Structural Damage Detection Patents: Who Leads, Where the Gaps Are 2026
- 40.2% of all 82 records sit with just five assignees, yet the ranked list runs to 48 companies — concentration at the top with a long tail below it.
- Filings peaked in 2018 at 12 and the most recent complete years sit lower, though publication lag understates 2025-2026 activity.
- G01M and G01N cover the core, but G06N and G06T appear in only 15.9% and 9.8% of records respectively — machine-learning and imaging claims remain comparatively open.
Top-5 share is the combined record count of the five largest assignees divided by all 82 records in scope (CR5), not by the ranked leaders only.
What this dataset covers
This landscape draws on 82 published patent records matching structural damage detection and modal parameter identification claims, filtered against frequency-shift, mode-shape, baseline-model and false-alarm concepts. The scope runs from 2015 through the 2026-07-31 data cut-off, so the final year or two is necessarily partial — publication typically lags filing by around 18 months.
Records here span both algorithmic claims (novelty detection, baseline modelling, environmental compensation) and the sensing hardware that feeds them. The assignee ranking behind this page lists 48 companies, drawn from academic labs, individual inventors and a handful of corporate and government filers, rather than a single dominant industrial cluster.
Filing trends and technology composition
Two views of the same 82 records: how filing activity moved year over year, and which IPC subclasses the claims sit in. Because a single record can carry several IPC classes, the composition shares add to more than 100% of the record total.
Filing trend, 2017-2026
Filings ran at 5 in 2017, rose to a peak of 12 in 2018, and by 2026 stand at 3 — though 2026 is a partial year and the two or three years before it are also undercounted because of publication lag. There are fewer than four complete post-peak years in scope, so a growth rate cannot be stated reliably from this trend alone.
IPC subclass composition
G01M (testing machine and structure balance) appears in 46.3% of the 82 records and G01N (material analysis and testing) in 30.5%, together forming the core of the field. G01H (vibration and sound measurement) sits at 20.7%. Software-adjacent classes are thinner: G06F and G06N each cover 15.9% of records, and G06T (image data processing) and G01D (general measuring) each sit at 9.8% — signalling that computational and imaging approaches are present but not yet heavily claimed.
Shares are the percentage of the 82 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Structural Damage Detection Algorithms with Eureka
This page is one run against one query. Ask Eureka your own question about structural damage detection algorithms and every answer comes back with the patent numbers behind it.
Try EurekaMost-cited records in this dataset
Sensor placement method for reducing uncertainty of structural modal identification (US20200089733A1)
Filed by Dalian University of Technology, this record addresses sensor placement for structural health monitoring by separating the influence of structural model error from measurement noise. It treats structural stiffness variation as model error and Gaussian noise as measurement noise, then uses a Monte Carlo simulation to generate mode shape matrices under each error condition. A conditional information entropy index is used to quantify the resulting uncertainty in identified modal parameters, addressing the ill-conditioning problem in the Fisher information matrix that standard optimal sensor placement methods run into.Published 2020-03-19
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20200175352A1 | Structure defect detection using machine learning algorithms | 314 |
| 2 | US6292108B1 | Modular, wireless damage monitoring system for structures | 299 |
| 3 | WO2018165753A1 | Structure defect detection using machine learning algorithms | 149 |
| 4 | US5774376A | Structural health monitoring using active members and neural networks | 115 |
| 5 | US20050072234A1 | System and method for detecting structural damage | 106 |
| 6 | US6192758B1 | Structure safety inspection | 74 |
| 7 | US6779404B1 | Method for vibration analysis | 71 |
| 8 | US20200284687A1 | A method for automatically detecting free vibration response of high-speed railway bridge for modal identific… | 70 |
| 9 | US20100089161A1 | Vibration Based Damage Detection System | 59 |
| 10 | US20120123981A1 | Software to facilitate design, data flow management, data analysis and decision support in structural health … | 57 |
Citation counts reflect influence within the searched corpus and favour older filings; they are not a measure of current commercial relevance.
Each row carries its publication number; clicking a row searches Eureka by that number.
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Browse MCP servers →What the numbers say about this field
Three patterns stand out once the filing trend, the concentration figures and the IPC composition are read together.
A moderate concentration, not a monopoly
The top five assignees combined account for 40.2% of all 82 records in scope, rising to 61.0% across the top ten. That leaves close to 40% of filings spread across the remaining 38 of the 48 ranked companies — a long tail of single- or few-filing entrants rather than a market controlled by two or three players.
Activity has cooled since 2018, on paper
Filings peaked at 12 in 2018 and have run lower since, down to 3 by 2026. Because the 2026 figure is a partial year and publication lags filing by roughly 18 months, the true recent trend is understated — the apparent decline should not be read as a shrinking field without confirming against more recent filing-date data.
