Liu Qiong
College of Finance and Economics, Yinchuan University of Science and Technology, Yinchuan, Ningxia, China.
PLoS One. 2025 Jun 2;20(6):e0322396. doi: 10.1371/journal.pone.0322396. eCollection 2025.
The first steel is the most prevalent material used in building. Steel's intrinsic hardness and durability make it appropriate for different uses, but its greater adaptability makes it ideal for seismic design. The brittle fracture occurred in welded moment connections of steel structures, which were originally thought to be ductile for resistance to earthquakes. The research aims to optimize structural parameters in steel structure seismic design. This paper presents an effective technique for the best seismic design of steel structures, which consists of two computational methodologies. First, particle swarm optimization (PSO) was presented to accurately define the structural characteristics in the seismic design of steel constructions, then a feed-forward neural network (FFNN) to determine unconventional seismic design methodologies for steel frameworks, precisely forecast the structural responses, and improve seismic resistance and dependability under dynamic conditions by using high-tech components and technological advancements. This study presents designing a realistic storey steel moment-resisting frame (MRF) structure and maximum weight under full seismic loading. The outcome demonstrates the reduction in generations that was accomplished during the optimization procedure. Although the PSO method in the paper converges in lower generations, the process indeed requires a significant amount of computing power. The FFNN approach involves the suggestion of a neural network model that works well to predict the necessary structural reactions during optimization. The proposed model considerably minimizes the total computation time. Study aims to improve the seismic analysis of steel using PSO along with forecasting structural responses using a network of feed-forward neural networks (FFNN) to enhance accuracy and reduce the computation time (2.4 min). The proposed FFNN model is more accurate than earlier methods, with the lowest MAPE values in S_IO (3.0661), S_LS (3.562), and S_CP (3.9252). Moreover, it reveals the highest predictive precision with the lowest RRMSE values of 0.0231 (S_IO), 0.0281 (S_LS), and 0.0314 (S_CP). Moreover, the FFNN model has a competitive run time of 2.4 minutes while possessing good goodness-of-fit, with 1.0096, 1.0995, and 0.9925 of R2 for S_IO, S_LS, and S_CP, respectively. As compared to WCFBP-RB, the proposed PSO+FFNN model has better prediction for S_IO, where the predicted value of 0.7879 is almost identical to the actual value of 0.8000. As compared to WCFBP-RB, the model predicts 1.4085 for S_LS, while the actual value is 1.4388. For S_CP, PSO+FFNN predicts 1.8621, which is more precise than WCFBP-RB and almost equals the actual figure of 1.9000.
钢材是建筑中使用最普遍的材料。钢材固有的硬度和耐久性使其适用于不同用途，但其更强的适应性使其成为抗震设计的理想选择。钢结构的焊接弯矩连接中发生了脆性断裂，而这些连接原本被认为具有延性以抵抗地震。该研究旨在优化钢结构抗震设计中的结构参数。本文提出了一种用于钢结构最佳抗震设计的有效技术，该技术由两种计算方法组成。首先，提出粒子群优化算法（PSO）以准确确定钢结构抗震设计中的结构特性，然后使用前馈神经网络（FFNN）来确定钢框架的非常规抗震设计方法，精确预测结构响应，并通过使用高科技组件和技术进步来提高动态条件下的抗震能力和可靠性。本研究提出设计一个实际的楼层钢框架结构以及在全地震荷载下的最大重量。结果表明在优化过程中实现了代数的减少。尽管本文中的PSO方法在较少代数内收敛，但该过程确实需要大量计算能力。FFNN方法涉及提出一种神经网络模型，该模型在优化过程中能很好地预测必要的结构反应。所提出的模型大大减少了总计算时间。研究旨在使用PSO改进钢的抗震分析，并使用前馈神经网络（FFNN）网络预测结构响应，以提高准确性并减少计算时间（2.4分钟）。所提出的FFNN模型比早期方法更准确，在S_IO（3.0661）、S_LS（3.562）和S_CP（3.9252）中具有最低的平均绝对百分比误差（MAPE）值。此外，它以最低的相对均方根误差（RRMSE）值显示出最高的预测精度，S_IO为0.0231、S_LS为0.0281、S_CP为0.0314。此外，FFNN模型具有2.4分钟的有竞争力的运行时间，同时具有良好的拟合优度，S_IO、S_LS和S_CP的决定系数（R2）分别为1.0096、1.0995和0.9925。与WCFBP - RB相比，所提出的PSO + FFNN模型对S_IO有更好的预测，预测值0.7879几乎与实际值0.8000相同。与WCFBP - RB相比，该模型对S_LS的预测值为1.4085，而实际值为1.4388。对于S_CP，PSO + FFNN预测值为1.8621，比WCFBP - RB更精确，几乎等于实际值1.9000。
