Detection and repair of composite damage is crucial to ensure the safety and reliability of aircraft structures.A novel approach to quantitatively evaluate the repair tolerance of composite structures in civil aircraf...Detection and repair of composite damage is crucial to ensure the safety and reliability of aircraft structures.A novel approach to quantitatively evaluate the repair tolerance of composite structures in civil aircraft based on Bayesian updating is presented.The method incorporates historical damage inspection data to determine the prior distribution of damage size,which is then updated with newly collected damage size data using Bayesian theory.Monte Carlo simulation is employed to investigate the probability of failure and estimate maintenance costs,considering various factors such as the frequency and timing of damage events,damage detection,structural strength,gust loads,and maintenance expenses throughout the lifecycle of composite structures.Safety and economic factors are considered to establish a lower threshold for repairs and an upper threshold for maintenance based on the occurrence of accidental impact damage.Verification of the effectiveness and feasibility of a quantitative assessment method for repair tolerance is conducted using damage statistics data from civil aircraft routes utilizing the structural skin panels of composite outer wing.The results demonstrate that the method proposed in conjunction with extensive simulations and full utilization of field damage inspection data can effectively simulate unexpected impact damage situations that may occur during civil aircraft service and evaluate the reliability and economic feasibility of the repair of structure.The research findings hold significant theoretical and practical value for the preparation of documents for continued airworthiness of composite structures,including structural repair manuals and maintenance programs.展开更多
Recently,the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly.However,updating the ground settlement profile,which is cruc...Recently,the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly.However,updating the ground settlement profile,which is crucial for determining potential damage to nearby infrastructures,has received limited attention.To address this,this paper proposes a physics-guided simplified model combined with a Bayesian updating framework to accurately predict the ground settlement profile.The advantage of this model is that it eliminates the need for complex finite element modeling and makes the updating framework user-friendly.Furthermore,the model is physically interpretable,which can provide valuable references for construction adjustments.The effectiveness of the proposed method is demonstrated through two field case studies,showing that it can yield satisfactory predictions for the settlement profile.展开更多
There exists model uncertainty of probability of detection for inspecting ship structures with nondestructive inspection techniques. Based on a comparison of several existing probability of detection (POD) models, a n...There exists model uncertainty of probability of detection for inspecting ship structures with nondestructive inspection techniques. Based on a comparison of several existing probability of detection (POD) models, a new probability of detection model is proposed for the updating of crack size distribution. Furthermore, the theoretical derivation shows that most existing probability of detection models are special cases of the new probability of detection model. The least square method is adopted for determining the values of parameters in the new POD model. This new model is also compared with other existing probability of detection models. The results indicate that the new probability of detection model can fit the inspection data better. This new probability of detection model is then applied to the analysis of the problem of crack size updating for offshore structures. The Bayesian updating method is used to analyze the effect of probability of detection models on the posterior distribution of a crack size. The results show that different probabilities of detection models generate different posterior distributions of a crack size for offshore structures.展开更多
Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(includi...Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.展开更多
In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algo...In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algorithm.In the proposed M-IMM algorithm,a new likelihood function is defined for the sake of updating flight mode probabilities,in which the influences of interacting to residual's mean error are taken into account and the assumption of likelihood function being a zero mean Gaussian function is discarded.Finally,the proposed M-IMM algorithm is applied to the simulation of the aircraft trajectory prediction,and the comparative studies are conducted to existing algorithms.The simulation results indicate the proposed M-IMM algorithm can predict aircraft trajectory more quickly and accurately.展开更多
The inter-well connectivity calculated from reservoir dynamic production data reflects formation heterogeneity quantitatively.Currently,the calculated inter-well connectivity between pair wells is mainly used as a too...The inter-well connectivity calculated from reservoir dynamic production data reflects formation heterogeneity quantitatively.Currently,the calculated inter-well connectivity between pair wells is mainly used as a tool for water flood management but not for quantitative reservoir characterization.This study proposes an innovative,dynamic data integration workflow that can integrate inter-well connectivity with a static reservoir model.In the workflow,the first step is calculating the inter-well connectivity vectors from the reservoir pairwise injector and producer wells.The second step covers interpolation in the domain of interest.The third step is to update the permeability model based on the Bayesian updating method.The result of this study shows that integrating the calculated inter-well connectivity with the static models enhances model reliability and it also provides an insight to deeper geological understanding reflected from dynamic data integration in reservoir modeling.展开更多
