针对当前传统的损伤识别方法和测试手段对于实际复杂工程结构和随机的测量环境存在一定的不足，且精度不高，本项目提出将拥有高精度和动态测试能力的光纤光栅传感器应用于组合梁的损伤识别。.通过在试验梁不同的位置设置“缺陷”来模拟组合梁不同部位可能会发生的损伤，通过分布式长标距光纤光栅传感器测试损伤前后的宏应变，利用构件损伤前后的宏应变差值的极大值进行损伤预警和损伤定位。同时结合试验数据，研究钢梁刚度降低、混凝土开裂、不同界面抗剪连接刚度、钢混界面滑移对组合梁各项受力指标的影响程度。.采用有限元计算软件进行建模分析，并将试验结果与计算结果进行对比分析，反复修正计算模型；以最准确的有限元模型计算得到多种损伤状态下的测点应变值，建立深度信念网络训练样本，建立基于深度信念网络的结构非参数化损伤识别模型，通过深层次神经网络直接从原始数据对对象进行认识与判断，将基于人工智能新兴理论的损伤识别方法应用于组合梁。

The conventional damage identification methods and inspection measures have certain drawbacks in the contexts of complex engineering structures and stochastic inspecting environment, and the inspection accuracy is also limited. In this paper, a method of using fiber Bragg grating (FBG) sensors to the damage identification of steel-concrete composite girder, which own high precision and are capable of doing dynamic inspection, is proposed..Defects were deliberately set in different locations in the testing girder to simulate the possible damages that would occur in the different parts of the composite girder. The distributed long-gauge FBG sensors were used to inspect the macro-strain of the girder before and after the damage inspection, and the maximum value of the macro-strain difference before and after the damage inspection were used for damage alarming and positioning. Meanwhile, along with the testing data, the influential degree of a series of parameters on the load bearing capacity of the steel-concrete composite girder was studied, including the decrease of the stiffness of the steel girder, concrete cracking, anti-shearing connection stiffness on different interfaces, and the sliding effect of the steel-concrete interface. .Then, the finite element software was used to establish the model of the girder to do calculation. The testing and calculated results were contrastively analyzed to modify the model, aiming to gain the strains in the testing points under the damaging conditions that were not included in the testing via the calculation done by the most accurate finite element model. Based on which, the deep belief network training samples and the non-parametric damage identification model based on deep belief networks were established, with the intension of using deep neural network to realize the identification and judgment of the targets by directly using the original data of the targets, and applying the damage identification method based on the new theories in artificial intelligence to the steel-concrete composite girder.