Timezyx develops Structural Digital Twins that integrate structural health monitoring data with real-time computation for infrastructure asset management. This technology enables infrastructure health prediction and safeguards transportation and utility networks against extreme climate events. By ensuring asset longevity, the platform helps reduce long-term maintenance costs for large-scale infrastructure projects.
Funding
$81.1K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Aging infrastructure faces increasing risks from extreme climate events and requires proactive management to ensure long-term stability and safety. Traditional risk assessment methods often lack the precision and predictive capabilities needed to anticipate and mitigate potential failures effectively.
Solution
Timezyx offers a Structural Digital Twin platform that combines physics-based modeling with AI, specifically Physics-Informed Neural Networks (PINN), to provide advanced risk assessment for infrastructure management. The platform integrates structural health monitoring data with real-time computation to deliver accurate predictions of future infrastructure performance, enabling clients to anticipate issues and make data-informed decisions. By incorporating extreme climate scenarios into the analysis, Timezyx helps identify vulnerabilities and implement adaptive strategies to safeguard assets against rising temperatures, severe weather events, and shifting environmental conditions. The Digital Twin Lab creates a living, data-driven representation of physical structures that envisions potential scenarios and guides strategic actions to fortify infrastructure resilience.
Target Audience
The primary audience includes infrastructure owners, civil engineers, and government entities responsible for managing and maintaining critical infrastructure assets such as bridges, buildings, dams, and wind towers.
Features
- Physics-Informed Neural Networks (PINN) for holistic risk understanding
- Integration of extreme climate scenarios for precise risk assessment
- Structural Health Monitoring (SHM) data integration for real-time insights
- Advanced modeling and analysis to enhance infrastructure resilience
- BIM-compatible data delivery