Mechanical Performance Analysis of Steel‐Timber Structure System with Reinforced Layer Based on Particle Algorithm

粒子群优化 计算机科学 材料科学 复合数 算法 人口 粒子(生态学) 流离失所(心理学) 结构工程 复合材料 工程类 地质学 人口学 社会学 心理治疗师 海洋学 心理学
作者
Meizhen Fu,Guoxi Tang
出处
期刊:Wireless Communications and Mobile Computing [Wiley]
卷期号:2022 (1)
标识
DOI:10.1155/2022/7031910
摘要

With the continuous development of science and technology, the state attaches great importance to industry, and the research on materials by researchers is getting more and more in‐depth. The steel‐wood structure is one of the material structures that has been focused on. The steel‐timber composite (STC) structure has the characteristics of good mechanical performance, sustainable attributes, and lightweight. For the steel‐timber structure with reinforced layer, its mechanical performance is better, stronger, and more durable. Particle algorithm (PSO for short) is an intelligent search algorithm derived from simulating the foraging behavior of birds. The PSO algorithm uses the information exchange ability of the population to search for the problem to obtain the optimal solution. The purpose of this paper is to study the mechanical properties of steel‐timber structural systems with reinforced layers. In this paper, an improved PSO algorithm is proposed, and the algorithm model is used to conduct static tests on steel‐timber structures with reinforced layers. The experimental results in this paper show for A and B two different steel‐timber specimens with reinforced layers. When the number of reinforced layers of the A specimen is twice that of the B specimen, the stagnation amplitude of the B specimen is twice that of the A specimen. The cumulative energy consumption of A will be relatively more, in every 1 mm of displacement. A specimen is 4.6 kN·mm on average than B specimen, and the damping coefficient of A specimen is 0.028 larger than that of B specimen. This shows that for specimens with more reinforced layers, the higher the ultimate fatigue strength, the stronger the bearing capacity and the better the stability.
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