Research on Rapid Detection Technology and Application of Mortar Compressive Strength Based on Neural Network

• Published in: Materials science forum Vol. 996; pp. 110 - 116
• Main Authors: Li, Yong Qian, Li, Xiao Min, Lin, Yue Zhong
• Format: Journal Article
• Language: English
• Published: Pfaffikon Trans Tech Publications Ltd 01.06.2020
• Subjects: Artificial neural networks; Compressive strength; Concrete; Construction industry; Construction materials; Mortars (material); Neural networks; Prediction models; Recycled materials; Waste disposal; Waste utilization; Recycled Concrete Powder; BP Neural Network; Mortar Strength
• ISSN: 0255-5476; 1662-9752; 1662-9752
• DOI: 10.4028/www.scientific.net/MSF.996.110

Since the rapid development of the construction industry, the production of construction waste has also multiplied, and the construction waste has caused tremendous pressure on the environment. Therefore, the main research of this subject is that the waste concrete is formed into a recycled material after a certain treatment--concrete powder. And the cement in the dry-mixed mortar is replaced by 0-30% concrete powder. The compressive strength of recycled concrete powder under different dosages was tested by experimental method. The compressive strength is then applied to the artificial neural network to establish a predictive model. Taking time as a variable, the feasibility and the best dosage of the 28-day compressive strength method for the 3d compressive strength during the test are discussed. In order to reduce the test cycle, improve work efficiency, and ultimately achieve the purpose of improving construction waste utilization.