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Run your analysis now →https://www.patsnap.com/resources/blog/rd-blog/digital-twins-for-bridge-structures-patent-landscape/ · Patsnap · data cut-off 2026-07-31 · downloaded from the live page
Patent Landscape · Structural Health MonitoringDigital Twin Patents for Bridge Structures: Who Files, and What They Claim
- All 14 records in scope trace back to just four ranked assignees, a field that is small enough for one filer to shape the claim map.
- Filing peaked at 12 records in 2023, then dropped off — publication lag means the 2026 count of 0 is not yet a real signal.
- 71.4% of records carry a G06N (AI model) class alongside structural claims, showing the digital twin is being built as a computing invention first, a bridge-specific one second.
14
Published Records
US
Leading Jurisdiction
2023
Peak Filing Year
G06N
Lead IPC Subclass
Published byPatsnap Research··7 min readSourced from Patsnap Eureka
Overview
What this landscape covers
This landscape tracks patent filings that combine a bridge or structural digital twin with a computational method for keeping that twin accurate — finite element updating, inverse analysis, field reconstruction, model calibration or real-time data ingestion. It is a narrow, applied intersection: not general digital twin filings, and not general structural health monitoring, but the overlap where a virtual model of a physical bridge is kept current against live sensor input and used to support a decision.
Fourteen published records sit in scope across the 2015–2026 window, with activity concentrated in a short run of years rather than spread evenly across the period. The assignee set behind those records is small, and the technology composition leans heavily on computing and modelling classes rather than civil-engineering-specific ones — a sign that the invention work is happening at the software layer as much as the sensor layer.
Records in scope by filing year
Source: Patsnap Eureka. Assignee ranking and totals. Derived from a Patsnap search on Digital Twins for Bridge Structures covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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The Numbers
Filing trend and technology composition
Two views of the same 14 records: when they were filed, and which IPC subclasses they fall under. Because a single record can carry several classes, the composition shares add up to well over 100%.
Filing activity, 2017–2026
Filings sat at zero in 2017 and climbed to a peak of 12 records in 2023 before falling away. The most recent years are undercounted because publication typically lags filing by around 18 months, so 2026's count of 0 reflects the reporting window more than a real stop in activity.
IPC subclass distribution
G06N (AI-based computing models) appears on 71.4% of the 14 records, ahead of B63B (marine vessels, 50.0%), G06Q (business data processing, 42.9%) and G06F (digital data processing, 35.7%). Structural-testing classes G01M and G01N each sit at only 7.1–14.3%, which is a useful marker of where the bridge-specific engineering claims are thin relative to the software claims wrapped around them.
Shares are the percentage of the 14 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Source: Patsnap Eureka. Filing trend and technology composition. Derived from a Patsnap search on Digital Twins for Bridge Structures covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.
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Representative filing
Representative Filing
Systems, methods, and applications for the construction of bridge digital twin
UNIVERSITY OF FLORIDA RESEARCH FOUNDATION
The disclosure covers a bridge digital twin built by executing a digital model of a physical bridge carrying vehicular traffic, coupling a control system to weight sensors positioned on the bridge or the approach road, receiving real-time sensor data from those sensors, and triggering an alert on a digital sign at the bridge entrance or approach when the sensor readings meet a defined condition.Filed by University of Florida Research Foundation, published 2026-01-15.
Most-cited records in scope
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20230382504A1 | Live Risk Analysis Model and Multi-Facet Profile for Improved Vessel Operations and Class Survey | 12 |
| 2 | US20220082389A1 | Evacuation using digital twins | 8 |
| 3 | WO2024129965A1 | Systems, methods, and applications for the construction of a bridge digital twin | 5 |
| 4 | US20260016364A1 | Systems, methods, and applications for the construction of bridge digital twin | 1 |
| 5 | US11747145B2 | Evacuation using digital twins | 1 |
Citation counts reflect the searched corpus only and favour older filings; treat them as a signal of influence within this dataset, not a ranking of current technical importance.
Publication numbers are shown where the record carries one (5 of 5 rows); clicking a row searches Eureka by that number.
Source: Patsnap Eureka. Citation counts and representative records. Derived from a Patsnap search on Digital Twins for Bridge Structures covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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What the numbers mean for a filing decision
With only 14 records in scope, this is not a crowded field in absolute terms — but the concentration among a handful of assignees and the tilt toward computing IPC classes both narrow where a new filing can sit cleanly.
Concentration
100.0%
of 14 records held by the ranked assignees
A small field, fully accounted for
The top four ranked assignees between them cover all 14 records in scope. There is no long tail of single-filer entrants here yet — the field is still small enough that a new filer is entering alongside a short, identifiable list rather than into an already-fragmented market.
Based on the assignee ranking, 4 companies, 14 records.
Technology tilt
71.4%
of records carry a G06N AI-model class
Modelling claims outweigh sensing claims
G06N appears far more often than the structural-testing classes G01M or G01N. Filings are being built around the computational method for maintaining the twin — model calibration, inverse analysis, real-time updating — rather than around new sensor hardware or measurement technique.
