针对传统塑料光纤在传输过程中损耗大,使用长度较短的问题,提出在光纤连接部位制备一种新型共轭聚合物CN-PPV掺杂塑料光纤放大器材料,并对其制备工艺、荧光特性和增益特性进行研究。试验结果表明,在25 mL MMA、0.032 g BPO、20~75μL n...针对传统塑料光纤在传输过程中损耗大,使用长度较短的问题,提出在光纤连接部位制备一种新型共轭聚合物CN-PPV掺杂塑料光纤放大器材料,并对其制备工艺、荧光特性和增益特性进行研究。试验结果表明,在25 mL MMA、0.032 g BPO、20~75μL n-BM的工艺条件下制备的预制棒表面状态良好,内部无气泡出现,并在拉伸温度为150~177℃的条件下成功拉丝。塑料光纤最大发射截面为1.7×10^(-20) m^(2);当泵浦光传输距离增加,光谱出现明显红移现象,最大红移波长为12 nm。在光纤长度为50 cm的条件下,掺杂质量浓度为1 mg/L的光纤存在最大增益值,达到25 dB。而掺杂质量浓度为0.2 mg/L的光纤具备持续光放大能力,更适合作为光纤放大器的制备材料。展开更多
This paper presents the static and fatigue tests of hybrid(bonded/bolted)glass fiber reinforced polymer(GFRP)joints.Nine specimens of single-lap hybrid GFRP joints have been fabricated to study the static and fatigue ...This paper presents the static and fatigue tests of hybrid(bonded/bolted)glass fiber reinforced polymer(GFRP)joints.Nine specimens of single-lap hybrid GFRP joints have been fabricated to study the static and fatigue behaviors in the experimental campaign.The static tests of uniaxial tension loading are first conducted,from which the static ultimate bearing capacities of the joints are obtained.High-cycle fatigue tests are subsequently carried out so that the fatigue failure mode,fatigue life,and stiffness degradation of joints can be obtained.The measuring techniques including acoustic emission monitoring and three-dimensional digital image correlation have been employed in the tests to record the damage development process.The results revealed that the static strength and fatigue behavior of such thick hybrid GFRP joints were controlled by the bolted connections.The four stages of fatigue failure process are obtained from tests and acoustic emission signals analysis:cumulative damage of adhesive layer,damage of the adhesive layer,cumulative damage of GFRP plate,and damage of GFRP plate.The fatigue life and stiffness degradation can be improved by more bolts.The S-N(fatigue stress versus life)curves for the fatigue design of the single-lap hybrid GFRP joints under uniaxial tension loading are also proposed.展开更多
Accurately estimating the interfacial bond capacity of the near-surface mounted(NSM)carbon fiber-reinforced polymer(CFRP)to concrete joint is a fundamental task in the strengthening and retrofit of existing reinforced...Accurately estimating the interfacial bond capacity of the near-surface mounted(NSM)carbon fiber-reinforced polymer(CFRP)to concrete joint is a fundamental task in the strengthening and retrofit of existing reinforced concrete(RC)structures.The machine learning(ML)approach may provide an alternative to the commonly used semi-empirical or semi-analytical methods.Therefore,in this work we have developed a predictive model based on an artificial neural network(ANN)approach,i.e.using a back propagation neural network(BPNN),to map the complex data pattern obtained from an NSM CFRP to concrete joint.It involves a set of nine material and geometric input parameters and one output value.Moreover,by employing the neural interpretation diagram(NID)technique,the BPNN model becomes interpretable,as the influence of each input variable on the model can be tracked and quantified based on the connection weights of the neural network.An extensive database including 163 pull-out testing samples,collected from the authors’research group and from published results in the literature,is used to train and verify the ANN.Our results show that the prediction given by the BPNN model agrees well with the experimental data and yields a coefficient of determination of 0.957 on the whole database.After removing one non-significant feature,the BPNN becomes even more computationally efficient and accurate.In addition,compared with the existed semi-analytical model,the ANN-based approach demonstrates a more accurate estimation.Therefore,the proposed ML method may be a promising alternative for predicting the bond strength of NSM CFRP to concrete joint for structural engineers.展开更多
基金the National Natural Science Foundation of China(No.51978400)。
文摘This paper presents the static and fatigue tests of hybrid(bonded/bolted)glass fiber reinforced polymer(GFRP)joints.Nine specimens of single-lap hybrid GFRP joints have been fabricated to study the static and fatigue behaviors in the experimental campaign.The static tests of uniaxial tension loading are first conducted,from which the static ultimate bearing capacities of the joints are obtained.High-cycle fatigue tests are subsequently carried out so that the fatigue failure mode,fatigue life,and stiffness degradation of joints can be obtained.The measuring techniques including acoustic emission monitoring and three-dimensional digital image correlation have been employed in the tests to record the damage development process.The results revealed that the static strength and fatigue behavior of such thick hybrid GFRP joints were controlled by the bolted connections.The four stages of fatigue failure process are obtained from tests and acoustic emission signals analysis:cumulative damage of adhesive layer,damage of the adhesive layer,cumulative damage of GFRP plate,and damage of GFRP plate.The fatigue life and stiffness degradation can be improved by more bolts.The S-N(fatigue stress versus life)curves for the fatigue design of the single-lap hybrid GFRP joints under uniaxial tension loading are also proposed.
基金the National Natural Science Foundation of China(No.51808056)the Hunan Provincial Natural Science Foundation of China(No.2020JJ5583)+1 种基金the Research Foundation of Education Bureau of Hunan Province(No.19B012)the China Scholarship Council(No.201808430232)。
文摘Accurately estimating the interfacial bond capacity of the near-surface mounted(NSM)carbon fiber-reinforced polymer(CFRP)to concrete joint is a fundamental task in the strengthening and retrofit of existing reinforced concrete(RC)structures.The machine learning(ML)approach may provide an alternative to the commonly used semi-empirical or semi-analytical methods.Therefore,in this work we have developed a predictive model based on an artificial neural network(ANN)approach,i.e.using a back propagation neural network(BPNN),to map the complex data pattern obtained from an NSM CFRP to concrete joint.It involves a set of nine material and geometric input parameters and one output value.Moreover,by employing the neural interpretation diagram(NID)technique,the BPNN model becomes interpretable,as the influence of each input variable on the model can be tracked and quantified based on the connection weights of the neural network.An extensive database including 163 pull-out testing samples,collected from the authors’research group and from published results in the literature,is used to train and verify the ANN.Our results show that the prediction given by the BPNN model agrees well with the experimental data and yields a coefficient of determination of 0.957 on the whole database.After removing one non-significant feature,the BPNN becomes even more computationally efficient and accurate.In addition,compared with the existed semi-analytical model,the ANN-based approach demonstrates a more accurate estimation.Therefore,the proposed ML method may be a promising alternative for predicting the bond strength of NSM CFRP to concrete joint for structural engineers.
