Zhou Weihua, Yang Hongyin, Hao Jing, Zhai Mengxiang, Cao Hongyou, Liu Zhangjun, Wang Kang
School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, China.
State Key Laboratory of Bridge Intelligent and Green Construction, Wuhan 430034, China.
Sensors (Basel). 2025 Aug 6;25(15):4831. doi: 10.3390/s25154831.
Accurate finite element (FE) models are essential for the safety assessment of civil engineering structures. However, obtaining reliable model parameters for existing bridges remains challenging due to the inability to conduct static load tests without disrupting traffic flow. To address this, this study proposes an FE model updating framework that integrates the response surface method and the nutcracker optimization algorithm (NOA). This framework is characterized by the incorporation of ambient vibration data into parameter optimization, thereby enhancing model accuracy. The stochastic subspace identification method is first adopted to extract the bridge's natural frequencies from vibration data. The response surface method is then employed to construct a response surface function that approximates the FE model. The NOA is subsequently applied to iteratively optimize this response surface function, ensuring rapid convergence and the precise adjustment of the FE model parameter. To validate the effectiveness of the proposed framework, a continuous beam-arch composite bridge with a span of 204.783 m was selected as a case study. The results indicate that the proposed method reduced the average frequency error from 5.58% to 2.75% by updating the model parameters. While the whale optimization algorithm required 21 iterations and the grey wolf optimizer needed 41 iterations to converge near the minimum, the NOA achieved this in merely 13 iterations, demonstrating the NOA's superior convergence speed. Furthermore, the NOA significantly outperformed both the whale optimization algorithm and the grey wolf optimizer in reducing the error of the first transverse vibration frequency.
精确的有限元（FE）模型对于土木工程结构的安全评估至关重要。然而，由于无法在不干扰交通流的情况下进行静载试验，为现有桥梁获取可靠的模型参数仍然具有挑战性。为了解决这个问题，本研究提出了一种集成响应面法和胡桃夹子优化算法（NOA）的有限元模型更新框架。该框架的特点是将环境振动数据纳入参数优化，从而提高模型精度。首先采用随机子空间识别方法从振动数据中提取桥梁的固有频率。然后使用响应面法构建一个近似有限元模型的响应面函数。随后应用NOA对该响应面函数进行迭代优化，确保快速收敛和有限元模型参数的精确调整。为了验证所提出框架的有效性，选择了一座跨度为204.783 m的连续梁拱组合桥作为案例研究。结果表明，通过更新模型参数，所提出的方法将平均频率误差从5.58%降低到了2.75%。虽然鲸鱼优化算法需要21次迭代，灰狼优化器需要41次迭代才能在最小值附近收敛，但NOA仅用13次迭代就实现了这一点，证明了NOA优越的收敛速度。此外，在降低第一横向振动频率的误差方面，NOA明显优于鲸鱼优化算法和灰狼优化器。
