Development of deep neural network model to predict the compressive strength of FRCM confined columns

The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius r(c), unconfined concrete strength f(co), thickness n(t), the elastic modulus of fiber E-f, the elastic modulus of mortar E-m. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.

Automated summary

This study conducted a reliability analysis of the strength model for predicting the confinement influence of concrete columns with Fabric-Reinforced Cementitious Matrix (FRCM) using both physical models and a Deep Neural Network model. The results showed that the proposed ANN models accurately predicted the compressive strength of FRCM with high accuracy. The unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio were found to be the most significant input variables in the efficiency of FRCM confinement prediction.