Advanced Method for Detecting Irregularities in Concrete Beams Using IOT Data from Sensors

Advanced Method for Detecting Irregularities in Concrete Beams Using IOT Data from Sensors

1Chandrashekara R S, 2Prof. (Dr.) Khanna Samrat Vivekanand Omprakash

CMR University
Chandrashekara.rs@cmr.edu.in

Registration No: 24CPHDF010

Abstract— The  increasing reliance on state  assessments in civil engineering has sparked extensive research into methods for damage detection based on structural vibrations. Modal parameters, such as natural frequencies and mode shapes, have gained  significant  attention  due  to  their  invariance  across structures.  These  parameters  provide  a  global  perspective, meaning their variations can help identify damage without the need for sensor placement directly at the damaged site. This feature  is  a  key  advantage  in  structural  health  monitoring (SHM)   systems.   Integrating   MEMS   sensors   into   SHM frameworks holds great potential for long-term monitoring, particularly for large-scale   infrastructures.    This    paper introduces  an  innovative  anomaly  detection  technique  that analyzes raw sequential data through a statistical approach to identify damage  associated with tendon prestress loss. The technique leverages a distributed monitoring system consisting of  six  high-performance  MEMS  sensors.  To  validate  the system, the first mode frequency is initially analyzed, and the method is then tested on acceleration data from a 240 cm beam  under  three  distinct  damage  scenarios.  The  results demonstrate high accuracy in damage detection and show that the system can also localize the damage effectively.

Keywords— Distributed monitoring system, structural health monitoring, MEMS sensors, frequency domain decomposition, anomaly detection.