Key Engineering Materials Vol. 852

Abstract: Glass fiber reinforced cement (GRC) is a new type of composite material formed by using alkali-resistant glass fiber as a reinforcing material and cement paste or cement mortar as a matrix. GRC is widely used in construction engineering. However, the durability of GRC is still a major problem in engineering applications, especially GRC materials have been in the hot and humid building engineering environment for a long time. The alkaline environment of the cement matrix will cause serious erosion of the glass fiber, and Will significantly reduce the mechanical properties such as flexural strength and toughness of GRC. In this paper, ordinary Portland cement is mixed with active mineral admixtures such as fly ash and silica fume to reduce the alkaline environment of GRC matrix, and to delay the erosion rate of glass fiber and increase the flexural strength and compressive strength of GRC; At the same time, the effects of different hot and humid building engineering environments on the mechanical properties of GRC were studied.

Abstract: The paper will use BP neural network analysis method to study the thermal conductivity of bentonite and its influencing factors as a system. The heat conduction of bentonite was used as the output of the system, and its influencing factors were used as the system input to simulate. The corresponding simulation model was established to verify the thermal conductivity data. In addition, the analysis of the mechanical properties of the bentonite-PVA fiber cement-based composite materials for construction has not only laid a theoretical and realistic foundation for the prediction and simulation of the thermal conductivity of bentonite, but also has opened up the mechanical properties of the bentonite-PVA fiber cement-based composite materials a new path.

Abstract: Through the long-term load creep test of CE131 geonet and SD L25 retaining wall foundation, which are widely used in reinforced earth engineering, a large number of experimental data are obtained. On this basis, the least-squares and BP neural network are used to predict its creep variables. The principle of least squares is to find a curve in the curve family to fit the experimental data. From the sum of the squared errors σ = 0. 001 16, the fitting accuracy is higher. The BP neural network has adaptive learning and memory capabilities, especially the three-layer BP neural network model. The maximum error between the predicted value and the actual value is 0.91%, which is a lot better than the error of the least square 3.4%. This method Found a new way for creep prediction.