Vibration-Based Damage Detection in Historical Adobe Structures: Laboratory and Field Applications

ABSTRACT

Structural Health Monitoring (SHM) has demonstrated to be a fundamental tool for detecting damage in early stages in existent civil engineering structures. This paper explores the accuracy of vibration-based SHM for identifying the existence of damage in adobe constructions, a widespread structural system but on which limited experimental and numerical applications of the technique are available. Two damage detection methodologies are investigated: (i) Autoregressive Models to predict the structural dynamic response taking into account the environmental parameters as input; and (ii) Principal Component Analysis to detect patterns and anomalies in this response without the need of information about environmental conditions. The results of the laboratory tests on a real scale adobe wall positively indicate the capabilities of these two methodologies to accurately identify damage. They also evidence the importance of monitoring several modes as their sensitivity to damage depends on damage location itself. Furthermore, the application of these two damage detection methodologies in a real case study related to the long-term monitoring of a 16^th Century adobe church allowed confirming the building safe condition during almost two years of monitoring period, as well as the absence of damage after a 5.2Mw earthquake.

KEYWORDS:

• Autoregressive models
• damage detection
• earthen structures
• principal component analysis
• structural health monitoring

Acknowledgments

The present work was carried out by the Engineering and Heritage research group of PUCP in collaboration with the Civil Engineering Departments of the University of Chile and the University of Minho. The work was developed thanks to the funding provided by the program Cienciactiva from CONCYTEC in the framework of the Contract N ° 222-2015and the funding received from the Pontificia Universidad Católica del Perú PUCP and its funding office DGI-PUCP (Project 349-2016). The second author gratefully acknowledges ELARCH program for the scholarship in support of his PhD studies (Project Reference number: 552129-EM-1-2014-1-IT-ERA MUNDUS-394 EMA21). The third author gratefully acknowledges CONCYTEC for the scholarship in support of his MSc studies (Contract N° 232-2015 FONDECYT).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the CONCYTEC [222-2015,232-2015 FONDECYT]; DGI-PUCP [349-2016];ELARCH program [552129-EM-1-2014-1-IT-ERA MUNDUS-394 EMA21].