Digital Twin of Transport Infrastructure – combining the power of data & model

Transport infrastructures are the backbone of our society due to that we heavily rely on the uninterrupted availability of the road network, and bridges are the bottleneck of this network. Reliable condition assessment of our aging transport infrastructures are essential to maintain their integrity. Moreover, climate change and extreme climatic events are important threats to the reliability and safety of the network. This has led to a growing demand for more accurate and reliable condition assessment processes – data collection, data analysis and performance prediction. Significant advances in robotics have radically transformed the current practices by leveraging aerial vehicles combined with optical data collection techniques. Given the recent advancements, massive point cloud data from existing structures are to be collected more accurately and more frequently than ever before. Such repository of data may leverage updating leaps in condition assessment processes only if they are analyzed with advanced methods and coupled with performance prediction models.

The overarching goal of the proposed project is to establish an innovative solution in which next-generation data collection, data analysis and performance prediction methods are customized and integrated to the Digital Twin framework for infrastructures. More specifically, the goal is to leverage 3D point could data of concrete structures to quantify cracking and to create and update FE models accordingly, and thus generating a digital twin of the structure. The goal will be realized through the following specific objectives:

Objective 1: To characterize crack pattern (geometry, length and width) from 3D point cloud data

Objective 2: To automate the generation of an FE mesh from 3D point cloud data  

Objective 3: To demonstrate, in real-time, data collection and data analysis leading to FE model of a test object

The complementary expertise of the applicant’s research group Concrete Structures in “developing models for the assessment of structures” and the expertise of Chalmers Data Science Research Center in “advanced methods of analyzing Big Data” will join forces to solve the challenge addressed in this proposal which primarily relates to Area of Advance ICT and secondary to Building Future. By combining the power of data & model through data drive research, we aims to ensure ever more resilient road transportation, and to establish Chalmers as a leading technical university in innovative application of Big Data related to built environment.

Start date 01/01/2019
End date The project is closed: 30/06/2020

Published: Thu 12 Dec 2019.