A framework for digital twin of civil infrastructure-challenges and opportunities
Paper i proceeding, 2019

All rights reserved. Digital Twin of an infrastructure is a living digital simulation that brings all the data and models together and updates itself from multiple sources to represent its physical counterpart. The primary focal point of the present study is to propose a framework for Digital Twin of infrastructure and to demonstrate it in the context of a next-generation condition assessment method. The proposed framework is based on the optimized integration of: (1) Structural Inspection: Autonomous Data Collection using drones to minimize intrusion on the transport flow, cover large areas in a minimum of time, access to hard-to-reach areas and minimize exposure to safety hazards for inspectors and users; (2) Damage Quantification: Automated Data Interpretation using data-driven techniques to detect and quantify geometrical and visual anomalies, e.g. cracking and spalling, on the surface and sub-surface of concrete infrastructure; and (3) Performance Prediction: Advanced Structural Simulation combined with physics-based deterioration models to calculate structural performance. The outcome of the study is expected to radically transform the current practices by leveraging drones for inspection, data-driven models for damage quantification, and physics-based models for performance prediction, all seamlessly connected to a living simulation platform "Digital Twin" which updates itself after each inspection round.

Författare

Kamyab Zandi

Chalmers, Arkitektur och samhällsbyggnadsteknik, Konstruktionsteknik

Elliot Harris Ransom

Chalmers, Arkitektur och samhällsbyggnadsteknik, Konstruktionsteknik

Tanay Topac

Stanford University

Ruiqi Chen

Stanford University

Surendra Beniwal

Stanford University

Mattias Blomfors

Chalmers, Arkitektur och samhällsbyggnadsteknik, Konstruktionsteknik

Jiangpeng Shu

Zhejiang University

Fu Kuo Chang

Stanford University

Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring

Vol. 1 1627-1633

The 12th International Workshop on Structural Health Monitoring
Stanford, USA,

Ämneskategorier

Annan data- och informationsvetenskap

Bioinformatik (beräkningsbiologi)

Datorsystem

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Senast uppdaterat

2019-11-13