Namma Metro to implement technology-driven digital inspections for structural health monitoring

BMRCL integrates digital technology for efficient metro infrastructure maintenance, using AI-driven inspections to prevent structural issues and enhance safety.

Bangalore Metro Rail Corporation Ltd. (BMRCL) is gearing up to integrate digital technology to enhance the monitoring of its concrete structures, including viaducts and pillars. This initiative aims to ensure timely maintenance and improve the overall efficiency of metro infrastructure management.

As part of this plan, the BMRCL recently invited tenders for the digital inspection of Reach 1, the metro’s first operational corridor that runs between M.G. Road and Baiyappanahalli. This corridor, which has been serving commuters since 2011, will undergo an advanced technology-driven assessment to detect and evaluate any structural issues.

A senior BMRCL official explained that the new digital inspection system would employ artificial intelligence (AI) and machine learning algorithms to analyse potential structural damage. “The AI-driven system will not only detect defects but also assess their severity, providing real-time alerts to our engineering team. This will allow us to undertake preventive maintenance before minor issues escalate into major concerns,” the official said.

Currently, structural inspections rely on manual techniques such as visual assessments using binoculars, cameras, and hydraulic platforms. These methods are not only time-consuming but also pose operational challenges. With the new technology-based inspection, specialised sensors will collect structural data, which will then be processed using deep learning algorithms and 3D modelling techniques, according to officials.

In 2023, the BMRCL curtailed metro train services at the Trinity station due to maintenance work. Earlier, in 2018, metro services on the Purple Line were disrupted by a “honeycomb formation” or concrete distress in a beam above a pier near the Trinity station. A safety audit of the entire Phase I was also conducted in 2019.

“The system will automatically detect and categorise defects based on their size, type, and location. This will enable our engineers to make data-driven decisions regarding maintenance and repairs,” the official added. The AI-powered evaluation will determine whether issues pose significant risks and suggest corrective actions, such as epoxy sealing or reinforcement.

Additionally, all processed data will be visualised on a cloud-based asset management platform. This digital interface will provide a comprehensive overview of structural health, allowing engineers to track defects over time, generate maintenance schedules, and optimise repair strategies.