‌‌‌‌‌‌Journal Information

Machine Learning in Structural Dynamics

Online ISSN: request pending

Print    ISSN: request pending

Publishing model:  Open Access

DOI Prefix: 10.62762/MLSD

Aims & Scope

Machine Learning in Structural Dynamics is a peer-reviewed journal dedicated to publishing original research that bridges the fields of machine learning and structural dynamics. The journal focuses on the development and application of data-driven methods for modeling, analyzing, and controlling the dynamic behavior of structures across various engineering domains. Topics of interest include, but are not limited to, structural health monitoring, system identification, vibration analysis, reduced-order modeling, uncertainty quantification, and intelligent control. The journal encourages interdisciplinary contributions that advance both theoretical understanding and practical implementation of machine learning techniques in structural dynamics.

Publication Frequency

Quarterly

Ownership

The journal is owned by Institute of Emerging and Computer Engineering.

Archiving

All journals published by IECE are archived in Portico, which provides permanent digital archiving for scholarly journals.

Ethics Statement

IECE is responsible for implementing rigorous peer review and strict ethical policies and standards to ensure that high quality scientific work is added to the field of scholarly publishing. IECE takes such publishing ethics issues very seriously, and our editors are trained to enforce COPE's Core Practices and Guidelines, with a zero-tolerance policy for plagiarism, data falsification, and other behaviours. To verify the originality of content submitted to our journals, we use iThenticate to check submissions against previous publications.