基于人工智能方法的混凝土企业质量管理

分析了目前混凝土质量管理中存在的问题,基于戴明循环,研究了各阶段混凝土质量管理的主要内容,进而提出了基于人工智能的质量管理方法。应用创新的遗传算法修饰的人工神经网络,在实际质量数据的基础上,预测了C30的28天强度,与实际值的偏差在-1.57,1.72]MPa,证明了此种创新的人工智能方法的可行性,同时为混凝土企业的质量管理提供了依据。

Quality Management of Concrete Enterprises Based on Artificial Intelligence Methodologies

Through analyzing the existing problems of quality management of concrete enterprises, the detailed content of every step of Deming Cycle is studied. Then a quality management method based on artificial intelligence is put forward with overall consideration of the properties and characteristic of the detailed content. Afterwards, an artificial neural network optimized by genetic algorithm is developed, which is used for the later empirical study to predict concrete 28-day compressive strength. The deviation of the predicted results and the actual value lies between -1.57 and 1.72 MPa, not only verifying the validity of the novel algorithm, but also providing evidence for quality management.