Modern infrastructure monitoring often involves many specialized contractors and systems. In practice, sensor data and survey results can end up scattered across different systems, formats, and platforms. This fragmented approach means that although many parties “watch” their piece of the asset, no single system watches the whole.
The result is slow, error-prone decision-making and missed warning signs. Researchers note that without integration of these disparate data streams, SHM systems become fragmented and data accuracy suffers.
Read more: Why Is Fragmented Data Killing Your Productivity (and How to Unify It)?
Stakeholders (contractors, consultants, asset owners, engineers) face several interrelated problems in current SHM practice:
1. Data Silos and Fragmentation
SHM projects often involve multiple teams collecting and storing data in isolation: one contractor installs sensors and manages dataloggers; another provides the remote sensing and yet another interprets reports and that to from different platforms. This leads to inconsistent datasets and delays when merging results.
A U.S.-focused 2021 review of bridge SHM deployments (Rizzo & Enshaeian, 2021) discusses challenges such as wireless sensor drift and stresses integrating multiple sensing modalities. Subsequent work points to data management and format standardization as important practical hurdles. (Rizzo & Enshaeian, 2021). Similarly, Indian practitioners note persistent data silos that block real-time decision-making. Without integration, critical patterns remain hidden until problems escalate.
2. Manual and Disconnected Processes
Traditional SHM still relies heavily on periodic inspections and manual surveys. These fixed-interval checks are labor-intensive, error-prone, and often interrupted by weather or access constraints. As Zhang et al. (2025) note, such approaches do not provide continuous condition awareness. Emerging alternatives—like mobile-based vibration sensing and computer vision—demonstrate how data can be automated at lower cost (Ozer & Kromanis, 2024).
This causes delays in detection, high costs, and gaps in coverage.
3. Skills and Workforce Gaps
Effective SHM requires expertise spanning civil engineering, IT systems, and data analytics. Reviews consistently report a shortage of personnel trained to manage calibration, long-term datasets, and wireless system drift (Rizzo & Enshaeian, 2021). This shortage is particularly acute in fast-growing regions, where infrastructure demand outpaces the availability of skilled monitoring professionals. As a result, even when advanced technology is installed, its potential is often underused.
Even when advanced systems are installed, they are underused. Potential value is left unrealized.
Read more: The Shift from Fragmented to Integrated Monitoring
4. Data Management and Standardization
Modern SHM generates terabytes of sensor readings across strain, vibration, displacement, and environmental channels. Lack of interoperability makes it hard to unify this flow. Variations in protocols, formats, and power requirements mean that each device often requires its own software (Kang et al., 2025). This heterogeneity magnifies silos, drives up integration costs, and complicates scaling. Researchers argue that standard protocols and data models are now essential to making SHM efficient at network scale.
This leads to a rise in complexity, slow down in scaling, and escalating costs.
Together, these challenges create a monitoring ecosystem that is fragmented, costly, and reactive. Infrastructure owners and engineers can’t easily combine information, so maintenance remains calendar-based or incident-driven. Decision-makers lack a holistic picture of asset health, undermining both safety and longevity.
The Promise of Integrated Monitoring
A growing body of research and industry experience shows that integrated SHM systems – which centralize sensor feeds, analytics, and reporting – can overcome these pain points. By pulling all data into one platform, stakeholders gain a unified picture of the asset. For example, an automated data integration system merges seismic, vibration, and displacement sensors into a single real-time dashboard. This eliminates silos: operators can instantly see how events (like a vibration spike) influence other readings across the structure.
Integrated monitoring also accelerates response times. Instead of waiting for manual reports, an owner can get immediate alerts when combined sensor readings cross safety thresholds. Studies suggest the impact is significant: one analysis found predictive SHM reduced maintenance expenditures by ~28% compared to reactive approaches. More investment in integration (common protocols, cloud databases, AI analytics) leads to cleaner data and better insights. As an industry workshop noted, embracing new SHM technology “leads to higher safety for field inspectors” and provides “actionable data for maintenance” – in other words, a unified system makes teams safer and more efficient.