Machine-learning claims remain a minority
G01M and G01N testing and material-analysis classes dominate the composition, appearing in 46.3% and 30.5% of records respectively. G06N (AI-based computing) and G06F (digital data processing) each sit at only 15.9%, and G06T (image processing) at 9.8% — the algorithmic and imaging layers of damage detection are present in the corpus but far less densely claimed than the underlying sensing and testing methods.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to structural damage detection algorithms, with the prior art for and against each one.
Who is filing, and where the field is still open
The ranked list runs to 48 companies, mixing universities, individual inventors, and a small number of corporate and government filers. No assignee shows filing activity in the latest year among those tracked for momentum, consistent with the broader publication-lag effect across the dataset.
A modest lead, not a dominant share
The top-ranked assignee holds 8 of the 82 records in scope. Fifth place holds 6 and tenth place holds 3 — the drop-off from leader to mid-table is gradual rather than steep, which is consistent with a field still being built out by individual research groups rather than consolidated industrial R&D.
Co-filing is rare and mostly academic
Only four co-assignee pairs appear in the dataset. The strongest, between two individual researchers, spans 7 shared records — an academic partnership rather than a corporate joint venture. Corporate co-filing between an energy major and a research institute appears only at the one- and two-record level.
Filing is concentrated in the US and via PCT
The United States receives the largest share of filings at 41, followed by the European Patent Office at 12 and WIPO/PCT filings at 10. India (7), Canada (5) and Austria (2) round out the remaining receiving offices, indicating that protection strategy for this field is still largely US-anchored with selective international filing.
| Assignee | Recent year | YoY |
|---|---|---|
| University of Maryland, Baltimore County | 0 | — |
| BRINCKER RUNE | 0 | — |
| ANDERSEN PALLE | 0 | — |
| University of Manitoba | 0 | — |
| Dalian University of Technology | 0 | — |
| 2872475 ONTARIO LTD | 0 | — |
| Vrije Universiteit Brussel | 0 | — |
| Massachusetts Institute of Technology (MIT) | 0 | — |
Where to take this analysis
The figures on this page describe what has been filed. Turning that into a filing or freedom-to-operate decision means going deeper into specific claims and specific competitors.
Check freedom-to-operate against the most-cited records
The highest-citation records in this dataset, including the machine-learning-based defect detection filings, are the ones most likely to define blocking claims in this space.
Explore claims in Eureka →Track the under-claimed branches as they fill in
G06N and G06T classes are thin today but are exactly the areas where a new filer has room to establish a position before the field consolidates.
Monitor white space in Eureka →Watch for filings that outrun this data cut-off
Because publication lags filing by roughly 18 months, the true 2025-2026 filing rate is not yet visible in this dataset and should be re-checked periodically.
Set up alerts in Eureka →Common questions about this landscape
The ranked list behind this dataset covers 48 companies and research institutions, with the top-ranked assignee holding 8 of the 82 records in scope. The field is not dominated by one or two entities: the top five assignees together account for 40.2% of all records, and the top ten for 61.0%, leaving a substantial share spread across many smaller filers. Many of the top entries are universities and individual academic researchers rather than large corporations, reflecting the research-driven nature of modal identification and damage-detection algorithm development.
Filings peaked in 2018 at 12 in this dataset and have trended lower since, reaching 3 by 2026. However, 2026 is a partial year within this data cut-off, and publication typically lags actual filing by about 18 months, so the last two or three years understate real activity. There are not enough complete post-peak years in scope to state a reliable growth or decline rate, so this apparent slowdown should be treated as provisional rather than conclusive.
The core of the field sits in G01M (testing of machines and structures), covering 46.3% of the 82 records, and G01N (material analysis and testing) at 30.5%. G01H, covering vibration and sound measurement, appears in 20.7% of records. Software and AI-adjacent classes — G06F, G06N, G06T and G01D — each appear in under 16% of records, indicating that computational and imaging methods are a smaller, less-claimed layer built on top of the physical testing and sensing core.
US20200089733A1, filed by Dalian University of Technology, claims a sensor placement method that separates structural model error from measurement noise and uses a conditional information entropy index to quantify uncertainty in identified modal parameters, addressing the Fisher information matrix ill-conditioning problem. It does not claim damage detection or novelty-detection algorithms generally — its scope is specifically sensor placement optimisation for modal identification. Work using different uncertainty-quantification methods, or applied outside sensor placement, is less likely to fall within its claims, but a detailed claim chart is needed before relying on that distinction.
Based on the IPC composition, AI-based computing (G06N) and image-based processing (G06T) each appear in under 16% of the 82 records, well below the 46.3% and 30.5% shares held by the core testing and material-analysis classes. This suggests that algorithmic approaches such as adaptive baseline modelling for environmental variability, AI-driven novelty-detection thresholds, and image-based defect quantification are comparatively under-claimed relative to the physical sensing and testing methods they build on. A first claim in these areas would need to tie the computational method tightly to a specific structural monitoring use case to stand apart from the broader testing-class prior art.
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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.