Tejas Admane

An integrated probabilistic and machine learning framework for slope stability assessment under uncertainty

A drift-aware explainable artificial intelligence framework for dynamic construction risk governance using multi-domain fuzzy inference and Bayesian updating

An integrated DXAI framework that combines fuzzy inference, Bayesian updating, temporal risk drift analysis and explainable governance support for dynamic construction risk management is introduced.

Detectability of short-term associations between ambient air pollution and cardiovascular morbidity: An ecological time-series analysis under heterogeneous atmospheric conditions

Introduction: Short-term associations between ambient air pollution and cardiovascular morbidity remain inconsistent across different settings. This study evaluated these associations using Generalized Additive Models (GAMs) and examined the robustness of the findings through lag and sensitivity analyses. Materials an...

Adaptive structural–operational resilience intelligence for BIM-enabled infrastructure systems: a causal graph neural and explainable AI framework for hidden vulnerability detection and disaster-resilient governance

Methodologically, it advances BIM-enabled resilience intelligence by integrating causal reasoning, multiplex graph learning, Bayesian uncertainty quantification and XAI within a unified framework for proactive disaster-resilient infrastructure governance and decision support.