GeoAI: An Intelligent Automation Platform for Geotechnical Data Processing, Subsurface Modeling, and Report Generation

Geotechnical engineering practice remains disproportionately labor-intensive at the data processing layer. From raw boring log digitization to subsurface profile interpolation to final report production, the workflow between field investigation and deliverable output is dominated by manual, error-prone transcription that consumes engineering time without adding interpretive value. GeoAI is a purpose-built artificial intelligence platform designed to automate this workflow end-to-end, from boring log ingestion to PE-sealed design reports.
This presentation describes the architecture and technical implementation of GeoAI, a platform organized into integrated modules spanning data characterization, design computation, and report production. The Subsurface Characterization Engine handles boring log parsing — automated extraction of TCP (Texas Cone Penetrometer) and SPT-N blow count data, parenthetical value handling, lab data identification, and measurement mode switching — and uses the parsed data to interpolate stratigraphy between investigation points, producing visual cross-sections that would otherwise require manual drafting. The Design Module performs standard geotechnical computations across foundations, retaining structures, slopes, and pavements. The Design Report Module assembles computed parameters, tabular summaries, and classifications into structured narrative deliverables compliant with agency and client formatting standards, reducing report production from days to hours.
GeoAI represents a new class of engineering automation tools: domain-specific, deterministic where engineering correctness demands it, and designed to augment — not replace — the judgment of licensed geotechnical engineers. The implications for practice efficiency, data quality, and small firm competitiveness are significant, and will be discussed within the broader transformation of civil engineering technology adoption.