Benesch Pilots AI/ML and Digital Twin Technology to Streamline Traditional Pavement Assessment Workflows
Using Bentley’s iTwin Applications Facilitates Data-centricity, Industrializing Industry Pavement Management Practices
Project Summary:
Most public agency assets, such as bridges and transit networks, include pavement, requiring crack detection survey and maintenance to ensure structural integrity throughout their design life. However, current inspection methods for collecting crack and joint data are time consuming, resulting in shutdowns that negatively impact infrastructure owners and operators, as well as the public.
In order to maximize asset value, and increase efficiency and comprehensiveness, Benesch initiated a research and development project, exploring the integration of artificial intelligence, machine learning, and digital twins for a more data-centric approach to pavement crack detection workflows.
Leveraging iTwin® with artificial intelligence and machine learning technology automated digitization and integration of crack linework data within the digital twin, Benesch saved more than 75% in manual field work. Their success was predicated on harnessing the power of artificial intelligence and machine learning within Bentley’s digital twin applications to manage pavement assets.
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