In short: integration eliminates silos, reduces lag, and builds trust.
Read more: Solving Alert Fatigue in Infrastructure Monitoring
Beyond integration: intelligence at scale
But integration is only the first step. Once all channels are connected, the next value comes from intelligence. Dataloggers process data as it is collected—running quality checks, filtering noise, and detecting outliers before transmission. On the central platform, statistical models and machine learning algorithms clean, validate, and correlate signals across subsurface, structural, and spatial domains. This ensures alarms are based on trusted evidence, not raw noise.
The same intelligence layer also forecasts trends by combining historical baselines with live data, allowing teams to see not just what is happening, but what is likely to happen next. In practice, this means fewer false alarms, faster validation of real events, and decision workflows that stay ahead of risk.
Global infrastructure is at a crossroads. Assets designed to last half a century are being asked to endure longer, under harsher conditions. Floods, extreme heat, and seismic risk are rewriting the baseline. Rapid urban growth loads bridges, tunnels, metros, and dams beyond what their creators imagined. Regulators and insurers now expect proof that safety systems not only exist but work as intended. In this context, scattered sensors and siloed reports fall short. Integrated monitoring changes the equation: it doesn’t just collect data; it provides evidence of readiness that reassures regulators, insurers, and the public.
This shift also reframes monitoring from a compliance cost to a competitive edge. With an integrated SHM approach, owners move from firefighting to foresight. Predictive maintenance reduces shutdowns and the ripple effects of service interruptions. Reliable early warnings extend asset life and curb emergency repairs. Trust grows across the value chain: contractors hand over projects with validated alerts, owners demonstrate resilience with auditable records, and consultants support decisions with clean, contextual data. In competitive bids, an integrated monitoring plan is no longer a nice-to-have—it’s a visible differentiator that wins confidence and contracts.
The takeaway is simple. Fragmented SHM creates silos, delays, and reactive maintenance. Integrated SHM delivers unified context, trusted alerts, and proactive action. Take it a step further and make it intelligent, and SHM becomes a decision tool—one that safeguards infrastructure, reduces costs, and builds resilience over the long term. For asset owners, contractors, consultants, and engineers, the path forward is clear: integration is no longer optional. Add intelligence, and monitoring becomes transformative.
FAQs
1. What is fragmented SHM?
Fragmented SHM occurs when sensor data and monitoring reports are scattered across multiple contractors, platforms, or formats, preventing a unified view of infrastructure health.
2. Why is fragmented SHM a problem?
It causes slow decision-making, missed early warnings, inconsistent datasets, and reactive maintenance, reducing safety and efficiency.
3. How do data silos affect monitoring?
Data silos prevent real-time correlation between sensors, leaving critical patterns hidden until issues escalate.
4. Can manual inspections replace integrated SHM?
No. Manual inspections are labor-intensive, slow, and often interrupted by weather or access issues, limiting continuous awareness.
5. What skills are needed for effective SHM?
Expertise in civil engineering, IT systems, sensor calibration, data analytics, and maintenance planning is required.
6. How does integrated SHM help?
Integrated SHM centralizes sensor feeds, analytics, and dashboards, providing a holistic, real-time view of asset health.
7. What role does intelligence play in SHM?
AI and machine learning clean, validate, correlate, and forecast data, ensuring alerts are actionable and reducing false alarms.
8. Does integration reduce maintenance costs?
Yes. Studies show predictive SHM with integration can reduce maintenance expenditures by ~28% compared to reactive approaches.
9. How does integrated SHM improve safety?
Centralized data and intelligent alerts allow operators to respond quickly to risks, protecting assets, inspectors, and the public.
10. Is integrated SHM becoming a competitive advantage?
Yes. Owners with integrated monitoring can demonstrate resilience, provide auditable records, and win stakeholder trust and contracts.