Digital Twins for Post‐Earthquake Loss Assessment of Building Portfolios

Conventional earthquake impact assessments often rely on broad assumptions and simplified models, which introduce significant uncertainty into building damage predictions. Although recent advances in machine learning, detailed building inventories, and low‐cost sensors have created opportunities for more refined analyses, these tools have not yet been comprehensively evaluated at the portfolio scale. To address this gap, this study presents a digital twin framework validated through comprehensive virtual experiments. The proposed system is designed to integrate ML‐based damage data with real‐time sensor data, thereby reducing the bias and uncertainty inherent in traditional methods. The approach is demonstrated through the development of a detailed digital twin of the Alvalade parish in Lisbon, Portugal, where advanced simulations were used to evaluate building responses under earthquake scenarios. Sensor placement strategies were also examined to further minimize uncertainty. Results from this computational validation demonstrate the theoretical potential for digital twins to substantially improve damage assessment accuracy, providing a framework for future real‐world implementation and operational deployment.