Preface Structural Health Monitoring & Machine Learning represents one of twelve volumes of technical papers presented at the 43rd IMAC, A Conference and Exposition on Structural Dynamics, organized by the Society for Experimental Mechanics, and held February 10-13, 2025. The full proceedings also include volumes on Nonlinear Structures & Systems; Model Validation and Uncertainty Quantification; Dynamic Substructuring & Transfer Path Analysis; Special Topics in Structural Dynamics & Experimental Techniques; Computer Vision & Laser Vibrometry; Dynamic Environments Testing; Sensors & Instrumentation and Aircraft/Aerospace Testing Techniques; Topics in Modal Analysis & Parameter Identification Iⅈ and Data Science in Engineering. Each collection presents early findings from analytical, experimental and computational investigations on an important area within Structural Dynamics. Structural Health Monitoring is one of these areas which cover topics of interest of several disciplines in engineering and science. Structural Health Monitoring & Machine Learning are specific subject areas within the SEM umbrella of technical divisions, namely the Dynamics of Civil Structures including other technical activities devoted to structural analysis, testing, monitoring, and assessment. This volume covers a variety of topics including Bayesian Inference Methods, and Structural Health Monitoring with Digital Twinning. The organizers would like to thank the authors, presenters, session organizers, and session chairs for their participation in this track. Editor: Brian Damiano, Oak Ridge National Laboratory, Oak Ridge, TN, USA; Babak Moaveni, Tufts University, Medford, MA, USA; Antonio De Luca, Thornton Tomasetti, Ft. Lauderdale, FL, USA; Keith Worden, University of Sheffield, Sheffield, UK. v
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