A reliable understanding of the properties of 3-D braided composites is of primary importance for the successful utilization of these materials. A new method is introduced to study the mechanical performance of braided composite materials using embeddedoptic fiber sensors. Experimental research is performed to devise a method of incorporating the optic fiber into a 3-D braided composite structure. The efficacy of this new testing method is evaluated on two counts. First, the optical performance of the optic fiber is studied before and after incorporation into the 3-D braided composites, as well as after completion of the manufacturing process for the 3-D braided composites, to validate the ability of the optic fiber to survive the manufacturing process. On the other hand, the influence of incorporated optic fiber to the original braided composite is also researched by tension and compression experiments. Second, two kinds ofoptic fiber sensors are co-embedded into 3-D braided composites to evaluate their respective ability to measure internal strain. Experimental results show that multipleoptic fiber sensors can be co-braided into the 3-D braided composites to determine its internal strain which is difficult to be fulfilled by other current existing methods.

Damage characterization takes place by comparing single mode sensor signals collected before and after the damage event. By subtracting the signals of both conditions from each other, a scatter signal is produced which can be used for damage localization. By using Gabor wavelet to analyze single mode Lamb wave recorded before damage and scatter signal, the difference of time-of-flight can be obtained. Combining with the ellipse technique, the localization experiment on fiberglass plate of dimension 100

Adopting wide-band Lamb wave based active monitoring technology, this study focuses on a neural network method based on a new damage signature for on-line damage detection applied to thin-walled composite structures. Honeycomb sandwich and carbon fiber composite structures are studied. Two kinds of damage are considered: delamination and impact damage. A new damage signature is introduced to determine the presence and extent of damage in composites, while eliminating the influence of different distances between the actuator and sensor. Neural network method is researched to take advantage of this new damage signature combined with several other signatures to decide the damage mode. Kohonen neural network is developed. The proposed method is shown to be effective, reliable, and straightforward for the specimens considered in the present study, which are composed of ifferent materials and suffer various levels of damage.

Structure health monitoring based on diagnostic Lamb waves has been found to be one of the most promising techniques recently. This paper has a brief review of the new developments on this method including the basic novel of the method, fundamentals and mathematics of Lamb wave propagation, narrowband and wideband Lamb wave excitation methods, optimization of excitation factors and diagnostic Lamb wave interpretation methods.

智能材料与结构是当前研究的热门课题，本文介绍了我们所研制的一种能够对损伤进行自诊断的智能结构系统，系统能进行损伤或大应变位置的自诊断及损伤级别的判断，主要介绍了智能复舍材料结构试件的制作、四点法传感元件的布置及应用模式识别算法的研究，该系统具有较高的自诊断功能率。