Most engineering assets were never designed to last forever. Bridges get 50-year design lives. Offshore platforms, maybe 25. A gantry crane might be rated for a certain number of load cycles, and after that the original design says you’re done. But here’s the thing: nobody wants to be done. Replacement costs are brutal. Decommissioning a single offshore platform can run into hundreds of millions, and rebuilding a highway bridge disrupts an entire region for years.
So what actually happens is that assets keep operating past their original design life, and the question shifts from “when do we replace this” to “how do we know it’s still safe.” That’s where structural health monitoring comes in. Sensors, inspections, updated FEA models, data analysis, all working together to track what’s really going on inside a structure. Replace the guesswork with measurements, and suddenly you can justify extending an asset’s life with actual engineering evidence behind the decision.
The Numbers Behind Aging Infrastructure
The 2025 ASCE Report Card for America’s Infrastructure gave the US its highest-ever grade. A C. That’s the best it’s ever been, and nine out of eighteen categories still scored in the D range. The estimated investment gap hit $3.7 trillion over the next decade.
“Aging infrastructure systems are increasingly vulnerable to natural disasters and extreme weather events, creating unexpected and often avoidable risks to public safety and disrupting economic activity.” — ASCE 2025 Report Card for America’s Infrastructure
Bridges are a good example because the data is public. The average US bridge is 47 years old. Design lifespan is 50. Of 621 218 bridges in the country, 6.8% are classified as poor and another 49.1% as fair. More fair than good. And that’s just the US, where at least somebody is counting. Globally, you’ve got port cranes running well past their rated cycles, offshore jackets still producing decades beyond their original design life, operating under extended life assessments rather than the original certification basis.
Nobody is going to replace all of these. The money isn’t there. What you can do is monitor them properly, figure out which ones still have capacity left, and focus your budget where it actually matters.
What SHM Looks Like in Practice
People sometimes talk about structural health monitoring as if it’s one technology you install. It’s not.
You’ve got sensors: strain gauges on critical connections, accelerometers, corrosion probes in the splash zone. That gives you continuous data on how the structure moves and where stresses build up. But sensors don’t catch everything. You still need inspectors with ultrasonic thickness gauges and magnetic particle testing kits, checking welds for cracks, measuring pitting in places no sensor can reach. Honestly, on older structures the inspection side matters more because nobody put sensors on a platform built in 1995.
Then you need to bring all of that together. Inspection findings feed into the FEA model directly: update wall thicknesses with measured values, recalculate fatigue damage based on real load history. Sensor data serves a different purpose. It tracks structural behavior over time (vibration patterns, strain levels, load cycles) and sits alongside inspection records and FEA results as part of the same monitoring picture. What you get is a remaining life estimate built on what the structure actually experienced, not the assumptions from the original design stage.
None of this works if the data sits in disconnected files. Sensor data in one system, inspection PDFs on someone’s laptop, calculations in a spreadsheet nobody can find. You need something that ties it together.
What Makes an SHM Program Actually Work
Not every monitoring setup delivers useful results. The ones that work tend to have six things in common: a baseline assessment (you can’t spot degradation if you don’t know what “healthy” looked like), sensor placement driven by actual engineering analysis rather than installation convenience, inspections that feed into the same data stream as the sensors, a digital twin that evolves with new findings, clear trigger criteria for when to repair or reassess, and centralized data management so everyone works from the same information.
That last one is worth stressing. If your inspectors and your engineers and your asset managers are all looking at different data sets, the whole exercise falls apart. Running sensors in one silo and inspections in another, which happens more often than you’d think, defeats the purpose entirely.
The Digital Twin Piece
The term gets used loosely, so it’s worth being specific. In SHM, a digital twin is an FEA model that incorporates measured wall thicknesses, detected cracks, revised material properties, actual load histories. It changes as you learn more about the structure.
Take an offshore jacket designed in 2001. Original analysis assumed certain corrosion allowances and fatigue spectra based on metocean data available at the time. Twenty years on, UT measurements reveal actual corrosion patterns, and updated hindcast metocean data allows fatigue damage to be recalculated against what the structure actually experienced. Feed that into the updated model, rerun the code checks, and you get remaining capacity that actually means something. Not the same conservative envelope from two decades ago.
SDC SAM connects FEA results, inspection history, photos, and repair tickets in one interface, giving maintenance managers a full picture of asset condition. They can manage repair priorities, detect problems early, and act before unpredicted failures happen. Their published numbers show up to 20% extension in asset lifespan and 95% accuracy in crack predictions, which is a serious improvement over scheduled replacement regardless of actual condition.
The Cost Argument
This is really where the conversation lands for most asset owners.
| Reactive Maintenance | SHM-Based Proactive Maintenance | |
| Detection timing | After failure or visible damage | Early-stage, before failure |
| Maintenance cost | High: emergency repairs, expedited parts | Lower, with planned interventions and optimized scheduling |
| Safety risk | Elevated; unknown structural condition between inspections | Managed through continuous or periodic monitoring |
| Unplanned downtime | Frequent, unpredictable | Minimized through condition-based planning |
| Asset life impact | Shortened, damage accumulates undetected | Extended because degradation is addressed before it becomes critical |
| Regulatory compliance | Difficult to demonstrate (fragmented records) | Streamlined: centralized documentation, audit-ready reports |
The U.S. Department of Energy has published numbers on this: predictive maintenance saves up to 40% compared to reactive and extends asset lifetimes by about 20%. A McKinsey report from 2024 showed predictive strategies cutting costs 20-30% and reducing breakdowns by nearly 70%. For offshore structures, a study in the Journal of Marine Science and Engineering found that regular maintenance intervals achieved 58% cost reduction compared to just running to failure.
The ASCE estimates every dollar invested in resilience saves $13 in post-disaster costs. That ratio tends to hold at the individual asset level too.
Organizing the Data
Truth be told, the hardest part of SHM isn’t the sensors or even the analysis. It’s keeping the data organized. Inspection reports from three contractors in three formats, scattered across email threads and shared drives. Try tracking a single crack’s progression over five inspection cycles and you’ll end up digging through hundreds of pages of PDFs.
SDC SAM addresses this by letting teams attach photos, thickness measurements, notes directly to locations on the structural model. Inspector logs corrosion at frame 47 and the reading shows up next to previous measurements at the same spot, linked to the FEA model. They report 50% faster inspection reporting, and regulators increasingly want this kind of continuous integrity management.
Where This Is Heading
The global SHM market was valued at $4.35 billion in 2025 by Grand View Research, with projections to $17.77 billion by 2033. A CAGR of 19.4%. That’s not hype. That’s asset owners looking at the cost of replacement versus the cost of monitoring and making a rational decision.
Structures will need to last longer than originally planned. They’ll carry loads nobody anticipated and face conditions that weren’t in the design basis. Monitoring them properly is how you deal with that reality. It’s just good engineering.