Resiliency in Geotechnical Asset Management: A Risk-Based Quantitative Approach-PPT Presentation

PPT Presentation

Pdf Summary

The presentation describes a risk-based, quantitative approach to improving resilience in geotechnical asset management. Extreme weather, climate change, rising demand, and asset vulnerability can accelerate deterioration and cause costly disruptions. Resilience means anticipating and withstanding hazards, limiting their impacts, and restoring service within an acceptable time.<br /><br />Resilience depends on several related qualities: robustness, rapid recovery, resourcefulness, and redundancy. Assessments should consider not only immediate damage and recovery time, but also cumulative damage, remaining service life, and gradual reductions in acceptable performance. Geotechnical assets include slopes, embankments, retaining structures, tunnels, culverts, and foundations.<br /><br />At the asset level, resilience should inform site investigation, design, maintenance, and project selection. Designs should account for uncertainty in loads and resistance, all relevant limit states—including serviceability—and recovery after disruption. At the network level, the goal is to maintain overall system function. Because resources are limited, agencies must weigh risk, cost, time, and return on investment when prioritizing improvements.<br /><br />The proposed assessment framework includes risk-based data collection; risk and vulnerability analysis; modeling asset and system response and cumulative damage; measuring resilience at multiple levels; and making decisions under uncertainty. Long-term strategies include strengthening assets, improving network redundancy and maintenance, and reducing consequences through supporting infrastructure.<br /><br />The presentation also highlights the computational demands of forecasting performance, capacity, risk, response, and maintenance effectiveness. Knowledge-guided machine learning—combining observations with physical-model outputs—could improve predictions, organize geotechnical data, capture uncertainty, and support better investment decisions. Shared databases across projects and regions would help advance these methods.

Keywords

geotechnical asset management

resilience

risk-based assessment

quantitative analysis

extreme weather

climate change

asset vulnerability

cumulative damage

network redundancy

machine learning