基于ANN 的碳纤维楠竹锚杆锚固力预测研究

为减小对文物本体的破坏,本文基于新疆某土遗址加固保护中碳纤维楠竹锚杆锚固力原位测试试验,考虑锚杆直径、长度、倾斜角以及灌浆体强度、孔径、碳纤维缠绕间距等锚固力影响因素,利用人工神经网络(artificial neural network, ANN) 的误差反向传播(back propagation, BP) 算法及MATLAB 人工神经网络工具箱,建立了锚固力预测的智能模型;并以原位测试所得的数据为学习样本和检验样本,验证了该方法的适用性和可行性. 将训练好的网络模型进行扩展计算,基于L25(56) 正交表试验理论分析了锚固力对各影响因素的敏感性,为同类加固工程的实际应用提供参考依据.

ANCHORAGE FORCE PREDICTION FOR THE CFRP-BAMBOO BOLT BASED ON ARTIFICIAL NEURAL NETWORK

To protect against the destruction of cultural relics, the artificial neural network and the toolbox of MATLAB artificial neural network (ANN) are applied to set up an intelligent model of the anchoring force prediction with consideration of the bolt diameter, the bolt length, the angle of inclination, the grouting body intensity, the aperture and the carbon fiber wrapped spacing, based on the in-situ test of the anchorage force of the CFRP (carbon fibre reinforced plastics)-bamboo bolt used in the protection of a certain earth site in Xinjiang. And by learning the samples from the in-situ test, the applicability and the feasibility of the method are checked. Based on the calculation results, the sensitivity of influencing factors on the anchorage force of the CFRP-bamboo bolt is analyzed by using L₂₅(5⁶) orthogonal table, which may provide a reference for similar reinforcement engineering practical applications.