The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling.The gap parameter is regarded as an effective tool to estimate the ground loss of tunnel...The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling.The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil.More specifically,x,which is a gap parameter component defined as the over(or insufficient)excavation due to the change in the posture of the shield machine,may contribute more to the uncertainty of the ground loss.However,the existing uncertainty characterization methods for x have several limitations and cannot explain the uncertain correlations between the relevant parameters.Along these lines,to better characterize the uncertainty of x,the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it.To attain this goal,1523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction,stratigraphic,and posture parameters were collected to form the database.According to the variance filter method,the mutual information method,and the value of the correlation coefficients,the original 44 parameters were reduced to 10 main parameters,which were unit weight,the stoke of the jacks(A,B,C,and D groups),the pressure of the pushing jacks(A,C groups),the chamber pressure,the rotation speed,and the total force.The multivariate probability distribution was constructed based on the Johnson system of distributions.Moreover,the distribution was satisfactorily verified in explaining the pairwise correlation between x and other parameters through 2 million simulation cases.At last,the distribution was used as a prior distribution to update the marginal distribution of x with any group of the relevant parameters known.The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.展开更多
This paper investigates the ordering decision problem for a short-life-cycle product under Bayesian updating. For a product characterized by a single manufacturing cycle and two selling periods, we depict a Two-Stage ...This paper investigates the ordering decision problem for a short-life-cycle product under Bayesian updating. For a product characterized by a single manufacturing cycle and two selling periods, we depict a Two-Stage （TS） ordering strategy with a stochastic dynamic programming model in the view of the whole system, and prove that the expected profit function of the whole system is concave on the first ordering quantity and the remedial ordering quantity, respectively. Then, the optimal ordering decision is developed. Finally, characteristics of the optimal ordering quantities are analyzed with several examples. Our results show that the suggested TS decision model is better than a Quick Response （QR） decision model.展开更多
Empirical models provide a practical way to estimate the displacements induced by excavations.However,there are uncertainties associated with the predictions of empirical models owing to:(a)the imperfect knowledge of ...Empirical models provide a practical way to estimate the displacements induced by excavations.However,there are uncertainties associated with the predictions of empirical models owing to:(a)the imperfect knowledge of the model and(b)the uncertainties of the input variables.The uncertainties of these models can be characterized by a bias factor which is defined as the ratio of the actual displacement to the predicted displacement.The bias factors associated with the C&O method and the KJHH model are evaluated using the Bayesian method and a database of 71 excavations in Shanghai.To improve the predictions of the maximum displacement,an adaptive algorithm is proposed using field performance data.The performance of the proposed algorithm is demonstrated by an example in which excavation-induced displacements are generated by finite element method in normally consolidated clays.The example shows that the developed algorithm can significantly improve the predictions by incorporating the field performance data.展开更多
Facing the challenge of attracting consumers and winning market share under the pro-liferation of TV stations and channels,the traditional TV stations often make some mar-keting strategies.However,how to evaluate the ...Facing the challenge of attracting consumers and winning market share under the pro-liferation of TV stations and channels,the traditional TV stations often make some mar-keting strategies.However,how to evaluate the effectiveness of different strategies and select the best one is a key issue.This study proposes to resolve this problem.We develop an innovative structural model to simulate the dynamic choices consumers make under two interactive behaviors:learning and forgetting.Learning behavior refers to updating programme quality assessment by using experience,while forgetting behavior prevents the use of previous experience.The Bayesian rules are employed to model learning behavior,and they are extended by incorporating an exponential decay function to mea-sure the effect of forgetting behavior.The structural model is tested and validated by using Hong Kong television viewing data.The empirical results show that when modeling consumer choice decisions,considering learning and forgetting behavior significantly improves the performance of the model in regard to rating prediction and marketing strategy evaluation.Five cases are simulated to show how the model is used to evaluate marketing strategies.Managerial implications are then discussed to guide the decision-making of traditional TV broadcasters and advertisers.展开更多
基金the financial support provided by the Natural Science Foundation of Jiangsu Province,China(Nos.BK20220687 and BK20201470)the National Natural Science Foundation of China(Nos.U1933202 and 12372079)The support provided by China Scholarship Council(No.201606830028)during the visit of Xin LI at the University of Toronto is also acknowledged and appreciated.
