At the core of GeoLiquefy is a domain-aware AI system trained to understand geotechnical behavior, seismic stresses, and soil dynamics with scientific precision.
We combine the rigor of traditional geotechnical engineering with the power of modern AI to create a system that respects physical laws while capturing complex non-linearities.
A multi-layered system that combines domain expertise with deep learning to deliver accurate, interpretable predictions.
SPT, CPT, Vs, groundwater depth, fines content, seismic parameters
Domain-aware fine-tuned LLM incorporating geotechnical equations, soil mechanics, and empirical relationships
Safety factors, liquefaction triggering formulations, Seed & Idriss logic validation
Multi-model ensemble, confidence quantification, uncertainty bounds
Step-by-step reasoning, parameter sensitivity analysis, feature attribution
Final liquefaction probability, risk classification, engineering explanation
Our models are trained on the most comprehensive liquefaction dataset ever assembled, with rigorous quality control and domain-balanced sampling.
Our models are rigorously evaluated against classical methods and real-world earthquake data to ensure reliability and accuracy.
| Metric | Traditional Methods | GeoLiquefy AI | Improvement |
|---|---|---|---|
| Overall Accuracy | 78% | 94% | +16% |
| False Negative Rate | 18% | 4% | -14% |
| False Positive Rate | 24% | 8% | -16% |
| Confidence Calibration | N/A | 0.92 | New |
| Processing Time | ~30 min | <1 sec | 1800x faster |
Japan • Mw 9.1
Validated against 2,400+ field observations
New Zealand • Mw 6.3
Extensive CPT database validation
Taiwan • Mw 7.6
Cross-validated with SPT records
Japan • Mw 7.5
Historical case study validation
"Our models have been reviewed and validated by leading geotechnical experts including Prof. Ashok Kumar Gupta, Prof. Robb Moss, and Prof. Saurabh Rawat, ensuring scientific rigor and practical applicability."
GeoLiquefy is not a black box. Every prediction comes with step-by-step reasoning, parameter sensitivity analysis, and engineering-readable explanations.
Our models generate human-readable reasoning chains that show exactly how input parameters lead to final predictions.
Understand which soil and seismic parameters have the greatest influence on liquefaction risk for any given site.
Break down the final probability into contributing factors: soil strength, seismic demand, and site conditions.
Every output includes plain-language explanations that engineers can include directly in reports.
Engineering ethics and safety are at the core of our platform. We maintain transparent assumptions, clear error boundaries, and human-in-the-loop validation.
All model assumptions, limitations, and applicability ranges are clearly documented and communicated to users.
Confidence intervals and uncertainty bounds are provided for every prediction to support risk-informed decision making.
The system alerts users when input parameters fall outside validated ranges or when predictions may be unreliable.
Critical decisions require professional engineering review. Our platform augments, not replaces, human expertise.
Minimum confidence thresholds ensure predictions meet engineering standards before being presented to users.
Model performance is continuously tracked against new earthquake data to detect and correct any drift or degradation.
A comprehensive deep dive into our model architecture, training methodology, scientific validation, and performance benchmarks.
Download the White Paper →We're pushing the boundaries of what's possible in geohazard prediction, working toward a future where every city has real-time, AI-powered resilience intelligence.
Japan-specific, California-specific, and New Zealand-specific models that capture local geological characteristics and seismic patterns.
High-resolution liquefaction risk maps for entire metropolitan areas, updated in real-time as new data becomes available.
Integrated prediction of liquefaction, lateral spreading, ground deformation, and settlement in a unified framework.
Incorporating borehole logs, GIS layers, satellite imagery, and historical seismic records for richer context.
Real-time integration with city digital twins for scenario planning and infrastructure resilience assessment.
Probabilistic forecasting of seismic risk over decades, accounting for climate change and urban development patterns.