Department of Civil and Environmental Engineering, Urban Design and Studies, Chung-Ang University, Seoul 06974, Republic of Korea.
Department of Smart Cities, Chung-Ang University, Seoul 06974, Republic of Korea.
Sensors (Basel). 2023 May 24;23(11):5040. doi: 10.3390/s23115040.
Prestressed girders reduce cracking and allow for long spans, but their construction requires complex equipment and strict quality control. Their accurate design depends on a precise knowledge of tensioning force and stresses, as well as monitoring the tendon force to prevent excessive creep. Estimating tendon stress is challenging due to limited access to prestressing tendons. This study utilizes a strain-based machine learning method to estimate real-time applied tendon stress. A dataset was generated using finite element method (FEM) analysis, varying the tendon stress in a 45 m girder. Network models were trained and tested on various tendon force scenarios, with prediction errors of less than 10%. The model with the lowest RMSE was chosen for stress prediction, accurately estimating the tendon stress, and providing real-time tensioning force adjustment. The research offers insights into optimizing girder locations and strain numbers. The results demonstrate the feasibility of using machine learning with strain data for instant tendon force estimation.
预应力梁减少了裂缝的产生并允许更长的跨度，但它们的施工需要复杂的设备和严格的质量控制。它们的精确设计取决于对张拉力和应力的精确了解，以及监测预应力筋的力以防止过度蠕变。由于预应力筋的有限接入，估计预应力筋的应力具有挑战性。本研究利用基于应变的机器学习方法来实时估计实际应用的预应力筋的应力。使用有限元方法（FEM）分析生成了一个数据集，在 45 米梁中改变预应力筋的应力。对各种预应力筋力场景进行了网络模型的训练和测试，预测误差小于 10%。选择 RMSE 最低的模型进行应力预测，准确估计预应力筋的应力，并提供实时的张拉力调整。该研究为优化梁的位置和应变数量提供了思路。结果表明，使用应变数据进行即时预应力筋力估计是可行的。
