Reasonable distribution of braking force is a factor for a smooth,safe,and comfortable braking of trains.A dynamic optimal allocation strategy of electric-air braking force is proposed in this paper to solve the probl...Reasonable distribution of braking force is a factor for a smooth,safe,and comfortable braking of trains.A dynamic optimal allocation strategy of electric-air braking force is proposed in this paper to solve the problem of the lack of consideration of adhesion difference of train wheelsets in the existing high-speed train electric-air braking force optimal allocation strategies.In this method,the braking strategy gives priority to the use of electric braking force.The force model of a single train in the braking process is analyzed to calculate the change of adhesion between the wheel and rail of each wheelset after axle load transfer,and then the adhesion of the train is estimated in real time.Next,with the goal of maximizing the total adhesion utilization ratio of trailer/motor vehicles,a linear programming distribution function is constructed.The proportional coefficient of adhesion utilization ratio of each train and the application upper limit of braking force in the function is updated according to the change time point of wheelset adhesion.Finally,the braking force is dynamically allocated.The simulation results of Matlab/Simulink show that the proposed algorithm not only uses the different adhesion limits of each trailer to reduce the total amount of braking force undertaken by the motor vehicle,but also considers the adhesion difference of each wheelset.The strategy can effectively reduce the risk and time of motor vehicles during the braking process and improve the stability of the train braking.展开更多
Equivalent axle load factor(EALF)is used to characterize the fatigue damage effect of multi-axle loads on asphalt pavement.EALF is calculated as the ratio of the pavement’s fatigue resistance under a single axle load...Equivalent axle load factor(EALF)is used to characterize the fatigue damage effect of multi-axle loads on asphalt pavement.EALF is calculated as the ratio of the pavement’s fatigue resistance under a single axle load to that under a multi-axle load.Existing studies use the same fatigue life function to predict the fatigue life of asphalt mixture under both single-and multi-axle loads,primarily focusing on the differences in pavement strains under these two configurations.However,strain waveforms in asphalt pavement caused by multi-axle loads differ from those under single-axle loads,altering the mixture’s fatigue behavior.To address this issue,this research tests the fatigue responses of asphalt mixtures under actual loading waveforms from single-axle,tandem-axle,and tridem-axle loads.Based on the test results,fatigue life functions are developed for each axle configuration and used to establish an updated EALF model.Since the applied fatigue life functions are based on test results from more realistic strain waveforms,the calculated EALFs provide more reliable predictions of the damaging effect of multi-axle loads on asphalt mixtures.展开更多
Axle box bearings serve as crucial components within the transmission system of high-speed trains.Their failure can directly impact the operational safety of these trains.Accurately determining the dynamic load experi...Axle box bearings serve as crucial components within the transmission system of high-speed trains.Their failure can directly impact the operational safety of these trains.Accurately determining the dynamic load experienced by bearings during the operation of high-speed trains can provide valuable boundary inputs for the study of bearing fatigue life and service performance,thereby holding significant engineering implications.In this study,we propose a high-speed train axle box bearing load estimation method(FMCC-DKF).This method is founded on the Kalman filtering technique of the Maximum Correntropy Criterion(MCC)and employs dummy measurement technology to enhance the stability of estimated loads.We develop a kernel size update algorithm to address the challenges associated with obtaining the key parameter,kernel size of MCC.Comparative analysis of the vertical and lateral loads of the axle box bearing obtained using FMCC-DKF,DKF,and AMCC-DKF,under both measurement noise-free and non-Gaussian noise conditions,is conducted to demonstrate the superiority of the proposed estimation method.The results indicate that the proposed FMCC-DKF method exhibits high estimation accuracy under both measurement noise-free and non-Gaussian noise interference,and maintains its high estimation accuracy despite changes in train speed.The proposed load estimation method demonstrates reliable performance within the low-frequency domain below 70 Hz.展开更多
