IIT Mandi develops AI-based structural health monitoring
Technology

IIT Mandi develops AI-based structural health monitoring

Researchers at the Indian Institute of Technology (IIT) in Mandi, in collaboration with France's National Institute for Research in Digital Science and Technology (INRIA), have made significant strides in the field of structural health monitoring (SHM) by harnessing the power of artificial intelligence (AI) and advanced signal processing techniques. Their innovative approach utilizes AI algorithms to accurately predict the structural health of bridges and other critical infrastructure, marking a substantial departure from traditional, manual inspection methods.

The application of these AI-based algorithms extends well beyond bridges and can be adapted for assessing the health of various structures, including ropeways, buildings, aerospace structures, transmission towers, and other components of essential infrastructure that require regular health assessments and protective measures.

Structures like bridges are subjected to natural ageing processes due to environmental factors such as temperature fluctuations, exposure to water and air, and the added stress of heavy road traffic. Traditionally, assessing the condition of bridges has heavily relied on visual inspections, which are often deemed inadequate by experts in the field. Visual inspections are subjective, time-consuming, and involve manual analysis of numerous photographs. As such, they fall short of detecting all structural issues, which can be detrimental to ensuring the safety and reliability of these vital structures.

The recent breakthrough achieved by the researchers at IIT Mandi and INRIA leverages recent advances in instrumentation, data analysis, and AI tools like deep learning to enhance structural health monitoring. These technologies facilitate the detection, measurement, understanding, and prediction of defects in structures over time. Consequently, they enable more effective planning for renovation or repair work, ultimately reducing maintenance costs and extending the lifespan and availability of bridges and other infrastructure.

The team at IIT Mandi has developed a Deep Learning-based SHM approach that relies on AI algorithms to identify and isolate structural damages by analyzing recorded ambient dynamic responses without requiring human intervention. This innovative method is based on data-driven techniques such as Machine Learning, AI, and Bayesian statistical inference, which estimate a bridge's health and predict its remaining usable life. This outcome has the potential to reduce risks to infrastructure, particularly under operational and adverse loading conditions.

One critical aspect considered in the AI-based SHM approach is the impact of temperature fluctuations on a bridge's dynamic traits, especially in structures like prestressed concrete and cable-stayed bridges. The algorithm developed by IIT Mandi was rigorously tested on a real bridge located in a cold region with extreme annual and daily temperature swings. The results demonstrated its effectiveness in identifying structural damage caused by various factors, including temperature fluctuations.

In another related study, the researchers employed advanced filtering techniques to assess the condition of different structural components without the need for direct measurement of their connections. This technique allows for the separate assessment of each component's health, aiding in the evaluation of overall structural integrity. Through computer simulations and extensive testing, the researchers verified the method's robust performance, even in the presence of background noise and varying levels of damage severity.

This groundbreaking research not only advances the field of structural health monitoring but also paves the way for safer, more efficient, and cost-effective maintenance and repair of critical infrastructure, benefiting society as a whole.

