PSI - Issue 70

Indu Sharma et al. / Procedia Structural Integrity 70 (2025) 380–385

384

The authors would like to acknowledge SRM University, Sonepat, Haryana, India, for providing the technical and financial support necessary to conduct all laboratory activities. Special appreciation is extended to the undergraduate students of the Department of Civil Engineering and the Department of Computer Science for their technical assistance. The authors also express their gratitude to the HOD, Dr. Abhay Kumar Choubay and Dr. R. Mohanraj from the Department of Civil Engineering for their contributions to the study on the Prediction of Geopolymer Composite Strength Using Soft Computing Techniques. References Ahmad, A., Ahmad, W., Aslam, F. and Joyklad, P., 2022. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques. Case Studies in Construction Materials , 16 , p.e00840. Ahmad, A., Ahmad, W., Chaiyasarn, K., Ostrowski, K.A., Aslam, F., Zajdel, P. and Joyklad, P., 2021. 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