Cable-Stayed Bridge Structural Monitoring Patents: Trends & Leaders 2026
- Filings peaked in 2018 at 17 and fell to 2 by 2022, a decline rather than a plateau, with the latest tracked year showing no published filings.
- Sensing and software split the field almost evenly, with 16 records in material testing (G01N) matched by roughly 20 across digital processing, imaging and AI subclasses combined.
- The densest collaboration cluster ties Hitachi-GE Nuclear Energy, the University of Bristol and Inductosense together at 8 shared families, pointing to acoustic/ultrasonic sensing as the field's most consolidated sub-area.
A small, concentrated field past its filing peak
Cable-stayed bridge structural monitoring sits at the intersection of civil sensing hardware and, increasingly, computational analysis. The dataset behind this page covers 46 patent families published between 2015 and mid-2026, drawn from filings that combine cable-stayed bridge structures with structural health monitoring, cable tension monitoring or bridge deformation monitoring in their text and classification. That is a modest pool by patent-landscape standards, which makes both the concentration at the top and the gaps at the edges easier to read clearly.
Filing activity rose through the mid-2010s, peaked in 2018, and has declined since — a pattern that shows up consistently across receiving offices, with the United States, China and PCT filings via WIPO accounting for most of the volume. The technology composition splits roughly evenly between physical sensing and testing claims and software-side claims covering image processing, digital data handling and AI-based computation, indicating the field has not settled on a single dominant monitoring architecture.
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Filing trends and technology composition
46 published families spanning 2015 to mid-2026 show a field that peaked early and has since gone quiet on volume, even as the underlying technical approaches diversified across sensing, imaging and computation.
A 2018 peak followed by a decline
Filings rose to 6 by 2017 and peaked at 17 in 2018, the high point of the dataset. By the 2022 midpoint, annual filings had fallen to just 2, and the trend has not recovered since — publication lag means the final year or two will always undercount, but the multi-year decline through the midpoint is a real pattern, not an artifact of the cut-off.
Sensing and testing dominate, but software claims are close behind
Material analysis and testing (G01N) leads with 16 records, more than double the next-largest bucket. Structural testing (G01M) holds 6, while the software-adjacent subclasses — digital data processing (G06F), image processing (G06T) and AI-based computing (G06N) — together account for 20 records, roughly matching the physical-sensing side. Narrower subclasses in dimensional measurement, image recognition and alarm systems each sit at just 3 records.
Shares are the percentage of the 46 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Cable-Stayed Bridge Structural Monitoring with Eureka
This page is one run against one query. Ask Eureka your own question about cable-stayed bridge structural monitoring and every answer comes back with the patent numbers behind it.
Try EurekaThe most-cited records in this dataset
Device for monitoring the health status of structures
A structural health monitoring device built for improved reliability, applied at selected structure locations. It combines data acquisition, processing and storage with a direct, independent wireless connection to a standard interconnected telecom network, uninterrupted power from at least two independent battery sources, and sensors designed to remain permanently active and asynchronously trigger acquisition sessions when they detect unpredictable, structurally relevant events.Filed by Bastianini Filippo, published 2010-09-23; cited 86 times within this corpus.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20200175352A1 | Structure defect detection using machine learning algorithms | 314 |
| 2 | WO2018165753A1 | Structure defect detection using machine learning algorithms | 149 |
| 3 | US20100238027A1 | Device for monitoring the health status of structures | 86 |
| 4 | WO2011016857A2 | Equipment and system for structure inspection and monitoring | 70 |
| 5 | US20140361888A1 | Solar light-emitting diode lamp wireless sensor device for monitoring structure safety in real-time | 55 |
| 6 | US20190195728A1 | System and Method of Monitoring a Structural Object Using a Millimeter-Wave Radar Sensor | 33 |
| 7 | WO2009063523A2 | Device for monitoring the health status of structures | 25 |
| 8 | US20220383478A1 | Computer vision-based system and method for assessment of load distribution, load rating, and vibration servi… | 24 |
| 9 | US11144814B2 | Structure defect detection using machine learning algorithms | 20 |
| 10 | CA3056498A1 | Structure defect detection using machine learning algorithms | 12 |
Ranked by citation count within the searched corpus; older records naturally accumulate more citations, so treat this as a measure of influence rather than current relevance.
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Browse MCP servers →What the filing pattern signals
Beyond the raw counts, three patterns stand out for anyone deciding where to file next or which claims to design around.
ML defect detection draws outsized downstream attention
US20200175352A1 and its PCT counterpart, both on machine-learning-based structure defect detection, are cited 314 and 149 times respectively — far ahead of any other record in the corpus. That gap indicates later filers have repeatedly had to reference or design around these specific claims, even though ML-based subclasses are not the largest by filing volume.
A field that has cooled since its high point
Annual filings rose to 6 by 2017, peaked at 17 in 2018, and had dropped to 2 by 2022. None of the more active assignees in this dataset show filings in the latest tracked year. Even accounting for publication lag, the multi-year decline through the midpoint points to reduced filing appetite rather than a temporary reporting gap.
