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Open AccessArticle

Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network

Department of Civil Engineering, College of Engineering, Ocean University of China, Qingdao 266100, China
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Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4638; https://doi.org/10.3390/s20164638
Received: 13 July 2020 / Revised: 13 August 2020 / Accepted: 14 August 2020 / Published: 18 August 2020
Proper and accurate mix proportion is deemed to be crucial for the concrete in service to implement its structural functions in a specific environment and structure. Neither existing testing methods nor previous studies have, to date, addressed the problem of real-time and full-scale monitoring of fresh concrete mix proportion during manufacturing. Green manufacturing and safety construction are hindered by such defects. In this study, a state-of-the-art method based on improved convolutional neural network multilabel image classification is presented for mix proportion monitoring. Elaborately planned, uniformly distributed, widely covered and high-quality images of concrete mixtures were collected as dataset during experiments. Four convolutional neural networks were improved or fine-tuned based on two solutions for multilabel image classification problems, since original networks are tailored for single-label multiclassification tasks, but mix proportions are determined by multiple parameters. Various metrices for effectiveness evaluation of training and testing all indicated that four improved network models showed outstanding learning and generalization ability during training and testing. The best-performing one was embedded into executable application and equipped with hardware facilities to establish fresh concrete mix proportion monitoring system. Such system was deployed to terminals and united with mechanical and weighing sensors to establish integrated intelligent sensing system. Fresh concrete mix proportion real-time and full-scale monitoring and inaccurate mix proportion sensing and warning could be achieved simply by taking pictures and feeding pictures into such sensing system instead of conducting experiments in laboratory after specimen retention. View Full-Text
Keywords: multilabel image classification; convolutional neural network; mix proportion; real-time monitoring; integrated intelligent sensing system; intelligent manufacturing and construction multilabel image classification; convolutional neural network; mix proportion; real-time monitoring; integrated intelligent sensing system; intelligent manufacturing and construction
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Yang, H.; Jiao, S.-J.; Yin, F.-D. Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network. Sensors 2020, 20, 4638.

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