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Buildings' Biaxial Tilt Assessment Using Inertial Wireless Sensors and a Parallel Training Model.

Luis Pastor Sánchez-Fernández1, Luis Alejandro Sánchez-Pérez2, José Juan Carbajal-Hernández1

  • 1Instituto Politécnico Nacional, Centro de Investigación en Computación, Juan de Dios Bátiz Ave., México City 07738, Mexico.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel biaxial tilt assessment for buildings using MEMS inertial sensors and advanced algorithms. The system enables real-time structural inclination monitoring, crucial for detecting issues like differential soil settlements.

Keywords:
biaxial tilt anglebuilding applicationsinclination severityreal-time measurementsignal processingstructural health monitoringtime-series algorithms

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Area of Science:

  • Structural Engineering
  • Geotechnical Engineering
  • Sensor Technology

Background:

  • Micro-Electro-Mechanical Systems (MEMS) sensors offer versatile applications in structural health monitoring.
  • Current real-time monitoring systems are often limited by cost and signal processing challenges, especially with noisy acceleration data.
  • Accurate processing of static acceleration variations is key for measuring biaxial inclination and structural patterns.

Purpose of the Study:

  • To develop and validate a biaxial tilt assessment system for buildings using MEMS inertial sensors.
  • To address the research gap in efficient signal processing for structural inclination monitoring.
  • To enable simultaneous supervision of structural inclinations and their severity in real-time.

Main Methods:

  • Utilized inertial sensors, Wi-Fi Xbee, and Internet connectivity for real-time data acquisition.
  • Developed two specialized algorithms with successive numeric repetitions to process gravitational acceleration signals.
  • Employed a parallel training model in cascade with neural models for recognizing and classifying 18 inclination patterns and their severity.

Main Results:

  • The developed algorithms significantly improved the processing of gravitational acceleration signals.
  • Inclination patterns were computationally generated, considering differential settlements and seismic events.
  • The monitoring software achieved a resolution of 0.1°, with classifiers demonstrating >95% precision, recall, F1-score, and accuracy in laboratory tests.

Conclusions:

  • The proposed biaxial tilt assessment system effectively monitors structural inclinations in real-time.
  • The advanced signal processing algorithms and neural models provide accurate classification of inclination patterns and severity.
  • The system is a viable solution for supervising the structural health of buildings, particularly in urban areas prone to differential soil settlements.