Digital Twins, IoT, and AI in Civil Engineering: The New Era of Infrastructure Management and Geotechnical Control
Connected sensors and digital models can help engineering teams monitor ongoing construction and existing assets more continuously. The value lies not in replacing inspections or professional decisions with an algorithm, but in gathering relevant data, detecting changes, and supporting a technically sound response.
This guide introduces the concepts, applications, and limitations of digital twins in civil and geotechnical engineering. The focus is practical and general, for professionals, students, and project or operation teams. Safety criteria, standards, and responsibilities vary by country, structure type, and contract; they must be defined for each undertaking.
What is — and what is not — a digital twin
A BIM model organizes information about a construction, such as geometry, components, and specified characteristics. A digital twin goes further by maintaining a systematic relationship between a physical asset and a digital representation, incorporating observed data during construction or operation to support analyses and decisions. The nature of this link can vary: some solutions update information at defined intervals; others integrate more frequent data streams. Not every BIM model connected to a sensor constitutes, by itself, a useful or complete digital twin. Technical literature does not adopt a single universal definition, so the degree of update and synchronization must be described for each application. [1] [2]
Such a solution typically combines four elements:
- Asset and context: the structure, the ground, the construction phase, and the conditions influencing expected behavior.
- Measurements and records: sensors, inspections, tests, environmental data, and execution information, with identifiable location, unit, time, and history.
- Representation and analysis: BIM model, numerical models, comparison rules or statistical methods, and, when appropriate, artificial intelligence (AI) techniques.
- Decision and response: responsible individuals interpret results, verify data, and determine if inspection, re-measurement, activity adjustment, or activation of foreseen procedures is necessary.
The Internet of Things (IoT) can connect instruments and data acquisition systems. Local processing — sometimes called edge computing — can be useful when data needs to be processed near the sensor or when connectivity is limited. Storage on proprietary servers or in the cloud depends on project requirements and security, access, and operation conditions. No single architecture serves all projects. [1]
When is it worth adding this digital layer?
The choice should start with an engineering need, not a list of technologies. A digital twin can be relevant when the team needs to relate measurements from different sources, track trends over time, or maintain useful information for operation. For a simple structure, a well-planned inspection routine and a controlled spreadsheet may suffice. Instrumentation without a digital twin can also answer a specific question with less complexity.
| Need | Sufficient option | When to evaluate a digital twin | |---|---|---| | Record components, location, and documentation | BIM model or updated registry | When this data needs to be continuously related to asset measurements and occurrences | | Monitor a defined point or parameter | Instrument with manual reading and consistent recording | When the frequency, volume, or combination of measurements justifies integration and systematic analysis | | Verify a specific condition | Planned inspection or test | When there is a need to compare successive observations with history and design criteria | | Coordinate data between teams and phases | Common repository with clear responsibilities | When different systems need to exchange data and feed a documented decision process |
Before investing, describe the decision the information should support. If it's unclear who will interpret the result, what they will do in the face of an anomaly, and how they will verify an alert, increasing the number of sensors may only increase data volume and maintenance work.
How to organize the solution
1. Define the question and response criteria
Delimit the section or asset, the phenomenon to monitor, the consequences of a change, and those responsible for acting. Observation and alert criteria must be consistent with the design, local conditions, and safety procedures. A limit should not be chosen merely because it is easy to configure on a platform.
2. Choose instruments appropriate to the problem
The selection depends on the parameter, required precision, environment, installation, and maintenance method. In geotechnical applications, for example, piezometers for pore water pressure, inclinometers for lateral displacements, and plates or benchmarks for settlement monitoring may be considered. In structures, extensometers and accelerometers can contribute to observing deformations or vibrations. These instruments are not interchangeable: each measures something specific and requires appropriate installation, reading, and interpretation. [3]
Instrumentation should be sufficient to answer the question, not necessarily extensive. It is advisable to provide access for inspection, calibration or verification, physical protection, power supply, and a plan to replace or repair equipment. In locations with unstable networks, local readings, intermittent transmission, or scheduled manual checks may be considered, provided the alternative is compatible with the risk and project requirements.
