School of Civil Engineering, Hunan University, ChangSha, Hunan Province, China.
Hunan Hongli Civil Engineering Inspection and Testing Co., Ltd., ChangSha, Hunan Province, China.
PLoS One. 2021 May 3;16(5):e0250795. doi: 10.1371/journal.pone.0250795. eCollection 2021.
To address the problem of low accuracy and poor robustness of in situ testing of the compressive strength of high-performance self-compacting concrete (SCC), a genetic algorithm (GA)-optimized backpropagation neural network (BPNN) model was established to predict the compressive strength of SCC. Experiments based on two concrete nondestructive testing methods, i.e., ultrasonic pulse velocity and Schmidt rebound hammer, were designed and test sample data were obtained. A neural network topology with two input nodes, 19 hidden nodes, and one output node was constructed, and the initial weights and thresholds of the resulting traditional BPNN model were optimized using GA. The results showed a correlation coefficient of 0.967 between the values predicted by the established BPNN model and the test values, with an RMSE of 3.703, compared to a correlation coefficient of 0.979 between the values predicted by the GA-optimized BPNN model and the test values, with an RMSE of 2.972. The excellent agreement between the predicted and test values demonstrates the model can accurately predict the compressive strength of SCC and hence reduce the cost and time for SCC compressive strength testing.
为了解决高性能自密实混凝土（SCC）原位抗压强度检测精度低、鲁棒性差的问题，建立了一种遗传算法（GA）优化的反向传播神经网络（BPNN）模型来预测 SCC 的抗压强度。设计了基于两种混凝土无损检测方法（超声波脉冲速度和 Schmidt 回弹锤）的实验，并获得了测试样本数据。构建了一个具有两个输入节点、19 个隐藏节点和一个输出节点的神经网络拓扑结构，并使用 GA 对传统 BPNN 模型的初始权重和阈值进行了优化。结果表明，建立的 BPNN 模型预测值与试验值之间的相关系数为 0.967，均方根误差为 3.703，而 GA 优化的 BPNN 模型预测值与试验值之间的相关系数为 0.979，均方根误差为 2.972。预测值与试验值之间具有极好的一致性，证明该模型可以准确地预测 SCC 的抗压强度，从而降低 SCC 抗压强度测试的成本和时间。
