Data-driven shear strength predictions of prestressed concrete hollow-core slabs

Abstract

Listen

Translate article

Although providing sufficient shear strength is crucial in the design of hollow-core slabs (HCS) to prevent shear failure, conventional shear equations for HCS could not always represent the non-linear relationship between the shear strength and influencing parameters, especially for deep HCS. Back-propagation-neural-network- (BPNN)-based models could be a possible solution since they have been widely used in predicting structural behaviour of other concrete structures. However, the feasibility of such an application has not been examined previously. Therefore, a set of BPNN-based models with and without optimisation algorithms were proposed for predicting the shear strength of prestressed HCS. The proposed BPNN-based models were trained and verified against a database of 227 HCS tested in shear. Comparisons between BPNN-based and mechanics-based models were also performed. The corresponding performances of different models were evaluated by the mean Vut/Vpre, R2, RMSE, MAE and MAPE criteria. To the author's best knowledge, this is the first study to apply hybrid-BPNN-based models for shear strength prediction of HCS. The study found that all proposed BPNN models were effective in predicting the shear strength, while the ACO-BPNN model yielded the best predictions among all the BPNNs. It was also shown that the ACO-BPNN model provided better predictions than the ACI 318 code provisions with/without the size effect factor. Besides, the well-trained ACO-BPNN model could present the size effect of slab thickness on shear strength, which was not explicitly considered in ACI 318. In summary, the ACO-BPNN-based model could a rational tool for predicting shear strength for HCS.