Bridges are omnipresent in every society and they affect its human, social, ecological, economical and cultural aspects. This is why a durable and safe usage of bridges is an imperative goal of structural management. ...Bridges are omnipresent in every society and they affect its human, social, ecological, economical and cultural aspects. This is why a durable and safe usage of bridges is an imperative goal of structural management. Measurement and monitoring have an essential role in structural management. The benefits of the information obtained by monitoring are apparent in several domains. In deformation analysis, the functional relationship between the acting forces and the resulting deformations should be established. If time depending observations are given, a regression could be used as a functional model. In case of stochastic model uncorrelated observations with identical variance are assumed. Due to the high sampling rate, a small time difference arises between two observations. Thus the assumed stochastic model is not suitable. The calculation has to be effected by means of auto-correlated observations. This paper investigates an integrated monitoring system for the estimation of the deformation (i.e., static, quasi-static) behavior of bridges from total station observations and studies the effect of autocorrelation technique on the accuracy of the estimated parameters and variances. The results have shown that autocorrelation technique is reduced the standard deviation of X&Y-direction about 6.7% to 29.4% and 6.5% to 15.5% of the original value, respectively, but the situation was differ in Z direction;the standard deviation in vertical component Z was increased.展开更多
Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea...Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.展开更多
Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com-...Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.展开更多
The gripper cylinder that provides braced force for Tunnel Boring Machine （TBM） might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for...The gripper cylinder that provides braced force for Tunnel Boring Machine （TBM） might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition （EMD） method is estab- lished for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiff- ness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the character- istic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylin- der based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.展开更多
A scheme for identifying rolling layers in roller-compacted concrete (RCC) dam automatically was presented. First, a conceptual model was developed. Second, by using a computational geometry method, the auto identific...A scheme for identifying rolling layers in roller-compacted concrete (RCC) dam automatically was presented. First, a conceptual model was developed. Second, by using a computational geometry method, the auto identification of rolling layers and auto matching between rolling compaction machines and rolling layers were realized based on spatial control points. An application to the construction of Guandi RCC dam showed that the auto identification of rolling layers played an important role in ensuring the engineering quality.展开更多
To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitori...To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.展开更多
Based on the structure of chute - feed and autoleveHer, an analysis of their working principle and the verification of their practical production results have been carried out. Finally, the future investigation direet...Based on the structure of chute - feed and autoleveHer, an analysis of their working principle and the verification of their practical production results have been carried out. Finally, the future investigation direetiom of chute - feed and card autuleveller are put forward.展开更多
建立了一种可用于水产品及食用油中氟乐灵残留量分析的分散型固相萃取-气相色谱-负化学离子源质谱方法。水产品及食用油经乙腈提取，4℃冷藏后，采用分散型固相萃取法净化，由气相色谱-负化学离子源质谱选择离子监测技术进行测定与确证...建立了一种可用于水产品及食用油中氟乐灵残留量分析的分散型固相萃取-气相色谱-负化学离子源质谱方法。水产品及食用油经乙腈提取，4℃冷藏后，采用分散型固相萃取法净化，由气相色谱-负化学离子源质谱选择离子监测技术进行测定与确证，同位素内标法定量。在1～40μg / L 范围内氟乐灵农药的线性关系良好；方法定量限（LOQ）为0.02μg / kg；对鳗鱼、烤鳗、梭子蟹、小龙虾、猪油和橄榄油等6种复杂基质进行1.0、2.0和3.0μg / kg 等3个水平的添加回收试验，平均回收率均处于80％～100％之间，RSD≤10.3％；无干扰现象出现。该方法可作为水产品及食用油中氟乐灵残留检测的确证方法。展开更多
文摘Bridges are omnipresent in every society and they affect its human, social, ecological, economical and cultural aspects. This is why a durable and safe usage of bridges is an imperative goal of structural management. Measurement and monitoring have an essential role in structural management. The benefits of the information obtained by monitoring are apparent in several domains. In deformation analysis, the functional relationship between the acting forces and the resulting deformations should be established. If time depending observations are given, a regression could be used as a functional model. In case of stochastic model uncorrelated observations with identical variance are assumed. Due to the high sampling rate, a small time difference arises between two observations. Thus the assumed stochastic model is not suitable. The calculation has to be effected by means of auto-correlated observations. This paper investigates an integrated monitoring system for the estimation of the deformation (i.e., static, quasi-static) behavior of bridges from total station observations and studies the effect of autocorrelation technique on the accuracy of the estimated parameters and variances. The results have shown that autocorrelation technique is reduced the standard deviation of X&Y-direction about 6.7% to 29.4% and 6.5% to 15.5% of the original value, respectively, but the situation was differ in Z direction;the standard deviation in vertical component Z was increased.
文摘Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.
基金Supported by National Natural Science Foundation of China(Grant No.51675098)
文摘Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few com- prehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition moni- toring and fault diagnosis. The recent research and devel- opment of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are dis- cussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mech- anism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suit- able method for a specific situation and pointing out potential research directions.
基金Supported by National Basic Research Program of China(Grant No.2013CB035403)National Natural Science Foundation of China(Grant No.51375297)Program of Shanghai Subject Chief Scientist of China(Grant No.14XD1402000)
文摘The gripper cylinder that provides braced force for Tunnel Boring Machine （TBM） might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition （EMD） method is estab- lished for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiff- ness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the character- istic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylin- der based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.
基金Supported by Foundation for Innovative Research Groups of National Natural Science Foundation of China (No. 51021004)National Natural Science Foundation of China (No. 50879056)Key Project in the National Science and Technology Pillar Program during the Eleventh Five-Year Plan Period(No. 2008BAB29B05)
文摘A scheme for identifying rolling layers in roller-compacted concrete (RCC) dam automatically was presented. First, a conceptual model was developed. Second, by using a computational geometry method, the auto identification of rolling layers and auto matching between rolling compaction machines and rolling layers were realized based on spatial control points. An application to the construction of Guandi RCC dam showed that the auto identification of rolling layers played an important role in ensuring the engineering quality.
基金Project 50279005 supported by the National Natural Science Foundation of China
文摘To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.
文摘Based on the structure of chute - feed and autoleveHer, an analysis of their working principle and the verification of their practical production results have been carried out. Finally, the future investigation direetiom of chute - feed and card autuleveller are put forward.
文摘建立了一种可用于水产品及食用油中氟乐灵残留量分析的分散型固相萃取-气相色谱-负化学离子源质谱方法。水产品及食用油经乙腈提取，4℃冷藏后，采用分散型固相萃取法净化，由气相色谱-负化学离子源质谱选择离子监测技术进行测定与确证，同位素内标法定量。在1～40μg / L 范围内氟乐灵农药的线性关系良好；方法定量限（LOQ）为0.02μg / kg；对鳗鱼、烤鳗、梭子蟹、小龙虾、猪油和橄榄油等6种复杂基质进行1.0、2.0和3.0μg / kg 等3个水平的添加回收试验，平均回收率均处于80％～100％之间，RSD≤10.3％；无干扰现象出现。该方法可作为水产品及食用油中氟乐灵残留检测的确证方法。