Researchers at the Indian Institute of Technology (IIT) in Mandi, in collaboration with France's National Institute for Research in Digital Science and Technology (INRIA), have made significant strides in the field of structural health monitoring (SHM) by harnessing the power of artificial intelligence (AI) and advanced signal processing techniques. Their innovative approach utilizes AI algorithms to accurately predict the structural health of bridges and other critical infrastructure, marking a substantial departure from traditional, manual inspection methods.The application of these AI-based algorithms extends well beyond bridges and can be adapted for assessing the health of various structures, including ropeways, buildings, aerospace structures, transmission towers, and other components of essential infrastructure that require regular health assessments and protective measures.Structures like bridges are subjected to natural ageing processes due to environmental factors such as temperature fluctuations, exposure to water and air, and the added stress of heavy road traffic. Traditionally, assessing the condition of bridges has heavily relied on visual inspections, which are often deemed inadequate by experts in the field. Visual inspections are subjective, time-consuming, and involve manual analysis of numerous photographs. As such, they fall short of detecting all structural issues, which can be detrimental to ensuring the safety and reliability of these vital structures.The recent breakthrough achieved by the researchers at IIT Mandi and INRIA leverages recent advances in instrumentation, data analysis, and AI tools like deep learning to enhance structural health monitoring. These technologies facilitate the detection, measurement, understanding, and prediction of defects in structures over time. Consequently, they enable more effective planning for renovation or repair work, ultimately reducing maintenance costs and extending the lifespan and availability of bridges and other infrastructure.The team at IIT Mandi has developed a Deep Learning-based SHM approach that relies on AI algorithms to identify and isolate structural damages by analyzing recorded ambient dynamic responses without requiring human intervention. This innovative method is based on data-driven techniques such as Machine Learning, AI, and Bayesian statistical inference, which estimate a bridge's health and predict its remaining usable life. This outcome has the potential to reduce risks to infrastructure, particularly under operational and adverse loading conditions.One critical aspect considered in the AI-based SHM approach is the impact of temperature fluctuations on a bridge's dynamic traits, especially in structures like prestressed concrete and cable-stayed bridges. The algorithm developed by IIT Mandi was rigorously tested on a real bridge located in a cold region with extreme annual and daily temperature swings. The results demonstrated its effectiveness in identifying structural damage caused by various factors, including temperature fluctuations.In another related study, the researchers employed advanced filtering techniques to assess the condition of different structural components without the need for direct measurement of their connections. This technique allows for the separate assessment of each component's health, aiding in the evaluation of overall structural integrity. Through computer simulations and extensive testing, the researchers verified the method's robust performance, even in the presence of background noise and varying levels of damage severity.This groundbreaking research not only advances the field of structural health monitoring but also paves the way for safer, more efficient, and cost-effective maintenance and repair of critical infrastructure, benefiting society as a whole.

Next Story
Infrastructure Urban

Afcons shares gain momentum with Bhopal Metro Line 2 project

Afcons Infrastructure made a quiet debut on the stock market on Monday but quickly gained momentum after emerging as the lowest bidder for the Bhopal metro project line 2 package.The Bhopal Metro Phase 1 project’s 12.915 km Blue Line (Line-2) will link Bhadbhada Chauraha and Ratnagiri Tiraha, spanning 13 elevated stations. This package, issued by the Madhya Pradesh Metro Rail Corporation (MPMRCL), involves constructing all 13 stations of the Blue Line’s viaduct. The depot will also be shared with the Orange Line (Karond Circle to AIIMS) for maintenance and stabling purposes.Afcons’ exten..

Next Story
Infrastructure Transport

Locals urge CM to opt for road bypass over flyover at Dandeavaddo

Chinchinim villagers respectfully requested Pramod Sawant, Chief Minister, to instruct the Public Works Department (PWD) and the National Highway authorities to construct a road bypass instead of a flyover on the Dandeavaddo stretch of NH66. The villagers, led by Sarpanch Frank Viegas and Velim MLA Cruz Silva, also raised the long-standing issue of building the Chinchinim Panchayat Ghar and a multi-purpose project on panchayat land that was acquired more than 15 years ago. The delegation met the Chief Minister at the St. Sebastian Chapel junction in Chinchinim as he was returning home from a..

Next Story
Infrastructure Transport

MLA Yashpal Suvarna Announces Malpe-Udupi Highway Construction from Nov 6

MLA Yashpal Suvarna instructed officials to commence work on the Malpe-Udupi Highway on Wednesday. A meeting took place, attended by MP Kota Shrinivas Poojary, Udupi MLA Yashpal Suvarna, and Kaup MLA Suresh Shetty Gurme, to discuss the National Highway 169A project, which spans from Malpe to Udupi, covering areas like Hiriyadka, Parkala, and Perdur. The project had experienced delays due to incomplete land acquisition, but compensation notices have now been issued to the landowners. Of the 214 land acquisition files, 19 pertain to government land, while 195 involve private owners. Notices ha..

Hi There!

"Now get regular updates from CW Magazine on WhatsApp!

Join the CW WhatsApp channel for the latest news, industry events, expert insights, and project updates from the construction and infrastructure industry.

Click the link below to join"

+91 81086 03000