Axle load data are an essential input for pavement design,yet for most North American agencies,there is uncertainty about the quality of axle load data obtained from weigh-inmotion(WIM)systems,the applicability of the...Axle load data are an essential input for pavement design,yet for most North American agencies,there is uncertainty about the quality of axle load data obtained from weigh-inmotion(WIM)systems,the applicability of these data for pavement design,and potential opportunities to integrate axle load data from disparate sources.This article presents a novel and practical methodology to evaluate the quality of axle load data from WIM systems and roadside weigh scales through a series of hierarchical analyses designed to test data validity.When applied using data from Manitoba,Canada,the methodology quantified the uncertainty of axle loads measured at the weigh scales and piezo-quartz WIM,concluding that both could be used for pavement design applications.Data collected at piezo-polymer WIM sites exhibited poorer data validity;however,application of site-specific temperature correction factors significantly improved data validity at these sites.The article describes how other data quality dimensions,including spatial coverage,temporal coverage,and long-term data availability,could be considered when determining the suitability of disparate axle load data sources for pavement design.Application of the methodology enables a pragmatic evaluation of the quality and limitations of commonlyavailable axle load data,revealing uncertainties and data needs relevant for pavement design practice.展开更多
To investigate the cost-effectiveness of different maintenance treatments of highways in Jiangsu Province, the historical pavement maintenance records, traffic load information and pavement performance data in the pav...To investigate the cost-effectiveness of different maintenance treatments of highways in Jiangsu Province, the historical pavement maintenance records, traffic load information and pavement performance data in the pavement management system （PMS） are recorded and analyzed. Compared with the growth model, the linear model, the logarithm model and the exponential model, the cubic model has higher regression accuracy R2 and it can capture the sigmoid shape of the deterioration curve. So it is selected to simulate the pavement rotting development. The benefit over cost ratio is calculated to quantify the treatment cost- effectiveness. The analysis results show that thin hot mix asphalt （HMA） overlays and micro surfacing are more cost- effective than the. other two treatments on light and moderate traffic roads. Hot in-place recycling and thick HMA overlays have much longer service lives and greater cost-effectiveness under heavy or extra heavy traffic.展开更多
The traffic of overloaded trucks is a critical problem in highways.It affects pavement performance life,reduces the service life of bridges,and has a negative impact on road safety,average speed and level of service.T...The traffic of overloaded trucks is a critical problem in highways.It affects pavement performance life,reduces the service life of bridges,and has a negative impact on road safety,average speed and level of service.There are several practices to prevent the truck overloading issue,i.e.,enforcement activities to verify the truck’s compliance with the legal weight limits.This paper investigates the development of a method that uses available weigh-in-motion(WIM)data to identify overloaded truck weight and travel patterns.The proposed approach is based on regression trees method,a simple and easily understandable analytic tool used to build prediction models from a large set of data.An overall analysis of the overloaded truck regression tree model shows that the most important variable to classify and predict overloading is the truck type.Regarding the axle overloading,the most significant variable is the time of the day(most of the overloaded trucks travel at late night or early morning).The regression tree results can be used to optimize the efficiency of administration activities by planning truck enforcement operations based on the more critical scenarios.Also,the results improve the knowledge about the load characteristics of trucks,which can lead to more effective pavement management systems and more assertive pavement structure designs.展开更多
The success of the mechanistic-empirical pavement design guide implementation depends largely on a high level of accuracy associated with the information supplied as design inputs.Truck axle load spectra play a critic...The success of the mechanistic-empirical pavement design guide implementation depends largely on a high level of accuracy associated with the information supplied as design inputs.Truck axle load spectra play a critical role in all aspects of the pavement structure design.Inaccurate traffic information will yield an incorrect estimate of pavement thick-ness,which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness.The primary objective of this study was to create an accurate traffic design input module,and thus to improve the quality of pavement designs.The traffic input module was created with the most recent data to better reflect the axle load spectra for pavement design.The unclassified vehicles by weigh-in-motion devices were analyzed and a neural-network-model-based classification method was utilized to determine the appropriate allocations of unclassified vehicles to truck classes.The updated truck traffic information includes average annual daily truck traffic,truck volume monthly adjustment factors,truck volume lane distribution factors,truck volume directional distribution factors,truck volume class distributions,traffic volume hourly distribution factors,distributions of for single-axle,tandem-axle,tridem-axle,and quad-axle loads,average axle weight,average axle spacing,and average number of axle types.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62173137,52172403,62303178).
