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Updated: Jan 7, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
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Optimal Sensor Placement in Buildings: Stationary Excitation.

Farid Ghahari1,2, Daniel Swensen1, Hamid Haddadi1

  • 1California Geological Survey, Sacramento, CA 95814, USA.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary

This study introduces a method for optimal sensor placement in buildings to reduce uncertainty in structural response predictions. It uses Gaussian Process Regression to identify the most informative locations for sensors, aiding structural health monitoring.

Keywords:
buildingsgaussian process regressionoptimal sensor placementstationary response

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Area of Science:

  • Structural Engineering
  • Seismology
  • Data Science

Background:

  • Advancements in sensing technology enable broader applications in earthquake and structural engineering.
  • Cost-effective sensor deployment is crucial for large-scale structural health monitoring and data collection.
  • Strategic sensor placement is vital to maximize data value and minimize uncertainty in structural response analysis.

Purpose of the Study:

  • To develop a methodology for optimal sensor placement in buildings to minimize uncertainty in structural response reconstruction.
  • To identify sensor locations that enable accurate interpolation of structural responses at non-instrumented floors.
  • To support the California Strong Motion Instrumentation Program's goal of collecting high-accuracy, low-uncertainty structural response data.

Main Methods:

  • Utilized Gaussian Process Regression (GPR) to quantify response prediction uncertainty.
  • Employed a shear-flexural beam model for building approximation with minimal parameters.
  • Focused on stationary excitations and buildings with uniform mass and stiffness distributions.
  • Proposed a decision-making table for sensor placement in strong motion networks.

Main Results:

  • A methodology was developed and validated using simulated and real data.
  • The GPR model effectively quantifies prediction uncertainty for sensor placement optimization.
  • The proposed method balances accuracy and simplicity for large-scale applications like CSMIP.
  • Identified informative sensor locations to minimize total uncertainty across building height.

Conclusions:

  • The study provides a practical approach for optimizing sensor placement in buildings for structural health monitoring.
  • The proposed method facilitates quantitative decision-making for sensor networks.
  • Future work includes extending the methodology to non-stationary excitations and diverse building types.