文摘Detection and repair of composite damage is crucial to ensure the safety and reliability of aircraft structures.A novel approach to quantitatively evaluate the repair tolerance of composite structures in civil aircraft based on Bayesian updating is presented.The method incorporates historical damage inspection data to determine the prior distribution of damage size,which is then updated with newly collected damage size data using Bayesian theory.Monte Carlo simulation is employed to investigate the probability of failure and estimate maintenance costs,considering various factors such as the frequency and timing of damage events,damage detection,structural strength,gust loads,and maintenance expenses throughout the lifecycle of composite structures.Safety and economic factors are considered to establish a lower threshold for repairs and an upper threshold for maintenance based on the occurrence of accidental impact damage.Verification of the effectiveness and feasibility of a quantitative assessment method for repair tolerance is conducted using damage statistics data from civil aircraft routes utilizing the structural skin panels of composite outer wing.The results demonstrate that the method proposed in conjunction with extensive simulations and full utilization of field damage inspection data can effectively simulate unexpected impact damage situations that may occur during civil aircraft service and evaluate the reliability and economic feasibility of the repair of structure.The research findings hold significant theoretical and practical value for the preparation of documents for continued airworthiness of composite structures,including structural repair manuals and maintenance programs.
基金the financial support from the Guangdong Provincial Department of Science and Technology(Grant No.2022A0505030019)the Science and Technology Development Fund,Macao SAR,China(File Nos.0056/2023/RIB2 and SKL-IOTSC-2021-2023).
文摘Recently,the application of Bayesian updating to predict excavation-induced deformation has proven successful and improved prediction accuracy significantly.However,updating the ground settlement profile,which is crucial for determining potential damage to nearby infrastructures,has received limited attention.To address this,this paper proposes a physics-guided simplified model combined with a Bayesian updating framework to accurately predict the ground settlement profile.The advantage of this model is that it eliminates the need for complex finite element modeling and makes the updating framework user-friendly.Furthermore,the model is physically interpretable,which can provide valuable references for construction adjustments.The effectiveness of the proposed method is demonstrated through two field case studies,showing that it can yield satisfactory predictions for the settlement profile.
文摘There exists model uncertainty of probability of detection for inspecting ship structures with nondestructive inspection techniques. Based on a comparison of several existing probability of detection (POD) models, a new probability of detection model is proposed for the updating of crack size distribution. Furthermore, the theoretical derivation shows that most existing probability of detection models are special cases of the new probability of detection model. The least square method is adopted for determining the values of parameters in the new POD model. This new model is also compared with other existing probability of detection models. The results indicate that the new probability of detection model can fit the inspection data better. This new probability of detection model is then applied to the analysis of the problem of crack size updating for offshore structures. The Bayesian updating method is used to analyze the effect of probability of detection models on the posterior distribution of a crack size. The results show that different probabilities of detection models generate different posterior distributions of a crack size for offshore structures.
文摘Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.
基金National Natural Science Foundation of China(No.71401072)Natural Science Foundation of Jiangsu Province,China(No.BK20130814)Fundamental Research Funds for the Central Universities,China(No.NS2013064)
文摘In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algorithm.In the proposed M-IMM algorithm,a new likelihood function is defined for the sake of updating flight mode probabilities,in which the influences of interacting to residual's mean error are taken into account and the assumption of likelihood function being a zero mean Gaussian function is discarded.Finally,the proposed M-IMM algorithm is applied to the simulation of the aircraft trajectory prediction,and the comparative studies are conducted to existing algorithms.The simulation results indicate the proposed M-IMM algorithm can predict aircraft trajectory more quickly and accurately.
文摘The inter-well connectivity calculated from reservoir dynamic production data reflects formation heterogeneity quantitatively.Currently,the calculated inter-well connectivity between pair wells is mainly used as a tool for water flood management but not for quantitative reservoir characterization.This study proposes an innovative,dynamic data integration workflow that can integrate inter-well connectivity with a static reservoir model.In the workflow,the first step is calculating the inter-well connectivity vectors from the reservoir pairwise injector and producer wells.The second step covers interpolation in the domain of interest.The third step is to update the permeability model based on the Bayesian updating method.The result of this study shows that integrating the calculated inter-well connectivity with the static models enhances model reliability and it also provides an insight to deeper geological understanding reflected from dynamic data integration in reservoir modeling.
基金support provided by the National Natural Science Foundation of China(Grant Nos.52078236 and 52122806)Guangzhou Metro Group Co.,Ltd(JT204-100111-23001)Chongqing Urban Investment Infrastructure Construction Co(Grant No.CQCT-JS-SC-GC-2022-0081).
