Geopolymer Concrete Digital Twin Patent Snapshot
Patenting at the intersection of geopolymer concrete and digital-twin modeling is in its earliest stages, with all 4 patent families on record filed in 2024 or later, split equally between China and India. The field is effectively open: five applicants each hold a single patent record, and no dominant incumbent has yet emerged.
A nascent field with no dominant player — yet
The corpus comprises 4 patent families, divided across five applicants — Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology (VNRVJIET), Nanjing Institute of Technology, SR University, Jianhua Construction Materials (China) Co. Ltd., and Liaoning Technical University — each holding exactly one patent record.
The top five filers account for the entire output of the ranked applicants visible in this query in this space, reflecting a field so nascent that no institution has yet accumulated more than a single record. There is no meaningful tier gap because no leader has separated from the pack.
| # | Applicant | Patent records | Share |
|---|---|---|---|
| 1 | Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology (VNRVJIET) | 1 | |
| 2 | Nanjing Institute of Technology | 1 | |
| 3 | SR University | 1 | |
| 4 | Jianhua Construction Materials (China) Co. Ltd. | 1 | |
| 5 | Liaoning Technical University | 1 |
This extreme dispersion, combined with the recency of all filings, signals that the digital-twin approach to geopolymer concrete is still being defined technically and has attracted only exploratory, proof-of-concept filings from a small set of academic and industrial pioneers.
All filings appeared in 2024 or later; the most recent entries are subject to typical publication lag of 18–24 months and the true corpus may be larger than currently indexed. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
Activity concentrated in 2024–2026; AI and simulation dominate the technology mix
The annual trend and technology composition together reveal a field that was dormant through 2023 and has only recently produced its first filings, almost all anchored in digital and computational methods rather than materials chemistry.
Annual filing trend
Zero activity was recorded from 2017 through 2023; 2 records appeared in 2024, followed by 1 in 2025 and 1 in 2026. Given standard publication lag, the 2025–2026 counts are likely under-reported and should not be interpreted as a plateau.
↗ Hover for values · click a bar to ask EurekaTechnology composition
Electric digital data processing (G06F), AI-based computing (G06N), business and administrative data processing (G06Q), and computational chemistry (G16C) each appear in 2 records, while cement shaping (B28B) and ceramics and refractories (C04B) each appear in 1. The balance tilts heavily toward software and simulation methods, with materials processing as a secondary theme.
↗ Hover for values · click a bar to ask EurekaHighly cited patent families surfaced by the query
Citation-heavy patent families returned by the query. Use this section as citation context, not as a curated list of the most topic-specific patents.
Machine learning, deep learning and interactive GU…
The present invention discloses an artificial intelligence-based interactive system with a graphical user interface for predicting the compressive strength of recycled aggregate incorporated geopolymer concrete. The system integrates eight machine learning and deep learning models, including Linear Regression, Random Forest, Support Vector Regression… (excerpt from the patent abstract)
Open this patent in Eureka →| # | Patent | Citations |
|---|---|---|
| 1 | 基于机器学习的碱激发尾矿胶凝活性及其制备混凝土强度预测方法 | 2 |
Ranked by total forward citations. Citation counts favour older and broadly cited patent families, and broad or adjacent patents may appear when they match the search scope. Treat this section as citation context, not as a curated list of the most topic-specific patents. Some patent titles may be shown in their original, non-English language where an accurate translation could not be guaranteed.
Assignee snapshot from the current evidence set
The applicants below are visible in this query result. Because the evidence set is relatively small, read this section as a directional snapshot rather than a full competitive ranking.
Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology
VNRVJIET holds 1 patent record with a focus on AI-model subclasses — G06N 20 (machine learning), G06N 3 (neural networks), and G06N 5 (knowledge-based reasoning) — indicating an approach centered on predictive modeling of geopolymer properties through learned models rather than physics-based simulation. As a new entrant, its trajectory is at the baseline of the field.
patent records: 1Jianhua Construction Materials & Nanjing Institute of Technology
Both Jianhua Construction Materials (China) Co. Ltd. and Nanjing Institute of Technology entered as new entrants with 1 patent record each, filed collaboratively. Their shared technical emphasis on G06F 30 (simulation-driven design), G06F 113, and G06F 119 points to a digital-twin workflow built around engineering simulation software — a complementary but distinct approach from the AI-model path pursued by VNRVJIET.
patent records: 1 eachFrequently asked questions
The corpus contains 4 patent families in scope. All were filed in 2024 or later, making this one of the earliest-stage intersectional technology spaces in the sustainable construction domain.
Five applicants each hold exactly 1 patent record: Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology (VNRVJIET), Nanjing Institute of Technology, SR University, Jianhua Construction Materials (China) Co. Ltd., and Liaoning Technical University. No single organization leads by volume.
China and India are the only active jurisdictions, each with 2 patent records. No filings have been identified in the United States, Europe, or other major patent markets.
Electric digital data processing (G06F), AI-based computing (G06N), business and administrative data processing (G06Q), and computational chemistry (G16C) each appear in 2 records. Cement product shaping (B28B) and ceramics and refractories (C04B) each appear in 1 record, confirming a bias toward software and simulation methods.
One co-applicant filing exists: Jianhua Construction Materials (China) Co. Ltd. and Nanjing Institute of Technology filed jointly. This is the only cross-institutional collaboration in the corpus, and it represents an industry-academia pairing focused on simulation-driven digital twin design.
Two areas appear underserved relative to what a complete digital twin would require: real-time sensor integration and process-control feedback during geopolymer production (adjacent to B28B), and long-term durability and degradation simulation (adjacent to C04B). Neither has attracted dedicated filings in the current corpus.
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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.
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