Determination of Final Strand Slips of Prestressed Precast Hollow-Core Slabs Subjected to Flexural Load Using Machine Learning Algorithms

Abstract

Precast prestressed concrete hollow-core slabs (HCUs) are structural elements with less self-weight, providing improved structural effectiveness in withstanding the straining action and allowing for a long span. This study investigated the additional strand slips and developed machine learning (ML) models for evaluating the final strand slips (Śf) of the precast HCUs. Two groups of HCUs, with nominal widths of 1.2 m and 0.55 m, were subjected to flexural loading conditions. One sample from each group was selected to form composite specimens by casting a concrete topping slab, and the restrain mechanism was attached at the ends of the additional HCU specimens. The experimental datasets used to train the ML models, including the support vector machine (SVM), multi-linear regression (MLR), and improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) models for the prediction of Śf. The efficacy of the IEPANN model compared to the nonlinear predictive models was evaluated, and the performances of the developed ML models were checked using the evaluation matrices. The results indicated that the prestressing strands with relatively higher initial strand slips may result in larger additional slips during flexural loading. The restraining mechanism and cast-in-place topping slab influenced the additional strand slip rate. The hybridized IEPANN model outperformed other classical models in estimating the additional slips with the R2 values greater than 0.9 in the two modelling stages, indicating the efficacy of the IEPANN compared to the nonlinear predictive modes.

Keywords

• machine learning
• hollow-core slabs
• prestressing strand
• flexural loading
• precast concrete