Hardware sensing and software inference are near parity
Material analysis and testing (G01N) leads at 16 records, but digital data processing, image processing and AI-based computing together account for a comparable share. No single approach has pulled decisively ahead, suggesting the field has not settled on one dominant monitoring architecture.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to cable-stayed bridge structural monitoring, with the prior art for and against each one.
| Assignee | Co-assignee | Shared families |
|---|---|---|
| Hitachi-GE Nuclear Energy, Ltd. | University of Bristol | 8 |
| Hitachi-GE Nuclear Energy, Ltd. | INDUCTOSENSE LTD | 8 |
| University of Bristol | INDUCTOSENSE LTD | 8 |
| KOVNAT SAM | GRAVES SPENCER | 2 |
| KOVNAT SAM | ELLIOTT JAMES C | 2 |
| Dalian University of Technology | Beijing University of Civil Engineering and Architecture | 1 |
| Dalian University of Technology | China Communications Construction Company | 1 |
| Dalian University of Technology | CCCC National Engineering Research Center for Highway Long-span Bridge Construction Co., Ltd. | 1 |
Eight co-assignee pairs exist in this dataset; the three strongest all trace back to the same three-way cluster — Hitachi-GE Nuclear Energy, the University of Bristol and Inductosense Ltd — each pairing shared across 8 families, indicating a tightly held research partnership around acoustic/ultrasonic sensing rather than a broadly distributed collaboration network.
Who is filing, and where the claim space is thin
The assignee ranking in this corpus is short and collaboration-heavy rather than dominated by a single large filer, with the strongest links running between a nuclear-services firm, a university and a sensor specialist.
Hitachi-GE Nuclear Energy, Bristol and Inductosense
These three names form all three of the strongest co-assignee pairs in the dataset, each sharing 8 families — a tight three-way research relationship centred on acoustic or ultrasonic sensing technology rather than a broad multi-party network.
University labs are active but not dominant
Institutions including the University of Manitoba and Dalian University of Technology appear among the more active assignees, reflecting the field's roots in structural-engineering research rather than a single corporate R&D pipeline.
US filings lead, with China and PCT routes next
The United States accounts for the largest share of receiving-office filings at 17, followed by China at 8 and PCT applications via WIPO at 6. Europe, Japan and the UK each contribute smaller shares, indicating the commercial interest is concentrated in a handful of jurisdictions rather than filed broadly worldwide.
| Assignee | Recent year | YoY |
|---|---|---|
| Hitachi-GE Nuclear Energy, Ltd. | 0 | — |
| University of Bristol | 0 | — |
| INDUCTOSENSE LTD | 0 | — |
| University of Manitoba | 0 | — |
| Dalian University of Technology | 0 | — |
| BASTIANINI FILIPPO | 0 | — |
| Infineon Technologies AG | 0 | — |
| South China University of Technology | 0 | — |
Where to take this analysis
The filing pattern here raises specific questions worth investigating further before committing R&D or filing resources.
Check freedom to operate around the ML-defect-detection claims
The two most-cited records in this corpus both cover machine-learning-based defect detection, with citation counts far above the rest of the dataset. Any product roadmap that touches automated defect classification should map its approach against those specific claims first.
Explore claim scope in EurekaInvestigate the thinner subclasses before assuming they're open
Dimensional measurement, image recognition and alarm-signalling subclasses each show only 3 records, but they sit next to much denser neighbours. A targeted search can confirm whether that thinness reflects real white space or simply narrower classification.
Run a targeted search in EurekaTrack whether filing activity resumes post-2022
Filings fell from a 2018 peak of 17 to just 2 by 2022, and publication lag means the most recent years are undercounted. Revisiting this trend in a future data pull will clarify whether the field is genuinely winding down or due for a rebound.
Set up trend tracking in EurekaCommon questions on cable-stayed bridge monitoring patents
The dataset's ranking is led by a small cluster of assignees rather than one dominant filer, including Hitachi-GE Nuclear Energy, the University of Bristol and Inductosense Ltd, which also form the strongest co-assignee pairing in the corpus at eight shared families each. University-affiliated filers such as the University of Manitoba and Dalian University of Technology also appear among the more active assignees. None of these show filings in the latest tracked year, consistent with the dataset's overall decline from its 2018 peak. Because the total pool is only 46 families, rankings here reflect a genuinely small field rather than a large market with a clear runaway leader.
Material analysis and testing (IPC class G01N) is the largest single bucket at 16 of 46 records, covering sensor-based structural material assessment. Structural and machine testing (G01M) adds 6 more. The remaining volume splits across digital data processing, image processing and AI-based computing subclasses, which together approach the size of the physical-testing side, showing that monitoring approaches now split fairly evenly between hardware sensing and computational analysis of the data it produces.
Filings peaked in 2018 at 17 and had fallen to just 2 by the 2022 midpoint, indicating a declining rather than growing trend over the tracked window. The most recent year in the dataset shows zero filings, but that figure is not a reliable signal on its own — patent publication typically lags actual filing by around 18 months, so recent years are always undercounted. Even allowing for that lag, the multi-year decline through the midpoint suggests filing activity has genuinely cooled since the 2018 peak rather than merely awaiting publication.
US20100238027A1, filed by Bastianini Filippo in 2010, describes a structural health monitoring device with dual independent battery power, a direct wireless link to a standard telecom network, and sensors that asynchronously trigger data capture when they detect unpredictable structural events rather than sampling on a fixed schedule. It is one of the most-cited records in this corpus, with 86 citations, making it a common reference point for later filings in permanently-installed monitoring hardware. Anyone designing a similar always-on wireless sensor node for bridge monitoring should review its claim boundaries before finalising a power and triggering architecture.
The thinnest subclasses in the dataset — dimensional measurement (G01B), image and video recognition (G06V), and alarm/signalling systems (G08B) — each carry only 3 records, well below the 16-record testing cluster and the 20-record combined software cluster. These sit adjacent to more crowded neighbours, so genuine white space likely lies in narrow combinations, such as tension-deviation alarm logic tied to specific communication protocols, rather than in the broad subclass itself. A freedom-to-operate check against the dataset's densest co-assignee cluster is still advisable before committing engineering resources to any of these narrower pockets.
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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.