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Last edited 26 Jan 2021
Big data from smart infrastructure
Advanced analysis is needed to crunch the growing flood of data from smart infrastructure sensors. Timo Hartmann of TU Berlin introduces a themed issue of the ICE Smart Infrastructure and Construction journal on the topic.
Maintaining smart infrastructure may be improved by moving from scheduled maintenance schemes to predictive maintenance schemes. Therefore, smart infrastructure has the potential to reduce significantly the financial burden imposed on public budgets by the continuous maintenance and upgrading requirements of our infrastructure systems.
For example, being able to react to short-term usage peaks or long-term changes in infrastructure user-behaviour allows the design and planning of agile infrastructure systems that are more responsive to the ever-changing requirements of our modern societies.
 The challenges for smart infrastructure systems
Without a doubt, these three areas will be driving a lot of scientific research and entrepreneurial development in the years to come. Providing an outlet for these research and development results is one of the reasons for the ICE establishing the new journal of Smart Infrastructure and Construction.
Now, we are collecting enormous amounts of data about more and more aspects of our infrastructure systems, structures are routinely equipped with advanced sensor systems, and we collect precise geometrical data using advanced light-detection and ranging systems.
At the same time, the exponential increase in the available computing power that’s been witnessed in the last decades makes comprehensive analysis of these extreme large amounts of data possible for the first time.
Therefore, we hope and expect that we’ll be publishing many papers dealing with advanced data fusion and analytics issues around our infrastructure systems in the upcoming years. The themed issue intends to make a start in this direction.
Big data is often characterised not only as data that are high in volume, but also as data that are highly diverse in their type and nature (a characteristic commonly referred to as ‘variety’), are generated at a high rate (a characteristic commonly referred to as ‘velocity’) and come in different levels of quality (a characteristic commonly referred to as ‘veracity’).
All of these characteristics make the analysis and fusion of big data very complex and sophisticated. Again, this is an issue that needs to be addressed by the research community in the years to come, if the move to smart infrastructure systems is to be realised.
The first of these two papers – ‘Real-time statistical modelling of data generated from self-sensing bridges’ – Lau et al. (2018), focuses on developing data analysis methods that can deal with the high velocity of big data.
The second paper, ‘Using data to explore trends in bridge performance’ – Bennetts et al. (2018), focuses on the variety and veracity of data, providing a study on how to use the vast amount of data available at Highways England to support bridge asset management.
The paper explores how data from different sources available, such as bridge inventory data, bridge inspection records and data about historic and current defects, can be used to identify trends around the condition of the thousands of bridges managed by Highways England.
However, unlike the first two papers, Hillel et al. (2018) focus on identifying the mode-choice behaviour of passengers, combining individual trip records from the London Travel Demand Survey with trip trajectories collected from Google Maps’ application programming interface.
With its focus on trips, the paper exemplifies how we can use big data to not only learn more about the behaviour of infrastructure assets, but also about the behaviour of the users of the infrastructure systems.
This article was originally published here by ICE on 20 Sept 2018. It was written by Timo Hartmann, TU Berlin.
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