文摘Reasonable distribution of braking force is a factor for a smooth,safe,and comfortable braking of trains.A dynamic optimal allocation strategy of electric-air braking force is proposed in this paper to solve the problem of the lack of consideration of adhesion difference of train wheelsets in the existing high-speed train electric-air braking force optimal allocation strategies.In this method,the braking strategy gives priority to the use of electric braking force.The force model of a single train in the braking process is analyzed to calculate the change of adhesion between the wheel and rail of each wheelset after axle load transfer,and then the adhesion of the train is estimated in real time.Next,with the goal of maximizing the total adhesion utilization ratio of trailer/motor vehicles,a linear programming distribution function is constructed.The proportional coefficient of adhesion utilization ratio of each train and the application upper limit of braking force in the function is updated according to the change time point of wheelset adhesion.Finally,the braking force is dynamically allocated.The simulation results of Matlab/Simulink show that the proposed algorithm not only uses the different adhesion limits of each trailer to reduce the total amount of braking force undertaken by the motor vehicle,but also considers the adhesion difference of each wheelset.The strategy can effectively reduce the risk and time of motor vehicles during the braking process and improve the stability of the train braking.
基金supported by grants from the National Key R&D Program of China(No.2023YFA1008904)the Guangxi Key Research and Development Project(No.AB22080091)the National Natural Science Foundation of China(No.52108412).
文摘Equivalent axle load factor(EALF)is used to characterize the fatigue damage effect of multi-axle loads on asphalt pavement.EALF is calculated as the ratio of the pavement’s fatigue resistance under a single axle load to that under a multi-axle load.Existing studies use the same fatigue life function to predict the fatigue life of asphalt mixture under both single-and multi-axle loads,primarily focusing on the differences in pavement strains under these two configurations.However,strain waveforms in asphalt pavement caused by multi-axle loads differ from those under single-axle loads,altering the mixture’s fatigue behavior.To address this issue,this research tests the fatigue responses of asphalt mixtures under actual loading waveforms from single-axle,tandem-axle,and tridem-axle loads.Based on the test results,fatigue life functions are developed for each axle configuration and used to establish an updated EALF model.Since the applied fatigue life functions are based on test results from more realistic strain waveforms,the calculated EALFs provide more reliable predictions of the damaging effect of multi-axle loads on asphalt mixtures.
基金National Key R&D Program of China(Grant numbers 2022YFB4301201-11,2022YFB4301203-05)National Natural Science Foundation of China(Grant number 52202464).
文摘Axle box bearings serve as crucial components within the transmission system of high-speed trains.Their failure can directly impact the operational safety of these trains.Accurately determining the dynamic load experienced by bearings during the operation of high-speed trains can provide valuable boundary inputs for the study of bearing fatigue life and service performance,thereby holding significant engineering implications.In this study,we propose a high-speed train axle box bearing load estimation method(FMCC-DKF).This method is founded on the Kalman filtering technique of the Maximum Correntropy Criterion(MCC)and employs dummy measurement technology to enhance the stability of estimated loads.We develop a kernel size update algorithm to address the challenges associated with obtaining the key parameter,kernel size of MCC.Comparative analysis of the vertical and lateral loads of the axle box bearing obtained using FMCC-DKF,DKF,and AMCC-DKF,under both measurement noise-free and non-Gaussian noise conditions,is conducted to demonstrate the superiority of the proposed estimation method.The results indicate that the proposed FMCC-DKF method exhibits high estimation accuracy under both measurement noise-free and non-Gaussian noise interference,and maintains its high estimation accuracy despite changes in train speed.The proposed load estimation method demonstrates reliable performance within the low-frequency domain below 70 Hz.
