Bayesian-Convolutional Neural Network Model Transfer Learning for Image Detection of Concrete Water-Binder Ratio

Abstract

Since concrete is still the most widely used building material at present, the phenomenon of engineering accidents caused by inaccurate mix proportion is extremely prominent. This makes the contradiction between timely and effective detection and test results cannot be given quickly by traditional technology become particularly outstanding. In this paper, a new method based on Bayesian-convolutional neural network model transfer learning is proposed to detect water-binder ratio, the most important parameter in mix proportion of concrete mixtures. Bayesian optimization was applied to pretrained convolutional neural networks to establish Bayesian-convolutional neural network models, avoiding tuning hyperparameters manually. The authors performed several experiments and obtained large numbers of images of freshly-mixed concrete mixtures, which were used as datasets to carry out water-binder ratio detection. These models achieved high accuracies on training, validation and testing sets. Applying these models, we could implement real-time and high sampling rate water-binder ratio detection. The authors integrated the models and developed a detection system of concrete mix proportion. Equipped with definite hardware facilities, this system can effectively monitor the quality of concrete in production process and prevent engineering accidents. According to training curves of the models, a new parameter was introduced to discuss how mix proportion influence the sensitivity of concrete mixtures apparent state to the change of water-binder ratio, which is an important consideration to preliminary assess the service behaviors of concrete. Through this parameter, we also explored the essence of image features learned by models is the fluidity of concrete mixtures.