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Area of Science:

  • Structural Engineering
  • Geotechnical Engineering
  • Applied Mathematics

Background:

  • Instrumentation is crucial for structural health monitoring, but sparse sensor placement is common due to cost constraints.
  • Estimating structural responses at unmonitored locations is essential for comprehensive analysis.
  • Gaussian Process Regression (GPR) combined with deterministic models offers a promising approach for response estimation.

Purpose of the Study:

  • To develop a methodology for optimal seismic sensor placement in buildings.
  • To minimize uncertainty in reconstructing structural responses at non-instrumented floors.
  • To extend previous work on sensor placement optimization to seismic excitations.

Main Methods:

  • A methodology was developed to determine optimal seismic sensor locations.
  • The approach minimizes uncertainty in structural response estimation using a hybrid model (deterministic beam model + GPR).
  • The method was validated using numerical simulations and applied to two real instrumented buildings.

Main Results:

  • The proposed sensor placement strategy achieved an average 40% reduction in response uncertainty compared to random distribution.
  • A 10% uncertainty reduction was observed in a 52-story building with near-uniform existing sensor distribution.
  • An 80% uncertainty reduction was achieved in a 73-story building where sensors were concentrated on localized behavior.

Conclusions:

  • Optimal sensor placement significantly enhances the accuracy of structural response estimation in instrumented buildings.
  • The methodology is effective for seismic excitations and applicable to diverse building structures.
  • Strategic sensor deployment is key to maximizing the benefits of structural health monitoring within budget limitations.