Asset Management for Extreme Weather Risks

For industries with networks of distributed assets, like electric and gas utilities, extreme weather patterns due to climate change are creating new challenges for asset management, infrastructure damage, and lost operations and production. While extreme weather events (EWEs) like tropical storms, hurricanes, and cyclones raise the risk of infrastructure failure through flood and wind damage, more frequent and severe droughts are increasing temperatures and fuel loads, heightening the risk of an asset failure leading to a catastrophic wildfire. Because past weather patterns are no longer as predictive of future ones, stakeholders need new asset management techniques for assessing, predicting, and reducing the risk of asset damage from EWEs and for quantifying the risk of infrastructure failures leading to wildfires, flooding, hazardous waste spills, or other catastrophic outcomes.

Exponent can help clients assess and reduce the risk of asset failure related to EWEs by identifying vulnerabilities in their existing infrastructure and developing a robust, quantitative, predictive asset management program. Our team of engineers, metallurgists, data scientists, and other technical professionals was the first to apply quantitative risk modeling using Bayesian updating for management of distributed asset networks and to develop an asset modeling tool with degradation modeling to bolster asset resiliency against EWEs. Our quantitative risk metrics help clients improve asset management by structuring a plan for timely asset repairs, replacements, and upgrades. Using preexisting data sets, we can also perform risk-informed operational assessments to help clients determine when it might be too risky to operate their equipment.
Exponent’s infrastructure asset management team offers the following services:

  • Evaluation of climate-related hazards
  • Vulnerability assessment relative to climate-related hazards
  • Conditions assessment to establish present-day baselines
  • Degradation modeling to account for climate-related changes in asset threats
  • Component testing and failure analysis
  • Infrastructure inspection planning
  • Bayesian updating/analysis, calibrating theoretical models based on real-life data.