Author: Denis Avetisyan
A new system leverages artificial intelligence to rapidly generate geotechnical reports from site data and imagery.
This paper details GeoReportIA, a web application employing large language models and prompt engineering to automate the creation of standardized geotechnical reports for rock mass characterization.
Traditional geotechnical reporting relies on manual analysis, introducing potential for error and subjective interpretation in assessing rock mass stability. This paper, ‘Automatización de Informes Geotécnicos para Macizos Rocosos con IA’, presents GeoReportIA, a novel web application utilizing large language models and prompt engineering to automate the generation of these critical reports from field data and imagery. The system demonstrates comparable performance to expert-authored descriptions, achieving BLEU and ROUGE-L scores of 0.455 and 0.653 respectively, while significantly reducing manual effort. Could this approach represent a new standard for efficient and standardized geotechnical data analysis in field geology and engineering?
The Inevitable Fracture: Characterizing Rock Mass
Traditional geotechnical reporting relies on manual analysis – a slow, error-prone process when assessing complex rock formations. This limits project scalability and introduces human error in ground stability assessments. Accurate rock mass characterization is critical for infrastructure safety and stability, yet current workflows struggle to integrate diverse data – imagery, spatial data, and historical records. This fragmentation hinders the creation of reliable geotechnical reports. Monitoring isn’t about preventing failures—it’s the art of fearing consciously.
A unified system capable of automatically synthesizing data would accelerate reporting, improve accuracy, reduce risk, and enhance infrastructure resilience.
Automated Cartography: GeoReportIA
GeoReportIA is a web system automating comprehensive geotechnical reports for rock mass formations. It streamlines site investigation and report creation by integrating data sources and employing artificial intelligence. A core function is automated collection and processing of visual data – image processing of outcrops and samples, integrated with GIS spatial data. This holistic approach, supplemented by the V3Geo virtual 3D model repository, provides a complete understanding of rock mass characteristics.
Built using the Django framework and deployed on Render, GeoReportIA offers a robust, scalable platform for report generation.
The Echo of Prediction: AI-Driven Report Generation
GeoReportIA utilizes the Gemini 1.5 Flash multimodal language model for information synthesis and report generation. Sophisticated prompt engineering refines model instructions, ensuring accurate geological reports. The system employs natural language processing and deep learning to extract insights from visual and spatial data.
Evaluations show BLEU scores between 0.4 and 0.5, and ROUGE-L scores between 0.6 and 0.7, indicating strong coherence and structural similarity to established geological descriptions. A high R² correlation of 0.92 between BLEU and ROUGE-L scores validates report reliability. Development is managed through GitHub, facilitating version control and collaboration.
The Sediment of Progress: Impact and Future Directions
GeoReportIA represents a significant advancement in geotechnical reporting, reducing time, resources, and costs. A key feature is its standardized reporting format, improving data consistency and communication among stakeholders. The system integrates borehole logs, laboratory results, geophysical surveys, and existing site data, enhancing assessment accuracy.
Ongoing development focuses on advanced predictive modeling and risk assessment tools. Future iterations will forecast hazards and optimize foundation designs. Every dependency is a promise made to the past.
The pursuit of automated geotechnical reporting, as detailed within GeoReportIA, reveals a familiar pattern. Systems designed to impose order on complex data—to generate standardized reports from the chaotic input of field work—are ultimately exercises in managed compromise. As Claude Shannon observed, “The most important thing in communication is to convey the meaning, not necessarily the information.” This holds true here; the application doesn’t create understanding of the rock mass, but rather translates observations into a codified form. The elegance of prompt engineering, of coaxing meaning from large language models, is not about eliminating ambiguity, but about choosing which ambiguities to preserve—and which to bury within the frozen architecture of a standardized report. It is a prophecy, gently whispered, that future revisions will inevitably be required, as the rock itself resists neat categorization.
What’s Next?
The automation of geotechnical reporting, as demonstrated by GeoReportIA, isn’t a destination but a divergence. The system’s efficacy isn’t measured by the reports generated, but by the failures it accelerates. Each standardized phrase, each automatically-placed decimal, narrows the field of acceptable variation – precisely the variation that signals emergent, unmodeled conditions. The application treats data as information; it forgets that information is merely a temporary reprieve from entropy.
Future work won’t center on improving the accuracy of the models—a guarantee is just a contract with probability—but on quantifying the nature of their inevitable errors. The true challenge lies not in teaching machines to read rock, but in designing systems that gracefully degrade when confronted with the unreadable. Stability is merely an illusion that caches well.
The logical extension isn’t more data, or larger models, but a deliberate introduction of controlled noise. A system that anticipates its own fallibility, that actively seeks out the edges of its competence, is less a predictive engine and more a cartographer of uncertainty. Chaos isn’t failure—it’s nature’s syntax. The next iteration won’t be about generating reports; it will be about generating questions.
Original article: https://arxiv.org/pdf/2511.04690.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-11-11 00:16