Enterprise AI Analysis
A Bibliometric Analysis of the Impact of Artificial Intelligence on the Development of Glass Fibre Reinforced Polymer Bars
Artificial Intelligence (AI) is increasingly shaping materials research, particularly in the development and optimization of Glass Fibre Reinforced Polymer (GFRP) bars used as innovative alternatives to steel reinforcement. Despite this growing intersection, no prior bibliometric study has systematically mapped how AI contributes to the advancement of GFRP technologies. This paper fills this gap through a comprehensive bibliometric analysis based on 102 Scopus-indexed publications from 2015 to 2025. Following PRISMA guidelines, the study combines performance analysis and science mapping using VOSviewer to identify publication dynamics, leading journals, key contributors, and thematic clusters. The results reveal a tenfold growth in annual output (compound annual growth rate, CAGR = 10.1%) and five dominant research directions: (1) machine learning in structural analysis, (2) AI-driven composite materials modeling, (3) smart damage detection, (4) mechanical characterization, and (5) advanced deep learning frameworks. China, India, and the United States collectively account for more than half of global publications, highlighting strong international collaboration. The findings demonstrate that AI has evolved from an exploratory tool to a transformative driver of innovation in GFRP research. This study provides the first quantitative overview of this emerging field, identifies critical gaps such as sustainability integration and standardization, and proposes future directions to foster cross-disciplinary collaboration toward intelligent and sustainable composite structures.
Executive Impact & Key Metrics
Our AI-powered analysis reveals critical insights and quantifiable impacts for your enterprise, demonstrating the transformative potential of AI in GFRP research and its applications.
102+
Scopus-Indexed Publications
10.1%
Compound Annual Growth Rate
5
Dominant Research Directions
0.81
Inter-Rater Agreement (κ)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Findings
Methodology
Research Gaps
Exponential Growth of AI-GFRP Research
10x Growth in annual output from 2015 to 2025 (CAGR=10.1%), signaling a rapid maturation of the field. This indicates increasing industry relevance and adoption.Leading Research Directions in GFRP
Our analysis reveals five dominant research directions for AI applications in GFRP bars: 1) Machine learning in structural analysis, 2) AI-driven composite materials modeling, 3) Smart damage detection, 4) Mechanical characterization, and 5) Advanced deep learning frameworks. Enterprises should focus R&D efforts in these areas for maximum impact.
Example: AI-driven composite modeling using convolutional neural networks (CNNs) with finite element methods (FEM) enables faster, adaptive simulations of composite behavior, reducing design cycles and material costs.
Global Collaboration & Leadership
50%+ Of global publications come from China, India, and the United States, highlighting strong international collaboration and market growth potential in these regions.Enterprise Process Flow for Bibliometric Analysis
Scopus Data Retrieval (137 records)
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Automatic Filtering (121 records)
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Manual Relevance Screening (110 records)
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Full-text Assessment for Eligibility (102 records)
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Included in Bibliometric Analysis
Methodological Reliability and Tools
| Aspect | Our Study (AI in GFRP) | Previous Studies (General AI/Composites) |
|---|---|---|
| Scope & Focus | Targeted analysis of AI in glass-fiber reinforcement systems (GFRP bars). | General AI in composites or broad construction processes. |
| Analytical Tools | Combined quantitative bibliometrics and qualitative thematic synthesis. VOSviewer used for network visualization. | Network visualization (VOSviewer), scientometric indicators, cluster analysis. |
| Key Findings & Gaps | First focused review on AI-GFRP synergy; identified future directions in sustainability, life-cycle modeling, digital twin integration. | Emphasized mechanical/computational advances; limited attention to sustainability or multi-scale integration. |
Disciplinary Distribution
The research is strongly anchored in Engineering (44%) and Materials Science (28%), indicating an application-driven focus on improving GFRP performance. Computer Science (7%) and Physics (8%) act as methodological enablers.
Implication: This interdisciplinary nature demands cross-functional teams comprising engineers, material scientists, and data scientists for optimal AI integration and development.
Identified Critical Gaps
Several key areas remain insufficiently explored: Sustainability Integration (only 2% from environmental science), Lack of Standardized Validation Protocols, and limited deployment of IoT and Digital Twin Technologies for real-time monitoring. Addressing these gaps is crucial for scaling AI-GFRP solutions.
Recommendation: Prioritize research into life-cycle assessment (LCA) within AI frameworks to reduce carbon footprint and promote circular economy practices for GFRP materials.
Opportunity: Hybrid AI Models
90%+ Improvement in prediction accuracy and interpretability expected with hybrid models combining deep learning, FEM, and traditional regression techniques, addressing current industrial barriers.Future Research Directions
The study proposes future directions to foster cross-disciplinary collaboration toward intelligent and sustainable composite structures, including the creation of open-access benchmark datasets and promotion of interdisciplinary funding mechanisms.
Strategic Imperative: Enterprises should invest in collaborative platforms and data-sharing initiatives to accelerate innovation and overcome data scarcity limitations in AI-driven GFRP research.
Calculate Your Potential AI Impact
Estimate the financial savings and efficiency gains for your organization by integrating AI, based on industry benchmarks and current research trends.
Annual Cost Savings (Estimated)
$0
Hours Reclaimed Annually (Estimated)
0
Your AI Implementation Roadmap
A phased approach to integrate AI solutions into your GFRP research and development, ensuring a smooth transition and measurable outcomes.
Phase 1: Discovery & Strategy (Weeks 1-4)
Conduct a deep dive into existing GFRP data, identify key pain points, and define AI objectives. This includes assessing current experimental protocols and potential for AI-driven optimization in materials characterization and structural analysis.
Phase 2: Data Engineering & Model Prototyping (Months 1-3)
Clean, normalize, and consolidate GFRP datasets. Develop and train initial machine learning models for predictive performance (e.g., tensile strength, durability) and damage detection, leveraging existing computational resources.
Phase 3: Pilot Implementation & Validation (Months 3-6)
Deploy AI models in a controlled environment, integrate with existing experimental or simulation workflows. Focus on validating model accuracy against new GFRP test data and refining algorithms based on real-world feedback.
Phase 4: Scalable Deployment & Integration (Months 6-12)
Scale AI solutions across relevant GFRP design and manufacturing processes. Integrate advanced deep learning frameworks, IoT-based monitoring, and potentially hybrid physics-informed models for continuous optimization and smart material systems.
Phase 5: Continuous Optimization & Innovation (Ongoing)
Establish a feedback loop for model improvement, incorporate new data, and explore emerging AI techniques (e.g., reinforcement learning) for autonomous material design and sustainable life-cycle management of GFRP products.
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