Abstract:
Digital Twins integrate computational models with monitoring data to represent structural behaviour and
support the assessment of civil infrastructure. Achieving sustained synchronisation between physical structures
and their digital counterparts requires these systems to adapt to changing structural conditions and evolving
data availability. A Self-Generating Digital Twin (SGDT) is conceived as a full realisation of a true Digital
Twin: one that continuously updates, learns and evolves through autonomous data assimilation and model
regeneration. Within this framework, incomplete monitoring data present a critical challenge, potentially
compromising the information required for reliable structural-response estimation and model updating. This
seminar presents research undertaken through the I-X Global Early-Career AI Fellowship at Imperial College
London, investigating AI-enabled reconstruction of missing monitoring data to address this challenge. Using
experimental earthquake-response measurements from a three-storey cross-laminated timber rocking-wall
structure, the study evaluates reconstruction under random data loss and contiguous sensor outages. Conventional approaches, including linear interpolation and Gaussian process regression, are compared with generative methods, including TimeGAN and conditional diffusion, across different missing-data patterns and severities. Performance is assessed through both the fidelity of reconstructed response histories and their influence on subsequent Digital Twin predictions. The study evaluates whether accurate reconstruction of missing measurements preserves the reliability of structural response estimates by integrating the recovered data into a fixed data-assimilative Digital Twin. By connecting reconstruction quality with downstream engineering
performance, the study assesses the contribution of data imputation to resilient operation within the SGDT
framework. The seminar will discuss methodological capabilities, preliminary findings and current limitations,
alongside future directions for uncertainty-aware reconstruction to support autonomous data assimilation and
model regeneration under degraded sensing conditions.
Bio:
Dr Shady Adib is a civil engineering researcher who recently completed an I-X Global Early-Career AI
Fellowship at Imperial College London, working with Dr Christian Málaga-Chuquitaype. He holds a PhD
in Civil Engineering from Newcastle University, where he developed a hybrid Digital Twin framework
for real-time structural damage identification. His current research focuses on advancing resilient and
autonomous Digital Twin systems for civil infrastructure, with particular emphasis on Self-Generating
Digital Twins (SGDTs). His work integrates artificial intelligence, physics-based modelling and data
assimilation to support continuous learning, adaptation and model regeneration, with the aim of enabling
reliable structural assessment and engineering decision-making under changing conditions and incomplete
or uncertain monitoring data.