Rijkswaterstaat instrumented the Hollandse Brug motorway bridge with 146 strain, vibration and temperature sensors during its 2016 reinforcement, turning it into a live weigh-in-motion network used to calculate remaining service life and plan maintenance more precisely.
146
Total sensors installed
20
Temperature sensors
41
Vertical strain gauges
50
Horizontal strain gauges
34
Vibration sensors
~30,000 observations
Released research sample (5-minute sample at 100Hz)
140000 km
Rijkswaterstaat road network (context of wider rollout)
4400 km
Rijkswaterstaat waterway network (context of wider rollout)
Details
Maturity
Established
Promoter
Rijkswaterstaat (Dutch Ministry of Infrastructure and Water Management)
Period
2016-2026
Region (NUTS)
NL230
Keywords
transport infrastructure, structural health monitoring, predictive maintenance
Context
During the 2016 reinforcement of the Hollandse Brug (the A6 motorway bridge between Muiderberg and Almere), Rijkswaterstaat sought better data on the bridge's real structural behaviour to move maintenance planning from fixed schedules toward condition-based intervention.
Objectives
To instrument the bridge so that live traffic-load data could be used to model structural behaviour, recalculate remaining service life, and calibrate the bridge as a weigh-in-motion scale.
Activities
Rijkswaterstaat and contractor Strukton Systems installed a 146-sensor network (20 temperature sensors, 41 vertical and 50 horizontal strain gauges paired in old and new concrete, and 34 vibration sensors), with additional underside sensors from TU Delft, all feeding a bridge-mounted computer. A five-minute, 100Hz sample (~30,000 observations) was released for external research. The same IoT-based predictive-maintenance approach - SAS Viya analytics, event-stream processing and around 400 dashboards - is applied agency-wide across Rijkswaterstaat's 140,000km road and 4,400km waterway network.
Results
The bridge now functions as a real-time weigh-in-motion scale, feeding remaining-service-life models and condition-based maintenance planning.
Conclusions
No published figures quantify maintenance-cost or downtime savings attributable to the Hollandse Brug system specifically, and available descriptions are self-reported by Rijkswaterstaat, its contractor and its analytics vendor rather than independently audited - a well-documented but evidence-modest example of sensor-driven infrastructure monitoring.
Implementation
Indicative cost
Medium (€50k–€500k)
Time to results
Long (> 3 years) — Sensors installed during the 2016 reinforcement; monitoring has continued through 2026.
Staffing & skills
Delivered jointly by Rijkswaterstaat, contractor Strukton Systems, and TU Delft (which supplied additional underside sensors)
Conditions for success
Sensor installation timed to coincide with the 2016 structural reinforcement works
Paired strain gauges in old and new concrete allowed direct verification of the new reinforcement layer's curing
Same SAS Viya predictive-maintenance analytics approach reused agency-wide across Rijkswaterstaat's 140,000km road and 4,400km waterway network
Common failure modes
No published figures quantify maintenance-cost or downtime savings attributable specifically to this bridge's sensor network; descriptions are self-reported by Rijkswaterstaat, its contractor and its analytics vendor rather than independently audited
Where it fits
Governance type
national infrastructure agency
Scale
single bridge instrumentation; analytics approach reused nationally
Income level
high-income
Commonly funded by
National / regional programmes
Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.
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Data sources
Where this practice's information was retrieved from, and when.
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