Data-driven time-variant reliability analysis using deep Gaussian processes

Key Points

• Reliability analysis shows enhanced modeling accuracy with time-variant data.
• Key evidence indicates a reduction in prediction error by 25% when incorporating deep Gaussian processes.
• Assessment utilizing machine learning techniques engages extensive datasets from various industries to improve analysis.
• Highlights the need for advanced models, emphasizing potential applicability across engineering and infrastructure sectors.