Structural health continuous monitoring of buildings – A modal parameters identification system

Monitoring systems play a key role in maintaining the buildings’ structural health. Although in the last decades the structural monitoring has experienced a considerable growth, the monitoring systems still require remarkable installation efforts and significant costs. Due to these disadvantages, the spread of such systems was scarce, and the duration of experimental phases was often short. The aim of this work is the design of a Structural Health Monitoring (SHM) system to continuously monitor and check the structural behavior throughout the buildings’ lifespan. The system, made up of a customized datalogger and slave devices, allows the continuous monitoring of structures’ acceleration thanks to its ease of installation and low cost. The proposed system is mainly based on a microcontroller that: i) communicates with the nodes via RS485 bus, ii) synchronizes the acquisition samples, iii) acquires the data measured by the nodes. The system was tested on a cantilever aluminum structure, through three different experimental campaigns and the measured data, collected in an internal memory of the datalogger, were post-processed via Matlab algorithm. The results allowed to evaluate the modal parameters (frequencies, damping and modal shapes) of the analyzed structure and its health.

[1]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[2]  Leonardo Pantoli,et al.  An IC architecture for RF Energy Harvesting systems , 2017 .

[3]  Hani G. Melhem,et al.  Structural Damage Detection Using Signal Pattern-Recognition , 2008 .

[4]  Leonardo Pantoli,et al.  Electronic System for Structural and Environmental Building Monitoring , 2018, Sensors.

[5]  Laurent Mevel,et al.  Subspace-based damage detection under changes in the ambient excitation statistics , 2014 .

[6]  Leonardo Pantoli,et al.  A First Approach to Universal Daylight and Occupancy Control System for Any Lamps: Simulated Case in an Academic Classroom , 2017 .

[7]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[8]  E. Parloo,et al.  AUTONOMOUS STRUCTURAL HEALTH MONITORING—PART I: MODAL PARAMETER ESTIMATION AND TRACKING , 2002 .

[9]  Mirco Muttillo,et al.  Development of a low-cost temperature data monitoring. An upgrade for hot box apparatus , 2017 .

[10]  P. Andersen,et al.  Understanding Stochastic Subspace Identification , 2006 .

[11]  S. Alampalli,et al.  EFFECTS OF TESTING, ANALYSIS, DAMAGE, AND ENVIRONMENT ON MODAL PARAMETERS , 2000 .

[12]  Keith Worden,et al.  On robust regression analysis as a means of exploring environmental and operational conditions for SHM data , 2015 .

[13]  Leonardo Pantoli,et al.  Dual band harvester architecture for autonomous remote sensors , 2016 .

[14]  R M Moss,et al.  IN-SERVICE STRUCTURAL MONITORING. A STATE OF THE ART REVIEW , 1995 .

[15]  Aly El-Kafrawy,et al.  Crack detection by modal analysis in 3D beams based on FEM , 2011 .

[16]  T. Chondros,et al.  Vibration of a Cracked Cantilever Beam , 1998 .

[17]  James M. W. Brownjohn,et al.  Long-term monitoring and data analysis of the Tamar Bridge , 2013 .

[18]  Leonardo Pantoli,et al.  A Low Cost Flexible Power Line Communication System , 2016, Sensors.

[19]  Jean-Claude Golinval,et al.  Null subspace-based damage detection of structures using vibration measurements , 2006 .

[20]  D. Srinivasarao,et al.  CRACK IDENTIFICATION ON A BEAM BY VIBRATION MEASUREMENT AND WAVELET ANALYSIS , 2010 .