National Taiwan University of Science and Technology, Taipei, Taiwan.
Comput Intell Neurosci. 2022 Aug 1;2022:9541115. doi: 10.1155/2022/9541115. eCollection 2022.
Most building structures that are built today are built from concrete, owing to its various favorable properties. Compressive strength is one of the mechanical properties of concrete that is directly related to the safety of the structures. Therefore, predicting the compressive strength can facilitate the early planning of material quality management. A series of deep learning (DL) models that suit computer vision tasks, namely the convolutional neural networks (CNNs), are used to predict the compressive strength of ready-mixed concrete. To demonstrate the efficacy of computer vision-based prediction, its effectiveness using imaging numerical data was compared with that of the deep neural networks (DNNs) technique that uses conventional numerical data. Various DL prediction models were compared and the best ones were identified with the relevant concrete datasets. The best DL models were then optimized by fine-tuning their hyperparameters using a newly developed bio-inspired metaheuristic algorithm, called jellyfish search optimizer, to enhance the accuracy and reliability. Analytical experiments indicate that the computer vision-based CNNs outperform the numerical data-based DNNs in all evaluation metrics except the training time. Thus, the bio-inspired optimization of computer vision-based convolutional neural networks is potentially a promising approach to predict the compressive strength of ready-mixed concrete.
当今大多数建筑结构都是用混凝土建造的，这主要是因为其具有多种优良特性。抗压强度是混凝土的一种力学性能，它与结构的安全性直接相关。因此，预测抗压强度可以方便早期进行材料质量管理规划。一系列适用于计算机视觉任务的深度学习（DL）模型，即卷积神经网络（CNNs），被用于预测预拌混凝土的抗压强度。为了展示基于计算机视觉的预测的有效性，使用成像数值数据的有效性与使用常规数值数据的深度神经网络（DNN）技术进行了比较。比较了各种 DL 预测模型，并使用新开发的仿生元启发式算法——水母搜索优化器，对相关的混凝土数据集进行了微调，确定了最佳的 DL 模型，以提高准确性和可靠性。分析实验表明，基于计算机视觉的 CNN 在所有评估指标上都优于基于数值数据的 DNN，除了训练时间。因此，基于计算机视觉的卷积神经网络的仿生优化是一种很有前途的预测预拌混凝土抗压强度的方法。
