Inthe upcoming fourth industrial revolution, technologies such as SHM are believed to have significant contributions in shaping the ways to respond against adverse structural changes, improving structural reliability and life cycle management.
Most of the existing infrastructure across the world is in a stage that demands extensive maintenance and management. However, the human resources needed for such maintenance can be huge. This is where structural health assessment or SHM comes to play. It is an important tool to get an idea of the location and extent of damage in any aging infrastructure in an automated way and can help in minimizing maintenance costs and extend service life.
Other important applications of SHM are in the aerospace sector, the manufacturing of wind turbine systems, civil engineering, architecture, mechanical, and marine systems. Carefully applied SHM can help determine the real-time health performance of an aircraft.
Whether the structure will survive based on damage severity is a very important aspect of aircraft maintenance/manufacturing. Other factors that can be monitored using SHM are dynamic strain, damage due to impact, loads on wings, detection of cracks and deformation, composite landing gears, and pressure value in the hydraulics system.
In the case of wind turbine systems, SHM helps designers to design rotors with reduced weight and can replace conservative design ideas with careful and precise automation. Information about loads and the damage arising from these loads might help manufacturers to improve the wind turbine designs and manufacturing and subsequent quality control processes.
SHM can be based on passive and active responses. Passive involves condition monitoring, where the periodic response received is compared with the available data to detect any abnormal effects in signal which may have its source from changes in the structural integrity.
On the other hand, monitoring the signal and giving feedback to the system with respect to changes in signal is an active response. The extraction of the features that potentially gives the idea of structural damages from these measurements and the subsequent statistical analysis of these features helps to determine the current health state of the structure concerned.
Even though it has been found that vibration-based inspection methods are better than non-destructive testing (NDT) methods for SHM, they are not fully developed. Therefore, they need to be used in conjunction with various NDT to fully understand the health of a structure.
For example, when a heavy vehicle crosses a bridge with structure defects, the new sensor-based technology converts the elastic waves emitted by the damaged area in the bridge to visualized data. The sensor collects data from the portion of the bridge where it is attached using the AE method.
Acoustic emission is the emission of acoustic/elastic waves from solids when there are internal structural changes happening in the materials, including crack formation or plastic deformation due to aging, temperature change gradient or external mechanical forces.
Several local sources of elastic waves are created due to these structural changes, which, in turn, results in small surface displacements in a material. In this way, the accumulated elastic energy in a material is released. In structural health monitoring, AE is used to detect the type and location of the internal damage.
The usage of raindrops as a source of elastic waves would decrease the overall data collection time for the sensing process of damage detection and location. This has opened up the possibility to explore a previously untested domain of sensor technology using AE, where during heavy rain, every raindrop could be used to sense deterioration, even in areas with reduced traffic flow.
Takashi Usui, a Research Scientist at Toshiba has mentioned the possibility of extending the concept of using raindrops distribution in SHM to monitor other social infrastructures such as roads, buildings, and even large-scale industrial machinery in the near future.
However, before increasing the scalability of this technology in other structural maintenance and improvement, considerable work still needs to be performed for verification tests through the collection and analysis of data. This will help to improve the overall reliability of the system.
Ankita Biswas is in the final phase of their Masters in Materials Science and Simulation at the Interdisciplinary Centre for Advanced Materials Simulations, Ruhr-University Bochum, Germany. Ankita has carried out their Bachelor's degree in Ceramic Engineering from the West Bengal University of Technology, Kolkata, India.
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But now, the country is faced with the challenge of monitoring the structural integrity of all these physical assets, many of them have deteriorated significantly. According to official estimates, about 25% of bridges over 2 metre in length have passed the 50-year mark in 2018, with this number of the percentage going up to 63% by 2033.
As structural monitoring of existing facilities has become a national imperative, Toshiba has developed a new system for structural health monitoring. The system deploys sensors that help visualise the interior health of structures and allows for efficient and comprehensive inspections.
The most significant aspect of Toshiba's structural health monitoring system is the brand-new sensing technology, which allows the system to visualize damage on the inside of the concrete, in areas where workers cannot see directly, and determine the level of deterioration. This technology provides the opportunity to prioritise bridges that needs urgent maintenance, and helps better the use of limited labour and budget.
The new sensing technology is elastic waves, a wave emitted by miniscule forms of damage in structures. The vibrations induced when a vehicle drives on the bridge causes several dozen to several hundred kHz worth of elastic waves to be emitted from cracks in the structures. The sensor measures these waves from where it is attached to the deck of the bridge, using a test called AE Method.
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