🌉 𝐒𝐨𝐮𝐥 𝐨𝐟 𝐭𝐡𝐞 𝐁𝐫𝐢𝐝𝐠𝐞 𝐏𝐨𝐬𝐭 𝟓: 𝐖𝐡𝐚𝐭 𝐒𝐇𝐌 𝐌𝐚𝐲 𝐋𝐨𝐨𝐤 𝐋𝐢𝐤𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞

SHM technology has come a long way — today’s sensors are accurate, robust, and capable of continuous monitoring. But there’s still no international standard for how monitoring data should be analysed or used. However, several good research projects exist (worth reviewing).

🤖 AI has opened up new capabilities:

• Detecting subtle changes

• Fast prototyping of visualisations

• Bridge-specific insights

Still, without standards, there’s no widely adopted SaaS model for SHM. Each bridge remains a custom case.

So maybe it’s time to think in terms of RaaS – Research as a Service. Modern tools make ad-hoc, continuous research feasible and necessary.

A future-proof SHM setup could follow this 4-phase process:

1️⃣ Data collection

2️⃣ Standardised analysis & visualisation

3️⃣ Bridge-specific research

4️⃣ Expert interpretation

Phases 1 & 2 can be fully automated (cost-efficient, repeatable).

Phases 3 & 4 are still expert-driven — but not forever. As FE-models and AI integrate, these too will become more automated.

🎯 Eventually, we’ll have a closed loop:

Monitoring data → AI metrics → FE-model → Damage localisation

This post series focuses on phase 3: how much can we learn from just one point in a bridge and its movement path?

So far in previous posts, all results are based on 3D movements of a single point. The actual safety decisions in this project were made from stress-based analysis, but this side project explores how far one can go using only spatial displacement — something I haven’t seen done systematically before.

⚙️ It’s still rough and done in spare time, not a polished product. But we’re actively working toward a standardised method to test on other bridges, including stiffer ones with smaller deflections.

📣 And if the data shows no change? That’s good news.

In SHM, no change = no concern — and that’s a result worth aiming for.