Part A

Submit Paper » Aims & Scope This Special Collection (SC) aims to provide a dedicated space for in-depth exploration and dissemination of advancements in uncertainty modeling and quantification of numerical methods in geotechnical engineering. The primary goal of this SC is to feature emerging developments, which address the calibration of soil or rock constitutive models developed in recent time, data-driven and physics-informed models for soil or rock constitutive relations, database assessment of the variability in geotechnical numerical predictions, and benchmark exercises for geotechnical analyses by commercial software....

Submit Paper » Background This Special Collection (SC) aims to provide a dedicated space for in-depth exploration and dissemination of advancements in vulnerability analysis, risk management, and uncertainty modeling. The primary goal of this SC is to feature emerging developments, which address hazards, risks and respective mitigation strategies towards resilience and sustainability of our infrastructure systems and the built environment. The contributions are supposed to provide key ideas, concepts and technologies to solve major challenges concerned with the high complexity and the multi-disciplinary character of the problems as well as with the comprehensive quantification, efficient processing and management of the involved uncertainties....

Submit Paper » Background The reliability of civil engineering structures is paramount for sustainable and resilient infrastructure. Ensuring robust behavior, particularly in the face of extreme events, is crucial for longevity and adaptability. This Special Collection focuses on a pivotal aspect of structural resilience: the control of vibrations, specifically addressing uncertainties. Scholars are invited to contribute original research papers exploring the nuanced interplay between vibration control and broader resilient civil engineering structures....

Submit Paper » Background Understanding the data and reaching accurate conclusions are of paramount importance in the present era of Big Data. Machine learning has been widely used in academia and industry to analyze voluminous and intricate datasets to uncover hidden patterns and reach incisive insights. Whilst machine learning approaches have extraordinary potential and are increasingly employed to aid in various complicated tasks, their results are not wholly reliable due to the challenges introduced by data uncertainty (aleatory uncertainty) and model uncertainty (epistemic uncertainty)....

Please find attached the Call for Papers for the Special Collection on Non-Deterministic Model Updating and Structural Health Monitoring for Existing Structures. Click to download the CFP Submit Paper » 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....