Park Hyun-Woo, Hwang Jin-Ho
Department of ICT Integrated Safe Ocean Smart Cities, Dong-A University, 37 Nakdong-Daero 550beon-gil, Saha-gu, Busan 49315, Republic of Korea.
Sensors (Basel). 2023 Jul 24;23(14):6649. doi: 10.3390/s23146649.
This paper proposes a physics-informed neural network (PINN) for predicting the early-age time-dependent behaviors of prestressed concrete beams. The PINN utilizes deep neural networks to learn the time-dependent coupling among the effective prestress force and the several factors that affect the time-dependent behavior of the beam, such as concrete creep and shrinkage, tendon relaxation, and changes in concrete elastic modulus. Unlike traditional numerical algorithms such as the finite difference method, the PINN directly solves the integro-differential equation without the need for discretization, offering an efficient and accurate solution. Considering the trade-off between solution accuracy and the computing cost, optimal hyperparameter combinations are determined for the PINN. The proposed PINN is verified through the comparison to the numerical results from the finite difference method for two representative cross sections of PSC beams.
本文提出了一种基于物理信息的神经网络（PINN），用于预测预应力混凝土梁的早期时变行为。PINN利用深度神经网络来学习有效预应力与影响梁时变行为的几个因素之间的时变耦合，这些因素包括混凝土徐变和收缩、预应力筋松弛以及混凝土弹性模量的变化。与传统数值算法（如有限差分法）不同，PINN直接求解积分微分方程，无需离散化，提供了一种高效且准确的解决方案。考虑到求解精度和计算成本之间的权衡，为PINN确定了最优超参数组合。通过与有限差分法对PSC梁两个代表性横截面的数值结果进行比较，验证了所提出的PINN。
