No abstract

China’s construction industry standard “technical code for concrete special-shaped column structure” has been implemented since 2006, and the use of special-shaped walls in the actual construction of buildings is more common. The cross-section wall can effectively reduce the protruding angle of the building, thereby expanding the effective building area of the room, reducing the proportion of the building components themselves, and making the structure layout of the building more beautiful. In order to analyze the dynamic characteristics of eccentrically compressed prestressed concrete beams, the finite element simulation of different reinforcement parameters, longitudinal reinforcement diameter, and reinforcement ratio was constructed. Through comparison, the load flexibility curve change law of concrete stress cloud and reinforcement stress cloud under various working conditions and the influence change law of test mold are studied, and the influence change law of dynamic characteristics under the above four conditions and unidirectional bias pressure is clarified.

Highway construction has always been an important strategy in China’s construction projects. However, because the soil in the construction area belongs to the soft soil zone, there will often be large vertical deformation in the construction process, which will seriously affect the engineering quality, so the highway FS (foundation settlement) prediction is particularly important. In order to improve the accuracy of highway stability prediction and ensure the safety of highway engineering, a prediction model based on PSO_SVM (support vector machine for particle swarm optimization) is proposed. By using the particle velocity and its position in the PSO algorithm to correspond to the kernel function parameters and penalty factors of the parameters in the model, the optimal parameters are found and substituted into the SVM prediction model to obtain the PSO_SVM. The results show that the MAD of section A# and section B # of PSO_SVM is 0.8991 and 1.3027 for different monitoring points. Conclusion. PSO_SVM has a strong learning and generalization ability, high prediction accuracy, stability, and adaptability, and can reflect the overall change information of highway FS data, which has practical application value.