Aging of infrastructure and in particular civil infrastructure has become a global challenge and a serious threat for many developed countries. For instance, the estimated investment needed by 2020 to improve the condition of America’s critical civil infrastructure has been estimated by ASCE as 3.6 trillion dollars.
The National Academy of Engineering (NAE) has also identified the restoring and improving of urban infrastructure as one of the grand challenges for engineering. Structural health monitoring (SHM) has been identified by both ASCE and the NAE as one of the most effective technologies for improving the status of infrastructure.
Until a decade ago, the majority of studies in SHM were focused on developing cost-effective and reliable sensing technologies for SHM applications and in particular long-term implementation for civil infrastructure. However, recent significant advances in sensing technologies have paved the way to long-term applications of SHM. Consequently, continuous monitoring of civil infrastructure is not as challenging as it used to be.
However, processing the massive amount of data (big data) generated through long-term monitoring of huge and complex civil infrastructure has now become an emerging challenge that needs to be addressed urgently by the SHM community. Therefore, this special issue is dedicated to recent research and advances in SHM data interpretation, damage detection methods, machine learning algorithms and algorithms developed to process and interpret massive (big) amount of SHM data.
Suitable topics include, but are not limited to:
- Machine learning approaches for SHM
- Damage detection algorithms
- Advanced signal processing methods
- Time series analysis
- Parametric and non-parametric methods
- Learning from broader applications of big data in other relevant fields, such as aerospace, manufacturing, etc.
Important Dates
Manuscript submission deadline: 15 November, 2015
End of first review period: 15 January, 2016
Revision and re-review deadline: 15 February, 2016
Final decision: 30 February, 2016
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