||Is formulated based on performance data and forms a statistical model to identify trends, patterns, and correlations|
|Modelling||Higher effort is required||Comparatively lower effort is required|
|Load Case||Loading/m Conveyor||Loading per Member per Node|
|Live distribution 4 m/s||250 kg/m||1.225 KN/m|
|Live point 4 m/s||750 kg/space||3.679 KN/m|
|Dead distributed load from conveyor||10 kg/m||0.049 KN/m|
|Dead point load from conveyor||30 kg/space||0.147 KN/node|
|Wind load (+ve)||20 m/s||1.43 KN/m|
|Wind load (−ve)||20 m/s||−1.43 KN/m|
|Cyclonic load normal to longitudinal section||55 m/s||3.9 KN/m|
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Mahmoodian, M.; Shahrivar, F.; Setunge, S.; Mazaheri, S. Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure. Sustainability 2022, 14, 8664. https://doi.org/10.3390/su14148664
Mahmoodian M, Shahrivar F, Setunge S, Mazaheri S. Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure. Sustainability. 2022; 14(14):8664. https://doi.org/10.3390/su14148664Chicago/Turabian Style
Mahmoodian, Mojtaba, Farham Shahrivar, Sujeeva Setunge, and Sam Mazaheri. 2022. "Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure" Sustainability 14, no. 14: 8664. https://doi.org/10.3390/su14148664