Friday, July 31st, 2020

5th seminar in ITRC’s Series on “Resilience and Sustainability of Urban Transportation Infrastructure.”

Please join us for the 5th seminar in ITRC’s Series on “Resilience and Sustainability ofmore…

Wednesday, July 22nd, 2020

Call for Paper to JCSHM Special Issue: Integration of numerical modeling, monitoring and machine learning for SHM in Civil Structures

Long-term structural health monitoring (SHM) of civil structures has been mainly performed using two approaches:more…

Call for Paper to JCSHM Special Issue: Integration of numerical modeling, monitoring and machine learning for SHM in Civil Structures

Long-term structural health monitoring (SHM) of civil structures has been mainly performed using two approaches: model- and data-based. A challenge in both approaches is to make the distinction between the variations of the structural response caused by damage and environmental or operational variability. Hybrid techniques for SHM that integrate model- and data-based approaches have emerged to complement the data measured by the monitoring system installed on the structure with data obtained from its numerical model, leading to both unsupervised and supervised machine learning strategies for damage identification. The hybrid approach to the SHM is still in its early development. Some of the challenges are related to the calibration of the numerical models such as to produce reliable data.

This Special Issue intends to bring together publications involving the integration of numerical modeling, monitoring and/or machine learning, in order to highlight the current capabilities and future trends. Besides theoretical contributions, the papers must address the practical engineering application of the techniques, using actual experimental or field monitoring data.

Papers are welcome on following topics but not restricted to:

  • SHM hybrid methods based on machine learning;
  • Long-term big data processing and management;
  • Damage identification based on machine learning;
  • Model-based damage detection and characterization;
  • Long-term condition monitoring involving hybrid methods;
  • Practical engineering applications of SHM hybrid methods;
  • Probabilistic approaches to hybrid methods in SHM.

The novelty and contribution of the submission are requested to be highlighted during the submission process.

Submission period: September 1, 2020 to December 31, 2020

Guest Editors:

Dr. Eloi Figueiredo
Associate Professor
Faculty of Engineering
Lusófona University, Lisbon, Portugal
E-mail: eloi.figueiredo@ulusofona.pt

Dr. Ionut Moldovan
Invited Professor
Faculty of Engineering
Lusófona University, Lisbon, Portugal
E-mail: dragos.moldovan@ulusofona.pt

Dr. Yun-Lai Zhou
Research Professor
Department of Engineering Mechanics, School of Aerospace Engineering
Xi’an Jiaotong Univeristy, China
E-mail: yunlai.zhou@xjtu.edu.cn