Machine Learning for Engineering

Machine learning models that solve engineering challenges using spatial data, historical project data, and domain expertise. From site assessment to design optimization.

• Predictive models for infrastructure planning and design, from site assessment to foundation requirements
• Integration of engineering datasets and spatial information with historical project data and domain expertise
• Custom machine learning solutions tailored to specific engineering challenges and workflow requirements
• Data-driven approaches to reduce uncertainty, optimize decisions, and improve project outcomes

Building intelligent engineering systems that integrate data analysis, modeling, and automation into cohesive workflows. Developing toward comprehensive AI-assisted engineering platforms.

• Integration of analysis, modeling, and documentation workflows into cohesive engineering platforms
• Open formats and components to avoid vendor lock-in while ensuring interoperability and future flexibility
• Traceable processes with full visibility and reviewable steps for quality assurance and compliance
• Modular systems that can evolve with changing engineering needs and technological advances

Research and consulting to bring machine learning and data-driven methods into infrastructure engineering work. Plan, prototype, and roll out with clear milestones, quality checks, and handover.

• Strategy, architecture, and governance: build/buy/partner choices; data, models, and safety guardrails; metrics and oversight
• Pilots and enablement: scoped experiments with clear success criteria, phased validation, training, and workflow change
• Active research in geotechnical ML applications and academic collaboration opportunities
• Currently developing: Advanced foundation prediction systems for infrastructure companies