3. Prepare data before modeling
A reading is only interpretable if it's possible to understand its origin and the conditions under which it was obtained. Record instrument identification and position, unit, time, acquisition method, configuration changes, periods without readings, and field interventions. Compare measurements with construction observations, inspections, and relevant tests. Differences in units, references, or clocks can create false patterns.
Data also needs access rules, backups, and change management. In connected infrastructure, digital security is part of planning: role-based permissions, controlled updates, access logging, and proper separation between monitoring systems and systems that can command equipment are measures to be evaluated with responsible teams. The specific configuration depends on the architecture and asset risk. [4]
4. Choose the level of analysis prudently
Comparison with defined limits, trend graphs, and physical models can be more transparent and sufficient for many needs. Machine learning methods can be evaluated when there is an appropriate question, relevant data, and means to test performance under representative conditions. An algorithm does not automatically transform scarce or biased data into reliable predictions. Seasonal changes, execution, use, or instrumentation can also affect observed patterns.
Analysis should indicate uncertainty and limitations, and allow review by competent professionals. An automated result can help prioritize an inspection; it should not be treated as isolated proof of a structure's safety or condition.
Hypothetical example: monitoring an embankment on compressible soil
Imagine a team responsible for an embankment constructed on compressible soil. The project foresees construction stages and criteria for evaluating pore water pressure and settlement. The team installs appropriate instruments and records readings along with the height of constructed layers and the dates of each activity.
If a reading appears different from the expected trend, the system can flag it for analysis. The team first checks for possible causes: instrument failure or displacement, unit error, communication interruption, recent change in the work, or a real change in ground behavior. If necessary, they confirm the reading with another measurement or inspection. Only then do the responsible professionals compare the results with the defined project criteria and decide whether the work sequence should be maintained, revised, or interrupted according to applicable procedures.
In this scenario, the system does not automatically “release” paving or predict final settlement with certainty. It helps organize evidence and make visible trends that might go unnoticed between readings. The decision remains dependent on data quality, geotechnical interpretation, and the technical responsibility established for the work.
Possible applications in different assets
- Slopes and excavations: monitoring displacements and water-related conditions can support comparison with project criteria and prioritization of inspections. Instrumentation does not eliminate the need to evaluate the ground, drainage, execution, and changes in local conditions.
- Embankments and foundations: settlement and pressure series can help understand behavior evolution throughout the stages. Consolidation models and predictions should be evaluated with appropriate data and assumptions, without extrapolating results to different locations without analysis.
- Concrete in construction: temperature sensors and maturity methods can provide complementary information on initial concrete strength gain. FHWA describes applications in pavements, including supporting decisions on traffic opening and formwork removal, but requires a specific calibration curve for the project's materials and mix; factors like consolidation may also not be reflected in the measurement. Use for guiding formwork removal, shoring, or prestressing depends on calibration, project specification, and approval from responsible parties. It does not automatically replace required tests and control procedures. [5] [6]
- Bridges, tunnels, and dams: measurements of deformation, vibration, displacement, pressure, or seepage can be combined with inspections and operational histories. An isolated variation does not necessarily identify the cause of an anomaly; environmental factors, traffic, interventions, and instrument condition need to be considered.
In all these cases, the system is a supporting layer for asset management. The ability to detect a change depends on what was measured, where, and with what quality; identifying the cause requires technical evaluation.
Limitations that need to be included in the plan
Sensor and communication failures. A device can lose power, go out of calibration, be damaged, or stop transmitting. Define how to identify gaps and improbable readings, how to maintain records, and what alternative method to use. Absence of an alert does not equate to absence of risk.
False alerts and context changes. Temperature, precipitation, traffic, construction sequence, and operating conditions can influence measurements. Alerts need context and a verification process to reduce both hasty responses and habituation to frequent warnings.
