Research on Mechanical Properties and Compressive Strength Prediction of Steam-Cured 3D Concrete Printing Based on Deep Learning

Abstract

Abstract: 3D concrete printing (3DCP) technology has garnered extensive attention in recent years. However, few investigations focus on the effect of curing conditions on the mechanical properties of 3DCP. This study primarily investigated the influences of different steam curing conditions (temperature rise rate, sustained temperature time and sustained temperature) on the mechanical performance of 3DCP at various curing ages. To identify optimal steam curing conditions, an orthogonal experiment was conducted to study the mechanical anisotropy of printed cementitious material. Moreover, based on laboratory test data, a conditional tabular generative adversarial network (CTGAN) was established for data set augmentation, expanding from 291 to 1 000 data entries. A one-dimensional residual convolutional neural network (1D-Residual CNN) was developed to predict the compressive strength of 3DCP, accompanied by five machine learning (ML) models for comparison. Experimental results indicate that CTGAN's data augmentation technique significantly enhanced the predictive accuracy of the 1D-Residual CNN model on the compressive strength of 3DCP, with the highest R² reaching 0.92.

Key words: 3D concrete printing, steam curing, anisotropy, compressive strength, deep learning, generative adversarial network

CLC Number:

TU528

SUN Junbo, WANG Yufei, ZHAO Hongyu, WANG Xiangyu. Research on Mechanical Properties and Compressive Strength Prediction of Steam-Cured 3D Concrete Printing Based on Deep Learning[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2024, 43(5): 1723-1738.

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