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中文摘要：
基于遗传神经网络与模态应变能,提出了一种斜裂缝两阶段诊断方法,识别梁体中斜裂缝的位置、角度和深度。根据线弹性断裂力学与虚功原理,推导了斜裂缝梁的单元刚度矩阵,得到了其频率与振型。采用遗传算法对BP神经网络的拓扑结构、权值和阈值进行优化,从而建立了遗传神经网络,用于识别梁体中斜裂缝的位置和角度;结合斜裂缝单元的模态应变能,通过对斜裂缝应力强度因子的积分,得到斜裂缝深度的解析表达式,用于识别斜裂缝的深度。数值仿真表明：能够高精度地诊断出梁体中斜裂缝的损伤状态,包括位置、角度和深度;与BP神经网络相比,遗传神经网络具有更强的泛化能力,且对测量噪声具有较好的鲁棒性。
英文摘要：
Based on genetic neural network and modal strain energy, a two-stage method for detecting diagonal cracks is proposed to identify the location, angle and depth of diagonal cracks in beams. According to linear elastic fracture mechanics and virtual work principle, the elemental stiffness matrix of a diagonally cracked beam is derived, and the frequencies and modes of the diagonally cracked beam are obtained. The topological structure, weight and threshold of the BP neural network are optimized using the genetic algorithm, and a genetic neural network is built to identify the location and angle of the diagonal cracks in beams. By combining the modal strain energy of the diagonally cracked element and integrating the stress intensity factor of the diagonally cracked element, the analytical expression of the depth of the diagonal crack is obtained to identify the depth of the diagonal cracks. The numerical simulation shows that the proposed method may detect the damage state, including the location, angle and depth, of the diagonal cracks in beams with high precision. By comparing with the BP neural network, the genetic neural network has stronger generalization capacity and better robustness against measuring noises.
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