Supplementary Material for "Integrating Experimental Results and Machine Learning for Predicting Pull-Out Strength of Cast-In Connectors in Thin UHPC Panels"

This dataset contains the supplementary material associated with the study “Integrating Experimental Results and Machine Learning for Predicting Pull-Out Strength of Cast-In Connectors in Thin UHPC Panels.” The deposited material includes a database of 54 experimental specimens compiled from published studies and used to develop and evaluate machine learning models for predicting connector pull-out strength. The database contains eight material and geometric input variables and the corresponding experimental pull-out strength. The supplementary material also includes MATLAB code for the developed Gene Expression Programming (GEP) model and documentation of the MATLAB-based graphical user interface developed for pull-out strength prediction.