MS23 - Structural health monitoring for urban and extra-urban enviroments


Nowadays Structural Health Monitoring (SHM) plays a paramount role for the preservation of existing civil structures and infrastructures (buildings, bridges, towers, etc.) composing urban environments. Indeed, the use of advanced techniques and innovative instrumentations, many of them carried out and widely assessed for other fields of engineering, allow for identifying important parameters characterizing the dynamic response of these structures toward service and exceptional loads, throughout noninvasive and expeditive procedures. Moreover, the correct identification of these parameters, together with the use of specific algorithms and techniques, allow to derive additional important information about the presence, location and extent of possible damages.

The ever-more frequent extreme weather, mainly depending on climate changes, have particularly emphasized the fragility of urban and extra-urban environments where, together with man-made structures, nature-made structures certainly play a crucial role. Regarding the latter, the frequent events of falling of trees point out the attention toward the study of the vulnerability of trees, particularly in urban areas, with the twofold aim of ensuring safety and quality of life of inhabitants.

SHM then represents a common tool able to support an integrated process for assessing and measuring the safety of urban environments and then, for carrying out efficacy strategies of intervention.

Aim of the present symposium is to share recent advances in the general context of SHM and, moreover, to show its potentiality in terms of application to different types of man-made and nature-made structures.

The main topics included in this symposium are:

  • Structural Health Monitoring techniques;
  • Damage identification techniques;
  • Monitoring and assessment of man-made and nature-made structures;
  • Statistical analysis of monitoring data for novelty detection;
  • Effects of environmental and operational variability on health sensitive features;
  • Artificial Intelligence and Machine Learning techniques;
  • Innovative sensing techniques;
  • Case studies.

© EMI 2023 International Conference