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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | https://arxiv.org/abs/2603.01022 |
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| _version_ | 1866918363844837376 |
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| author | Bekele, Yared W. |
| author_facet | Bekele, Yared W. |
| contents | Analytical methods underpin geotechnical engineering practice, yet their implementation remains fragmented across error-prone spreadsheets and opaque proprietary software. While Large Language Models (LLMs) offer transformative potential for streamlining engineering workflows, their statistical nature fundamentally conflicts with the strict determinism required for safety-critical calculations. Their tendency to hallucinate formulas, misinterpret units, or alter methodologies between sessions creates a critical trust gap. This paper introduces GeoMCP, a framework built to bridge this gap via a key insight: engineering methods should be represented as structured data, not embedded code. GeoMCP captures analytical methods as "method cards", declarative JSON files defining formulas, units, applicability limits, and literature citations. A constrained symbolic engine executes these cards with verified dimensional consistency, while structured "Agent Skills" guide LLMs to apply engineering judgment and orchestrate the analysis. By exposing these verified capabilities through the Model Context Protocol (MCP), GeoMCP shifts the role of the AI from an unreliable calculator to an intelligent orchestrator. Validated against an official JRC Eurocode~7 worked example, the framework demonstrates computational parity with traditional approaches while ensuring complete mathematical transparency. Ultimately, GeoMCP provides a blueprint for transitioning the industry from isolated legacy software to an interoperable, AI-ready ecosystem where engineers can leverage modern AI without surrendering professional responsibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GeoMCP: A Trustworthy Framework for AI-Assisted Analytical Geotechnical Engineering Bekele, Yared W. Computational Engineering, Finance, and Science Analytical methods underpin geotechnical engineering practice, yet their implementation remains fragmented across error-prone spreadsheets and opaque proprietary software. While Large Language Models (LLMs) offer transformative potential for streamlining engineering workflows, their statistical nature fundamentally conflicts with the strict determinism required for safety-critical calculations. Their tendency to hallucinate formulas, misinterpret units, or alter methodologies between sessions creates a critical trust gap. This paper introduces GeoMCP, a framework built to bridge this gap via a key insight: engineering methods should be represented as structured data, not embedded code. GeoMCP captures analytical methods as "method cards", declarative JSON files defining formulas, units, applicability limits, and literature citations. A constrained symbolic engine executes these cards with verified dimensional consistency, while structured "Agent Skills" guide LLMs to apply engineering judgment and orchestrate the analysis. By exposing these verified capabilities through the Model Context Protocol (MCP), GeoMCP shifts the role of the AI from an unreliable calculator to an intelligent orchestrator. Validated against an official JRC Eurocode~7 worked example, the framework demonstrates computational parity with traditional approaches while ensuring complete mathematical transparency. Ultimately, GeoMCP provides a blueprint for transitioning the industry from isolated legacy software to an interoperable, AI-ready ecosystem where engineers can leverage modern AI without surrendering professional responsibility. |
| title | GeoMCP: A Trustworthy Framework for AI-Assisted Analytical Geotechnical Engineering |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2603.01022 |