Research data supporting the journal article: Damage identification of brick masonry under cyclic loading based on acoustic emissions

No Thumbnail Available
Change log
Alexakis, Charalampos  ORCID logo
Liu, Han 
DeJong, Matthew 

Research data supporting the accepted manuscript ENGSTRUCT_2019_4876_R1: Alexakis H, Liu H, DeJong MJ (2020) "Damage identification of brick masonry under cyclic loading based on acoustic emissions", Engineering Structures

Creator: Haris Alexakis, June 2020

Affiliation: Centre for Smart Infrastructure and Construction, University of Cambridge, UK

These data have originated from the research project: “Experimental Investigation of the Long-Term degradation of masonry using Acoustic Emission sensors”. Source: CSIC Research & Development Seed Funds, EPSRC and Innovate UK.

Acoustic emission data have been generated in LabVIEW and processed in Matlab. LED displacement data have been generated by the CTrack software, supporting the triple-camera dynamic tracking measurement system in Structures Lab, Department of Engineering, University of Cambridge.

Description of Data:

  1. txt files txt file name format: Load level / Parameter / Sensor txt files contain two columns. The first is Time (sec) and the second the parameter. Parameters are
  • Cumulative AE energy. Units in Volts*s
  • Maximum AE signal peak per sec-data segment. Units in decibel
  • Displacement in mm (e.g. x27x24 represents the horizontal distance between targets 27 and 24)
  1. excel files Contain the b-value analysis data from 3 methods described in the paper. Column 1: Load level Columns 2-5: Mean b-value per Sensor Columns 6-9: Standard deviation per Sensor (only for the 1st method) Columns 10-13: Number of events per Sensor (only for the 1st method)

Software / Usage instructions
LabVIEW, Matlab, CTrack
acoustic emission, railway bridge, smart infrastructure, structural health monitoring, asset management, non-destructive testing