Sure, but "the movements in the cluster began to show an atypical pattern with diverging paths" does highlight that even though movements might be common for all bridges, the particular pattern they discovered for this bridge was uncommon.
Agree that the maximum value one could get from a process like that would be "When will this bridge fail?", but better than nothing, is to get a "This bridge looks like it will fail soon".
Besides, could manual human-driven inspections even be able to give a "more or less exact prediction of when a bridge fails"? If not, then at least a somewhat automated way of getting the same answer would be cheaper for local governments to run, and for the cases where it makes sense, send the human bridge inspectors there.
That's not the case, though. This isn't an "I told you so," like the 2022 Pittsburgh bridge collapse. (https://www.ntsb.gov/news/press-releases/Pages/NR20240221.as...)
The study ended 7 days before the collapse, and it only concluded that an on-site inspection is needed:
> Its findings revealed there were significant indicators present prior to the incident to suggest inspection/consultation with on-site engineers would have been necessary.
https://www.encardio.com/blog/bridge-monitoring-sensors-type...
Seriously, I think if they can show in hindsight how this could have been predicted using satellite data, and then using this knowledge in future to save a few other bridges (and lives), it is quite a cool finding.
- "According to a spokesperson from Dresden's Road and Civil Engineering Office, the bridge was frequently inspected"
Implies it frequently passed inspections–multiple, recent ones. How did the civil engineers make such an egregiously wrong call on this one? They didn't make enough alarm to even temporarily close it; it was a near-disaster,
- "The last tram crossed the span just 18 minutes before the collapse, dpa reported."
https://apnews.com/article/dresden-germany-bridge-collapse-c...
[1] https://practical.engineering/blog/2024/6/18/this-bridge-sho...
What is this strange mismash of terms thrown around? I always assumed(limited liabilities) that machine-learning was a sub-set of artificial-intelligence.
> A distinct movement cluster on the western superstructure of the bridge of up to 23.50 mm per year [...] Additionally [...] from October 2022, the movements in the cluster began to show an atypical pattern with diverging paths
> Additional movement on the northern side of the bridge structure was also revealed, which could have indicated additional stress accumulation in the bridge’s western superstructure
So, the bridge was under stress and moving(breaking off?) slowly by few mm each year causing the eventual collapse? Was the movement due to the traffic over the bridge or also supported by the movement of water on the ground?
> It was also determined that bridge elements in the water, would raise the water level by around 30 to 50cm from the Carola Bridge to the level of the Waldschlösschen Bridge about 4.5km west of the Carola Bridge.
So, the collapsed and submersed debris from the bridge will raise the water levels by several inches having sone consequences for the Waldschlösschen bridge?
Can someone with more knowledge on such topics ELI5 me the overall things I quoted above?
This is what it looks like from the data. The key here is "diverging paths", which means different parts of the bridge were moving in different directions, putting strain on it.
Thanks! But how was this not noticed[1] by the maintenance and renovation works? I would assume, such patterns should show visible cracks or some symptoms beforehand.
[1] I know that, for a huge structure, a few mm movement is tough to even notice without major equipment and monitoring, but still I believe inspections are done frequently for structural changes and damages.
You can't measure their position to the millimeter range, but with some interferometry techniques you can measure their movement to the millimiter range, relative to close points. Some variation of https://www.sciencedirect.com/science/article/pii/S092427161... was likely used in that work, I've seen it done for many other structures (and I even tried to setup a pipeline for doing that for commercial customers, but in the end we didn't manage anybody to fund us).
You can probably get better measurements with an onsite survey, but using satellite data has the advantage that with a handful of satellites you can map an entire country once every one or two week, and after throwing some computing power at it you can theoretically monitor all the bridges and houses at once and get early predictors of possible problems.
These case studies give you a hint of what can be done: https://www.sarproz.com/case-studies/ (I'm not and never have been affiliated with that product, just linking some cool pages).
What the heck does this mean?
> Insurtech utilises technologies such as artificial intelligence, big data analytics, blockchain, and machine learning, to support the insurance industry.
Holy buzzword soup, batman.
Why is this being upvoted? This is a meaningless article.
The worst part is how they present it as if they totally could have predicted it if they tried.
