Wang Junwu, Song Yinghui, Yuan Chunbao, Guo Feng, Huangfu Yanru, Liu Yipeng
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China.
Hainan Research Institute, Hainan Research Institute, Sanya 572019, China.
Comput Intell Neurosci. 2022 Jun 8;2022:9230412. doi: 10.1155/2022/9230412. eCollection 2022.
As countries around the world pay more and more attention to the sustainable development of the construction industry, the prefabricated building model has become the best construction type to achieve energy conservation and emission reduction. However, the prefabricated building entails higher technical requirements, and the workers involved in the construction must be trained to reduce the risks. For China, where the demographic dividend is gradually disappearing, how to quickly promote the industrializing workers process has become an urgent issue. This research focuses on the training and management of industrializing workers in prefabricated building. First, the facial images of the participants were collected from the actual test data, and the changes of participants' facial expressions were analyzed through multitask convolutional neural network-Lighten Facial Expression Recognition (MTCNN-LFER). The results of the analysis were plugged into the facial expression recognition and evaluation model for industrializing workers training in this research to calculate the weights, and then all the weights were clustered through the improved SWEM-SAM method. The results show the following: (1) the values of objective data were used to judge the participating workers' mastery of each knowledge and to evaluate whether they are qualified. (2) The evaluation results were used to analyze the risk events that may be caused by participating workers.
随着世界各国越来越重视建筑业的可持续发展，装配式建筑模式已成为实现节能减排的最佳建筑类型。然而，装配式建筑需要更高的技术要求，参与施工的工人必须经过培训以降低风险。对于人口红利逐渐消失的中国来说，如何快速推进建筑工人产业化进程已成为一个紧迫的问题。本研究聚焦于装配式建筑产业工人的培训与管理。首先，从实际测试数据中收集参与者的面部图像，并通过多任务卷积神经网络-轻量级表情识别（MTCNN-LFER）分析参与者面部表情的变化。将分析结果代入本研究的装配式建筑产业工人培训表情识别与评估模型中计算权重，然后通过改进的SWEM-SAM方法对所有权重进行聚类。结果表明：（1）利用客观数据值判断参与工人对各知识的掌握程度，并评估其是否合格。（2）利用评估结果分析参与工人可能引发的风险事件。
