Health Monitoring of Existing Building Stock: New Insights and Strategies

Dear Colleagues,

In recent years, an important part of research has been devoted to the experimental and theoretical analysis of existing building stock, which is characterized by a large percentage of structures with a deficit in terms of seismic protection and/or building standards. Indeed, a major part of these structures are reaching the end of their service life and, consequently, need rehabilitation interventions to improve their performance and to avoid damage induced by adverse operational and environmental conditions.

It is crucial to continuously monitor the integrity of structures and infrastructures for the efficient management of this patrimony. One of the strategies that can be followed is represented by structural health monitoring, the application of which has increasd due to the advances in sensor technology and analytical techniques.

Furthermore, structural health monitoring may allow for the faster detection of damage, which is indispensable for the quicker planning of interventions and to guaranteeing the safety and operability of infrastructures.

Several sensors and techniques are available that can be easily adapted to different structural typologies, and a large amount of data can be acquired.

This circumstance has encouraged research towards the improvement of ‘traditional’ structural identification techniques and the adoption of new methods based on machine learning. These methods represent a powerful tool to treat a large volume of data, both in the fields of structural identification and of damage detection.

However, the development of an accurate numerical model of a structure is the most efficient tool in vulnerability and seismic risk assessment and in the design of rehabilitation interventions. To this aim, a crucial role is played by optimization techniques in guaranteeing the capabilities of such a model of predicting identified structural behavior.

The purpose of this Special Issue on “Health Monitoring of Existing Building Stock: new insigths and strategies” is to provide an overview of the new insights and perspectives in the management of existing building stock.

We would like to invite researchers to contribute original research articles as well as review articles in the field of experimental approaches to the detection of structural behavior, the analysis of acquired data by means of traditional and/or machine learning approaches, modeling techniques, and optimization tools for the development of an accurate numerical model for investigated infrastructures, as well as case studies.

Dr. Mariella Diaferio
Guest Editor

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• structural health monitoring
• machine learning
• damage detection
• performance evaluation
• structural modeling
• experimental investigations of existing structures
• optimization techniques
• signal processes
• case studies