Please find attached the Call for Papers for the Special Collection on Non-Deterministic Model Updating and Structural Health Monitoring for Existing Structures.
Guest Editors
- Masaru Kitahara, Assistant Professor, Department of Civil Engineering, The University of Tokyo, kitahara@bridge.t.u-tokyo.ac.jp
- Sifeng Bi, Lecturer, Department of Mechanical and Aerospace Engineering, University of Strathclyde, sifeng.bi@strath.ac.uk
- Matteo Broggi, Deputy Head, Institute for Risk and Reliability, Leibniz University Hannover, broggi@irz.uni-hannover.de
- Takayuki Shuku, Associate Professor, Architecture, Civil Engineering and Environmental Management Program, Okayama University, shuku@cc.okayama-u.ac.jp
Aims & Scope
This Special Collection (SC) aims to gather contributions presenting the state-of-the-art on uncertainty analysis in model updating and structural health monitoring (SHM) for existing structures. Over the past few decades, civil infrastructures have been aging in many countries, and more and more infrastructures are being assessed as structurally deficient. Such structural deficiencies in key infrastructures come with massive consequences such as structural failures and even human deaths. The development of a framework for the safe operation and maintenance of infrastructures is thus required. To this end, SHM has attracted increasing attention in recent years, aiming at condition assessment and service life monitoring of existing structures, often on the basis of structural vibration data. SHM strategies can be mainly classified into two categories, i.e., model-based and data-driven methods. Model-based SHM employs physics-based models in combination with inverse analysis techniques to infer a set of model parameters, such that the best possible fit is gained between model outputs and measurements. This approach is generally referred to as model updating. Data-driven SHM, on the other hand, only exploits the monitoring data without use of physics-based models to infer structural condition. This approach is often rooted in signal processing, pattern recognition or machine learning techniques. Regardless of whether model-based or data-driven approach is used, uncertainties are practically inevitable in both the measuring and modeling processes due to very limited number of sensors, variation in environmental and operational conditions, simplification and approximation of complex physical behavior, and so on. Uncertainties may cause large deviations in model updating and SHM results and thus need to be appropriately dealt with by non-deterministic approaches, i.e., either probabilistic or non-probabilistic approaches. Considering the above issues, this SC reports the latest advances and challenges related to uncertainty analysis in model updating and SHM, encompassing not only the theoretical and computational aspects but also the practical and application aspects, especially for large-scale civil infrastructures. The concept of model updating and health monitoring have been widely accepted and used in many different fields such as geotechnical engineering, and the scope of this SC is not limited to structural engineering.