IPC composition, 14 records in scope.
Filing momentum
12 in 2023
peak year for records in scope
Activity clustered, then paused
Filings were absent in 2017, rose to a peak of 12 in 2023, and have since fallen — though the most recent years understate true activity because publication lags filing by roughly 18 months. A near-term filer should not read the 2026 figure of 0 as a market that has gone quiet.
Filing trend, 2017–2026, data cut-off 2026-07-31.
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Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to digital twins for bridge structures, with the prior art for and against each one.
Source: Patsnap Eureka. Co-assignee relationships and derived observations. Derived from a Patsnap search on Digital Twins for Bridge Structures covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
Players
Who holds the claim space
Four assignees account for every record in this dataset, spanning a classification society, a large technology company and a university research foundation. None of the four show new filings in the latest year, consistent with the broader slowdown after the 2023 peak — though publication lag means that is not conclusive.
Filing leader
7
records
Leads the ranked assignees
The top-ranked assignee holds 7 of the 14 records in scope, roughly half the entire dataset on its own — an unusually strong concentration for a field this size.
Assignee ranking, 14 records total.
Classification society entrant
Marine + bridge overlap
B63B alongside G06N
Vessel-survey logic applied to bridges
Filings referencing marine vessel classes (B63B) alongside AI-model classes suggest at least one filer is extending risk-and-survey modelling built for ships into bridge structures — a route that reuses an existing digital twin architecture rather than starting from a civil-engineering base.
IPC co-occurrence, 14 records in scope.
University filer
2026-01-15
most recent publication in scope
Academic filing anchors the newest record
University of Florida Research Foundation holds the most recently published record in this dataset, a bridge-specific weight-sensor and digital-sign alert system, filed later than the rest of the corpus and worth tracking for follow-on continuations.
Representative record, published 2026-01-15.
Recent-year filing momentum by assignee
| Assignee | Recent year | YoY |
|---|---|---|
| American Bureau of Shipping | 0 | — |
| International Business Machines Corporation (IBM) | 0 | — |
| University of Florida Research Foundation, Inc. | 0 | — |
| AMERICAN BUREAU OF SHIPPING SPRING | 0 | — |
Source: Patsnap Eureka. Assignee-level momentum. Derived from a Patsnap search on Digital Twins for Bridge Structures covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
What's Next
Where to take this
The dataset points to a small, concentrated field with a clear computing-class tilt. Two directions follow from that.
Check freedom-to-operate against the leader's 7 records
With one assignee holding half the dataset, any new bridge digital twin filing should be checked claim-by-claim against that assignee's portfolio before drafting.
Run a freedom-to-operate checkDraft around structural-testing classes, not computing ones
G01M and G01N sit at 14.3% and 7.1% of records respectively, well below the AI-model classes — a claim anchored in sensor placement or field reconstruction method has more open space than one anchored in general model calibration.
Explore white space in EurekaSource: Patsnap Eureka. Forward-looking reading of the same dataset. Derived from a Patsnap search on Digital Twins for Bridge Structures covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
FAQ
Common questions about bridge digital twin patents
This dataset identifies 14 published records in scope, filed between 2015 and 2026, that combine a bridge or structural digital twin with a real-time calibration method such as finite element updating, inverse analysis or field reconstruction. That is a small, tightly defined intersection rather than a count of every digital twin or structural monitoring patent — broader searches on either topic alone would return far more. The field is young enough that filing activity only became visible from the late 2010s onward.
Four assignees account for all 14 records in this dataset, including a classification society, a major technology company, and a university research foundation. The top-ranked assignee alone holds 7 of the 14 records, meaning roughly half the field sits with a single filer. That level of concentration is unusual and worth checking directly before filing a new application in this space.
The dominant IPC class is G06N, covering AI-based computing models, which appears on 71.4% of the 14 records. B63B (marine vessels), G06Q (business data processing) and G06F (digital data processing) also appear frequently, while structural-testing classes like G01M and G01N are comparatively rare. This suggests most inventions in scope are built around the computational method for maintaining the twin rather than around new sensor hardware for the bridge itself.
Filings in scope peaked at 12 records in 2023 and have declined since, with the two most recent years showing little to no activity. Because patent publication typically lags actual filing by around 18 months, the most recent years understate real activity and should not be read as the field going quiet. A clearer trend will only be visible once the 2025 and 2026 filing years finish publishing.
The IPC composition shows structural-testing classes such as G01M (testing machine and structure balance) and G01N (material analysis and testing) covering only 14.3% and 7.1% of the 14 records respectively, far below the AI-modelling classes. That gap points to under-claimed territory in sensor-specific field reconstruction, load-path inverse analysis, and decision-support alerting tied directly to bridge structures rather than general-purpose digital twin computing methods.
Answers are grounded in the same dataset. Derived from a Patsnap search on Digital Twins for Bridge Structures covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish.Run this in Eureka MCP
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company's registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.