Integration and technological dependence. Formats, identifiers, and systems from different vendors may not communicate without adaptation. Before contracting, clarify how to export data, preserve history, manage access, and replace components or suppliers. Information exchange standards can help, but concrete compatibility must be verified in the project.
Cybersecurity and continuity. Connectivity expands possibilities but also requires access management, updates, and incident handling. Plan what happens if the platform, network, or external service becomes unavailable. For critical functions, the contingency process and authority to act need to be defined outside the digital dashboard. [4]
Total cost and team capacity. Beyond acquisition, consider installation, connectivity, licenses, calibration, storage, support, replacements, training, and analysis time. If the team cannot maintain the solution, a smaller configuration, periodic readings, or conventional instrumentation may be more realistic.
An implementation roadmap
- Formulate a use case: write down what decision you intend to support, for whom, and at what stage of the lifecycle.
- Define the baseline: gather available information about the asset, project, ground, instruments, and inspections.
- Establish criteria and responsibilities: determine who receives each alert, who confirms the measurement, and who has authority to decide, according to applicable procedures.
- Test at a controlled scale: verify installation, transmission, data quality, and interpretation before expanding the solution.
- Record results and limitations: document changes, failures, decisions, and lessons learned; revise the system when project or asset conditions change.
- Plan future operation: define maintenance, budget, data ownership and portability, service continuity, and technology decommissioning or replacement.
For professionals starting or transitioning careers, this topic brings together engineering, instrumentation, information management, and data analysis. A good contribution does not require mastering all these areas: understanding the physical problem, documenting assumptions, and communicating uncertainties are useful skills for collaborating with multidisciplinary teams.
Conclusion
Digital twins, IoT, and AI can improve how teams monitor infrastructure when they address a concrete need and rely on reliable data, clear processes, and expert review. They are not a guarantee of perfect prediction, absolute safety, or automatic cost reduction. The best starting point is a well-defined engineering question, a solution proportional to the risk, and a plan that states how to act — and how to verify — when information changes.
References
[1]: https://www.nist.gov/digital-twins "Digital twins" [2]: https://www.iso.org/standard/68078.html "ISO 19650-1:2018 Organization and digitization of information about buildings and civil engineering works, including building information modelling BIM — Information management using building information modelling — Part 1: Concepts and principles" [3]: https://rosap.ntl.bts.gov/view/dot/75281 "Geotechnical Instrumentation Reference Manual" [4]: https://csrc.nist.gov/pubs/sp/800/82/r3/final "NIST SP 800-82 Rev. 3 Guide to Operational Technology OT Security" [5]: https://highways.dot.gov/media/192146 "TechBrief Maturity Testing for Concrete Pavement Applications" [6]: https://www.fhwa.dot.gov/pavement/pub_listing.cfm?TitleStart=T "FHWA Pavement Publications listing" [7]: https://www.iea.org/energy-system/industry/cement "Cement Production, energy demand and emissions" [8]: https://essd.copernicus.org/articles/11/1675/ "Global CO2 emissions from cement production, 1928–2018" [9]: https://www.strategicmarketresearch.com/market-report/geotechnical-engineering-market "Geotechnical Engineering Market Report" [10]: https://www.marwadiuniversity.ac.in/blog/10-emerging-technologies-in-civil-engineering-transforming-the-industry-in-2026-and-beyond/ "10 Emerging Technologies in Civil Engineering Transforming the Industry in 2026 and Beyond" [11]: https://onlinedegrees.sandiego.edu/future-engineering-trends/ "The Future of Engineering: 15 Emerging Trends in Engineering Fields for 2026" [12]: https://ascelibrary.org/doi/book/10.1061/9780784485606 "Digital Twins in Construction and the Built Environment" [13]: https://link.springer.com/article/10.1007/s11440-025-02752-2 "Innovative LSTM-transformer hybrid models for predicting consolidation settlement" [14]: https://www.iea.org/reports/global-energy-review-2025/co2-emissions "CO2 Emissions in Global Energy Review 2025"