Reliability Analysis
Reliability analysis focuses on evaluating the dependability and trustworthiness of systems, particularly in safety-critical applications, aiming to quantify the probability of failure. Current research emphasizes developing efficient and robust methods for reliability assessment across diverse domains, including complex engineering systems, machine learning models (e.g., using neural networks, Bayesian networks, and polynomial chaos expansions), and even psychological assessments from text data. These advancements are crucial for ensuring the safety and reliability of increasingly complex systems in various fields, from aerospace engineering and robotics to AI-driven applications and infrastructure management.
Papers
Highly efficient reliability analysis of anisotropic heterogeneous slopes: Machine Learning aided Monte Carlo method
Mohammad Aminpour, Reza Alaie, Navid Kardani, Sara Moridpour, Majidreza Nazem
Slope stability predictions on spatially variable random fields using machine learning surrogate models
Mohammad Aminpour, Reza Alaie, Navid Kardani, Sara Moridpour, Majidreza Nazem