In this study, the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised. Silica fume was used at concentrations of 0%, 5%, 10%, and 20%. Cube specimens (100 mm × 100 mm × 100 mm) were prepared for testing the compressive strength and ultrasonic pulse velocity. They were cured at 20°C±2°C in a standard cure for 7, 28, and 90 d. After curing, they were subjected to temperatures of 20°C, 200°C, 400°C, 600°C, and 800°C. Two well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM) networks, were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures. The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectively. Various statistical measures were used to validate the prediction performances of both the approaches. This study found that the LSTM network achieved better results than the stacked autoencoders. In addition, this study found that deep learning, which has a very good prediction ability with little experimental data, was a convenient method for civil engineering.

在这项研究中，设计了深度学习模型来估算高温下含硅粉混凝土的机械性能。硅粉的使用浓度为0％，5％，10％和20％。准备用于测试抗压强度和超声脉冲速度的立方体样品（100 mm×100 mm×100 mm）。将它们在20°C±2°C下以标准固化时间固化7、28和90 d。固化后，使它们经受20℃，200℃，400℃，600℃和800℃的温度。两种著名的深度学习方法，即堆叠式自动编码器和长短期记忆（LSTM）网络，被用于预测高温下含硅粉混凝土的抗压强度和超声脉冲速度。预测实验分别使用MATLAB深度学习和神经网络工具进行。使用各种统计量来验证这两种方法的预测性能。这项研究发现，LSTM网络比堆叠式自动编码器获得了更好的结果。此外，这项研究发现，深度学习具有很好的预测能力，几乎没有实验数据，是土木工程的一种便捷方法。