By using the concept of modified structural number(SNC)and deflection measurements,a simplified calculation methodology,that permits the structural condition evaluation of an existing pavement,is being proposed.The va...By using the concept of modified structural number(SNC)and deflection measurements,a simplified calculation methodology,that permits the structural condition evaluation of an existing pavement,is being proposed.The values of SNC and the curvature parameters were first determined through simulations using the ELSYM-5 software.Deflection measurements were carried out in experimental segments of Brazilian highways.The resilient moduli of each layer were determined from backcalculation using the ELMOD program for a three-layer system.Theoretical correlation models between SNC and the basin deformation parameter were determined and later,calibrated with the results of experimental sections.Utilizing the studied models,a good correlation was found between SNC,area parameter and maximum deflection,enabling the determination of SNC through deflection measurements and assisting in the diagnostic of structural condition of asphalt pavements.展开更多
Significant exploration progress has been made in ultra-deep clastic rocks in the Kuqa Depression,Tarim Basin,over recent years.A new round of comprehensive geological research has formed four new understandings:(1)Es...Significant exploration progress has been made in ultra-deep clastic rocks in the Kuqa Depression,Tarim Basin,over recent years.A new round of comprehensive geological research has formed four new understandings:(1)Establish structural model consisting of multi-detachment composite,multi-stage structural superposition and multi-layer deformation.Multi-stage structural traps are overlapped vertically,and a series of structural traps are discovered in underlying ultra-deep layers.(2)Five sets of high-quality large-scale source rocks of three types of organic phases are developed in the Triassic and Jurassic systems,and forming a good combination of source-reservoir-cap rocks in ultra-deep layers with three sets of large-scale regional reservoir and cap rocks.(3)The formation of large oil and gas fields is controlled by four factors which are source,reservoir,cap rocks and fault.Based on the spatial configuration relationship of these four factors,a new three-dimensional reservoir formation model for ultra-deep clastic rocks in the Kuqa Depression has been established.(4)The next key exploration fields for ultra-deep clastic rocks in the Kuqa Depression include conventional and unconventional oil and gas.The conventional oil and gas fields include the deep multi-layer oil-gas accumulation zone in Kelasu,tight sandstone gas of Jurassic Ahe Formation in the northern structural zone,multi-target layer lithological oil and gas reservoirs in Zhongqiu–Dina structural zone,lithologic-stratigraphic and buried hill composite reservoirs in south slope and other favorable areas.Unconventional oil and gas fields include deep coal rock gas of Jurassic Kezilenuer and Yangxia formations,Triassic Tariqike Formation and Middle-Lower Jurassic and Upper Triassic continental shale gas.The achievements have important reference significance for enriching the theory of ultra-deep clastic rock oil and gas exploration and guiding the future oil and gas exploration deployment.展开更多
The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testi...The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59.The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.展开更多
文摘By using the concept of modified structural number(SNC)and deflection measurements,a simplified calculation methodology,that permits the structural condition evaluation of an existing pavement,is being proposed.The values of SNC and the curvature parameters were first determined through simulations using the ELSYM-5 software.Deflection measurements were carried out in experimental segments of Brazilian highways.The resilient moduli of each layer were determined from backcalculation using the ELMOD program for a three-layer system.Theoretical correlation models between SNC and the basin deformation parameter were determined and later,calibrated with the results of experimental sections.Utilizing the studied models,a good correlation was found between SNC,area parameter and maximum deflection,enabling the determination of SNC through deflection measurements and assisting in the diagnostic of structural condition of asphalt pavements.
基金Supported by the National Natural Science Foundation of China(U22B6002)PetroChina Science and Technology Project(2023ZZ14).
文摘Significant exploration progress has been made in ultra-deep clastic rocks in the Kuqa Depression,Tarim Basin,over recent years.A new round of comprehensive geological research has formed four new understandings:(1)Establish structural model consisting of multi-detachment composite,multi-stage structural superposition and multi-layer deformation.Multi-stage structural traps are overlapped vertically,and a series of structural traps are discovered in underlying ultra-deep layers.(2)Five sets of high-quality large-scale source rocks of three types of organic phases are developed in the Triassic and Jurassic systems,and forming a good combination of source-reservoir-cap rocks in ultra-deep layers with three sets of large-scale regional reservoir and cap rocks.(3)The formation of large oil and gas fields is controlled by four factors which are source,reservoir,cap rocks and fault.Based on the spatial configuration relationship of these four factors,a new three-dimensional reservoir formation model for ultra-deep clastic rocks in the Kuqa Depression has been established.(4)The next key exploration fields for ultra-deep clastic rocks in the Kuqa Depression include conventional and unconventional oil and gas.The conventional oil and gas fields include the deep multi-layer oil-gas accumulation zone in Kelasu,tight sandstone gas of Jurassic Ahe Formation in the northern structural zone,multi-target layer lithological oil and gas reservoirs in Zhongqiu–Dina structural zone,lithologic-stratigraphic and buried hill composite reservoirs in south slope and other favorable areas.Unconventional oil and gas fields include deep coal rock gas of Jurassic Kezilenuer and Yangxia formations,Triassic Tariqike Formation and Middle-Lower Jurassic and Upper Triassic continental shale gas.The achievements have important reference significance for enriching the theory of ultra-deep clastic rock oil and gas exploration and guiding the future oil and gas exploration deployment.
基金supported by the National Key Research and Development Program of China (Grant No. 2020YFA0714300)the National Natural Science Foundation of China (Grant Nos. 61833005 and 62003084)the Natural Science Foundation of Jiangsu Province of China (Grant No.BK20200355)。
文摘The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59.The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.