基金financial contributions of Manitoba Infrastructure and the Natural Sciences and Engineering Research Council(NSERC)of Canada(grant number RGPIN/418427-2012)。
文摘Axle load data are an essential input for pavement design,yet for most North American agencies,there is uncertainty about the quality of axle load data obtained from weigh-inmotion(WIM)systems,the applicability of these data for pavement design,and potential opportunities to integrate axle load data from disparate sources.This article presents a novel and practical methodology to evaluate the quality of axle load data from WIM systems and roadside weigh scales through a series of hierarchical analyses designed to test data validity.When applied using data from Manitoba,Canada,the methodology quantified the uncertainty of axle loads measured at the weigh scales and piezo-quartz WIM,concluding that both could be used for pavement design applications.Data collected at piezo-polymer WIM sites exhibited poorer data validity;however,application of site-specific temperature correction factors significantly improved data validity at these sites.The article describes how other data quality dimensions,including spatial coverage,temporal coverage,and long-term data availability,could be considered when determining the suitability of disparate axle load data sources for pavement design.Application of the methodology enables a pragmatic evaluation of the quality and limitations of commonlyavailable axle load data,revealing uncertainties and data needs relevant for pavement design practice.
基金The Science and Technology Project of Jiangsu Provincial Communications Department(No.7621000078)
文摘To investigate the cost-effectiveness of different maintenance treatments of highways in Jiangsu Province, the historical pavement maintenance records, traffic load information and pavement performance data in the pavement management system （PMS） are recorded and analyzed. Compared with the growth model, the linear model, the logarithm model and the exponential model, the cubic model has higher regression accuracy R2 and it can capture the sigmoid shape of the deterioration curve. So it is selected to simulate the pavement rotting development. The benefit over cost ratio is calculated to quantify the treatment cost- effectiveness. The analysis results show that thin hot mix asphalt （HMA） overlays and micro surfacing are more cost- effective than the. other two treatments on light and moderate traffic roads. Hot in-place recycling and thick HMA overlays have much longer service lives and greater cost-effectiveness under heavy or extra heavy traffic.
基金The authors thank the Arteris S.A.(Autopista Fernao Dias and Centro de Desenvolvimento Tecnologico),ANTT(Agencia Nacional de Transportes Terrestres),and CNPq(Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)for supporting this research.
文摘The traffic of overloaded trucks is a critical problem in highways.It affects pavement performance life,reduces the service life of bridges,and has a negative impact on road safety,average speed and level of service.There are several practices to prevent the truck overloading issue,i.e.,enforcement activities to verify the truck’s compliance with the legal weight limits.This paper investigates the development of a method that uses available weigh-in-motion(WIM)data to identify overloaded truck weight and travel patterns.The proposed approach is based on regression trees method,a simple and easily understandable analytic tool used to build prediction models from a large set of data.An overall analysis of the overloaded truck regression tree model shows that the most important variable to classify and predict overloading is the truck type.Regarding the axle overloading,the most significant variable is the time of the day(most of the overloaded trucks travel at late night or early morning).The regression tree results can be used to optimize the efficiency of administration activities by planning truck enforcement operations based on the more critical scenarios.Also,the results improve the knowledge about the load characteristics of trucks,which can lead to more effective pavement management systems and more assertive pavement structure designs.
基金supported in part by the Joint Transportation Research Program administered by the Indiana Department of Transportation and Purdue University.
文摘The success of the mechanistic-empirical pavement design guide implementation depends largely on a high level of accuracy associated with the information supplied as design inputs.Truck axle load spectra play a critical role in all aspects of the pavement structure design.Inaccurate traffic information will yield an incorrect estimate of pavement thick-ness,which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness.The primary objective of this study was to create an accurate traffic design input module,and thus to improve the quality of pavement designs.The traffic input module was created with the most recent data to better reflect the axle load spectra for pavement design.The unclassified vehicles by weigh-in-motion devices were analyzed and a neural-network-model-based classification method was utilized to determine the appropriate allocations of unclassified vehicles to truck classes.The updated truck traffic information includes average annual daily truck traffic,truck volume monthly adjustment factors,truck volume lane distribution factors,truck volume directional distribution factors,truck volume class distributions,traffic volume hourly distribution factors,distributions of for single-axle,tandem-axle,tridem-axle,and quad-axle loads,average axle weight,average axle spacing,and average number of axle types.
