During hot summer days the metal of the Binnenhavenbrug swells so much, that the bridge can no longer open or close. The temperature of the bridge is reduced by cooling it with water from the river. MOCS has developed an early warning system that measures the bridge with sensors. The bridge can now be cooled pro-actively which prevents logistic headaches for Rotterdam.

Results

  • Direct warning when there is a risk of bridge bridging
  • Bridge can be proactively cooled so that no problems arise
  • Applicable to many different types of projects
  • Contributes to the realisation of making Rotterdam a Smart City

Our role

  • Live reading of the bridge temperature and an automatic calculation of the effect on the structure
  • Prediction when the bridge gets too hot and gets stuck

How it’s done

 


Working principle

→ Temperature measurements are collected within the web-based VIKTOR platform.
→ A finite element model is used to simulate the expansion for the available measurements.
→ The simulation results are calibrated with measurements of the actual bridge length. After this, the temperature measurements are used to calculate the dilation of the bug.
→ The behavior of the system can be combined with current weather forecast to predict the dilation add in the future.

Temperature sensors

→ A total of 52 sensors supply data about the temperature distribution of the bridge.
→ A measurement is performed every 15 minutes. The results are immediately available on the online platform.
→ The sensors are distributed over the different parts of the bridge:

  • Deck
  • Sides
  • Primary stiffeners
  • Secondary stiffeners

Temperature modeling

→ Ansys software is used to create a finite element model of the bridge.
→ This model uses the surface polynomials to determine the temperature distribution in the structure.
→ The animation shows the temperature of the deck on 1 August between 10: 00-15: 00.

Expulsion simulations

→ Differences in temperature cause the bridge to warp. The upper figure shows the expansion of the left and right. The differences in expansion show clear similarities with the measured temperature differences in the bridge.
→ The bottom figure shows the relationship between the average temperature and the average expansion.
→ The directional coefficient of the linear relationship can be interpreted as the thermal expansion coefficient of the bridge.

Expulsion prediction

→ The ratios of the temperatures of the sides and the primary stiffeners are used to predict the degree of warping.
→ An empirical relationship is defined that gives the maximum expansion:
δ𝑚𝑎𝑥 = δ𝑎𝑣𝑔 ∙ (𝑟𝑎𝑡𝑖𝑜𝑤𝑒𝑏𝑠 ∙ 𝑐1 + 𝑟𝑎𝑡𝑖𝑜𝑠𝑖𝑑𝑒 ∙ 1 – 𝑐1) 𝑐2
→ The coefficients are chosen so that the margin of error is minimized.
→ The bottom figure shows the results of the simulations and the predictions of the empirical relationship. The maximum deviation is 0.2 mm.

System adjustments

→ Before the system can be used to predict the dilatation joint, the system will have to be calibrated.
→ The predicted expansions must be defined in relation to a reference temperature, reference length and reference dilatation joint.
→ The measurements of the distances between the abutments and the dilatation joint are combined to determine these values.
→ These values are measured at five different points to determine the accuracy of the warp prediction.

System operation

→ After the system has been calibrated, the current temperature measurements can be translated into a minimal dilatation gap. The degree of change can be used to give a timely warning.
→ The combination of temperature measurements and weather data can be used to predict an expected dilatation gap based on a weather forecast.

Ontdek hoe jouw organisatie kan profiteren van een geautomatiseerd ontwerpproces!