文摘The probability analysis of ground deformation is becoming a trend to estimate and control the risk brought by shield tunnelling.The gap parameter is regarded as an effective tool to estimate the ground loss of tunnelling in soft soil.More specifically,x,which is a gap parameter component defined as the over(or insufficient)excavation due to the change in the posture of the shield machine,may contribute more to the uncertainty of the ground loss.However,the existing uncertainty characterization methods for x have several limitations and cannot explain the uncertain correlations between the relevant parameters.Along these lines,to better characterize the uncertainty of x,the multivariate probability distribution was developed in this work and a dynamic prediction was proposed for it.To attain this goal,1523 rings of the field data coming from the shield tunnel between Longqing Road and Baiyun Road in Kunming Metro Line 5 were utilized and 44 parameters including the construction,stratigraphic,and posture parameters were collected to form the database.According to the variance filter method,the mutual information method,and the value of the correlation coefficients,the original 44 parameters were reduced to 10 main parameters,which were unit weight,the stoke of the jacks(A,B,C,and D groups),the pressure of the pushing jacks(A,C groups),the chamber pressure,the rotation speed,and the total force.The multivariate probability distribution was constructed based on the Johnson system of distributions.Moreover,the distribution was satisfactorily verified in explaining the pairwise correlation between x and other parameters through 2 million simulation cases.At last,the distribution was used as a prior distribution to update the marginal distribution of x with any group of the relevant parameters known.The performance of the dynamic prediction was further validated by the field data of 3 shield tunnel cases.
基金This work was supported in part by Natioanl Natural Science Foundation of China under Grant No.70321001 Nature Science Foundation of Henan Province Education Committee under Grant No.2006120004 Natural Science Foundation for PhD of Henan Agricultural University under Grant No.30700300
文摘This paper investigates the ordering decision problem for a short-life-cycle product under Bayesian updating. For a product characterized by a single manufacturing cycle and two selling periods, we depict a Two-Stage （TS） ordering strategy with a stochastic dynamic programming model in the view of the whole system, and prove that the expected profit function of the whole system is concave on the first ordering quantity and the remedial ordering quantity, respectively. Then, the optimal ordering decision is developed. Finally, characteristics of the optimal ordering quantities are analyzed with several examples. Our results show that the suggested TS decision model is better than a Quick Response （QR） decision model.
文摘Empirical models provide a practical way to estimate the displacements induced by excavations.However,there are uncertainties associated with the predictions of empirical models owing to:(a)the imperfect knowledge of the model and(b)the uncertainties of the input variables.The uncertainties of these models can be characterized by a bias factor which is defined as the ratio of the actual displacement to the predicted displacement.The bias factors associated with the C&O method and the KJHH model are evaluated using the Bayesian method and a database of 71 excavations in Shanghai.To improve the predictions of the maximum displacement,an adaptive algorithm is proposed using field performance data.The performance of the proposed algorithm is demonstrated by an example in which excavation-induced displacements are generated by finite element method in normally consolidated clays.The example shows that the developed algorithm can significantly improve the predictions by incorporating the field performance data.
基金This research was financial supported by NSFC(No.71602089)Research Grants Council of the Hong Kong Special Administrative Region,China(No.11507817)+1 种基金Natural Science Foundation of Jiangsu Province,China(No.BK20160785)the Fundamental Research Funds for the Central Universities(NR2019015).
文摘Facing the challenge of attracting consumers and winning market share under the pro-liferation of TV stations and channels,the traditional TV stations often make some mar-keting strategies.However,how to evaluate the effectiveness of different strategies and select the best one is a key issue.This study proposes to resolve this problem.We develop an innovative structural model to simulate the dynamic choices consumers make under two interactive behaviors:learning and forgetting.Learning behavior refers to updating programme quality assessment by using experience,while forgetting behavior prevents the use of previous experience.The Bayesian rules are employed to model learning behavior,and they are extended by incorporating an exponential decay function to mea-sure the effect of forgetting behavior.The structural model is tested and validated by using Hong Kong television viewing data.The empirical results show that when modeling consumer choice decisions,considering learning and forgetting behavior significantly improves the performance of the model in regard to rating prediction and marketing strategy evaluation.Five cases are simulated to show how the model is used to evaluate marketing strategies.Managerial implications are then discussed to guide the decision-making of traditional TV broadcasters and advertisers.
