Over the last decade, civil infrastructure has remained susceptible to a significant loss in functionality due to structural deficiencies. It is mainly due to the deterioration in material quality and the lack of structural health monitoring (SHM). The global structural infrastructure failures, like the Nanfangao tied-arch bridge in Taiwan, the Wuxi National Highway in China, and the Mexico City metro overpass collapse in 2021, raised the importance of SHM.
In the publication of IoPScience, it is found that SHM remains the topic of research worldwide to prevent catastrophic failures and provide engineers with quantitative data.
Today, civil infrastructure authorities implement a structural health monitoring system to make decisions on structural maintenance, repair, and rehabilitation efficiently.
Compared to conventional SHM systems, which are mainly used as a tool for damage assessment, new-generation structural monitoring systems leverage advanced technologies like AI, machine learning, etc. With the Internet of Things (IoT) and data mining as core approaches to implementing SHMs, civil engineers overcome various challenges, like complex quantitative analysis.
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New-Gen Structural Health Monitoring
New-generation structural health monitoring automates the condition assessment of engineered systems. It provides valuable insights into cutting-edge advancements in SHM structures and materials while minimising the risk of structural damage. A recent study by Research Gate found that different new-gen SHMs have various requirements, including frequency range of interest, monitoring method, functional participation, etc.
As a result, most engineers face a challenge in bridge monitoring in the absence of time.
Conventional structural damage detection techniques are replaced with new-generation SHMs, which are more efficient and less time-consuming. Today, real-time assessment and automated monitoring of bridges and highways allow engineering to mitigate the risk of structural damage.
As per the publication in Sage Journals, in the age of smart engineering, technologies like big data analytics provide the necessary tools to augment the capabilities of SHMs and provide intelligent solutions.
A Step Forward to SHM Lead By the New Generation
The new-generation structural health monitoring roadmap is mainly organised into four sections: surface sensing, distributed embedded sensing systems, multifunctional materials, and remote sensing. As per the Measurement Science and Technology article, most of the sensing technologies might overlap and cause slight differences in assessment values, which is crucial to understanding engineers.
Fibre Optic Strain Sensors (FOSS): The new age structural health monitoring is backed by fibre optic strain sensors that bring transformative possibilities to the structural assessment process. The long-gauge FOSS enables accurate measurement in homogeneous materials like concrete and provides greater spatial coverage per unit sensor.
Acoustic Emission Technologies: Acoustic emission is crucial for new-gen SHM, aligning with non-destructive testing. It is conducted in an event where spontaneous emissions of sound waves from the material are subject to external stress. It is a simple structural health monitoring concept that can provide nearly accurate results. Today, it is demonstrated in wide applications for risk assessment.
Radiofrequency Sensors: One of the fundamental challenges for engineers is detecting and localising critical damage to civil infrastructure. Radiofrequency sensors in SHM systems are efficient and detect structural damage while suffering from limited spatial coverage. As per the data from Science Direct, radio frequency sensors are widely implemented by government agencies to bring efficiency to structural health monitoring.
Vibration-Based Structural Health Monitoring
In the National Centre for Biotechnology Information study, a team of researchers used ML techniques and developed numerous vibration-based damage assessment methods. For large-scale structures, this SHM technique introduces new horizons and facilitates the acquisition of a large set of data from different sensors.
The industrial demand for modern SHM for ongoing projects requires engineers to leverage vibration-based and other sensor monitoring. In the age of AI, where most human activities are replaced by algorithms and ML programs, proven structural health monitoring is still a need under various questions. Various methods, like ultrasonic and radiography, require prior knowledge and a detailed framework to include in new-gen SHM.
Navigating the Future of Civil Engineering with SHM
The evolution of engineering in the last three decades has witnessed advanced risk assessment models to help engineers create and maintain safe buildings, bridges, etc. However, various news reports of collapse also raise the question of the most efficient structural health engineering. As we live in the age of AI and machine learning, the new-generation SHMs leverage these technologies and bring accuracy to the structural assessment process.
Leaders and engineers must be aware of evolving technologies and share them with their peers and teams to leverage new-gen structural health monitoring. It will help them create a building that will withstand a disastrous collapse and ensure the public’s safety.
