To investigate the evolution of load-bearing characteristics of pre-stressed beams throughout their service life and to provide a basis for accurately assessing the actual working state of damaged pre-stressed concret...To investigate the evolution of load-bearing characteristics of pre-stressed beams throughout their service life and to provide a basis for accurately assessing the actual working state of damaged pre-stressed concrete T-beams,destructive tests were conducted on full-scale pre-stressed concrete beams.Based on the measurement and ana-lysis of beam deflection,strain,and crack development under various loading levels during the research tests,combined with the verification coefficient indicators specified in the codes,the verification coefficients of bridges at different stages of damage can be examined.The results indicate that the T-beams experience complete,incom-plete linear,and non-linear stages during the destructive test process.In the complete linear elastic stage,both the deflection and bottom strain verification coefficients comply with the specifications,indicating a good structural load-bearing capacity no longer adheres to the code’s requirements.In the non-linear stage,both coefficients exhi-bit a sharp increase,resulting in a further decrease in the structure’s load-bearing capacity.According to the pro-visions of the current code,the beam can be in the incomplete linear stage when both values fall within the code’s specified range.The strain verification coefficient sourced from the compression zone at the bottom of theflange is not recommended for assessing the bridge’s load-bearing capacity.展开更多
Evaluating the performance of existing concrete structures is essential in civil engineering,with compressive strength serving as an indicator of performance.Non-destructive testing(NDT)techniques are commonly employe...Evaluating the performance of existing concrete structures is essential in civil engineering,with compressive strength serving as an indicator of performance.Non-destructive testing(NDT)techniques are commonly employed due to their cost-effectiveness and the ability to assess structural integrity without causing damage.However,NDT methods often yield less accurate results than destructive testing(DT),which,although highly reliable,is costly and invasive.To address this limitation,recent research has focused on developing predictive models that correlate DT and NDT outcomes using machine learning techniques.This study explores the application of Support Vector Machine(SVM)models,enhanced with optimization techniques,to improve prediction accuracy.Experimental concrete practical samples,ranging from M10 to M40 grade,were prepared and tested at 14 and 28 days of curing,totaling 126 laboratory specimens.Additionally,231 field samples were collected from a 20-year-old structure to reflect in situ conditions.The performance of SVM was improved using optimization algorithms such as Bayesian Optimization and Genetic Algorithms(GA).Among various kernel functions tested,the Gaussian non-linear kernel proved most effective in modeling the complex relationship between NDT and DT results.The SVM model optimized using Bayesian methods and a Gaussian kernel achieved superior performance,with a high coefficient of determination(R²=0.9771)and significantly lower error metrics,including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).Bayesian-optimized SVM with a Gaussian kernel offers a highly accurate and practical tool for predicting compressive strength from NDT data,enhancing decision-making in structural assessment.展开更多
Air blasts induced by rock-ice avalanches are common natural phenomena known for their far-field destructive impact.In this study,remote sensing images,eyewitness videos and numerical modelling were comprehensively ap...Air blasts induced by rock-ice avalanches are common natural phenomena known for their far-field destructive impact.In this study,remote sensing images,eyewitness videos and numerical modelling were comprehensively applied to analyze the initiation and propagation of the 2021 Chamoli avalancheinduced air blast.Our findings indicate that air blasts are observed from the avalanche source area to the Rishiganga valley,but nearly disappear in the Dhauliganga valley.The most intense air blast is concentrated on the left side of Ronti Gad valley,with maximum velocity and pressure estimated at over 70 m/s and 20 kPa,respectively.Such high pressure results in widespread tree breakage in the area.Based on the analysis of the Chamoli event,we further discussed the potential contribution of the avalanche flow regime,avalanche dynamics and geomorphology to the destructive potential of air blasts.Rapidly moved sliding mass can impart the air blast a high initial momentum,and this process will be exaggerated when the avalanche impacts valley walls at bends.However,when the rock-ice avalanche transforms into a debris-enriched flash flood,free water within the flowing mass can displace air,inhibiting the generation of air blasts.Our work offers new insights into the generation and propagation of rock-ice avalanche-induced air blasts,underscoring the importance of including this type of hazard during avalanche risk assessment in high-altitude glacial regions.展开更多
高速铁路轮轨系统在服役过程中产生的疲劳裂纹是威胁行车安全的重大隐患。传统的无损检测方法难以有效识别处于闭合或半闭合状态的早期微小裂纹,尤其是在列车运行载荷作用下的动态工况。针对这一挑战,本文提出并系统研究了一种基于涡流...高速铁路轮轨系统在服役过程中产生的疲劳裂纹是威胁行车安全的重大隐患。传统的无损检测方法难以有效识别处于闭合或半闭合状态的早期微小裂纹,尤其是在列车运行载荷作用下的动态工况。针对这一挑战,本文提出并系统研究了一种基于涡流脉冲热成像(Eddy Current Pulsed Thermography,ECPT)技术的轮轨疲劳裂纹检测方法。研究首先构建了负载作用下的非稳态疲劳裂纹多物理场模型,通过有限元仿真与实验相结合,深入探究了局部接触(闭合)裂纹的涡流-热响应机理,并揭示了裂纹闭合深度与表面温度场特征(如等温线内凹现象)之间的定量关系。在此基础上,自主研制了适用于车轮与钢轨的动态ECPT检测平台及专用磁轭传感器,并开展了高铁轮轨实物的动态检测试验。结果表明,所提方法不仅能有效区分开口与闭合裂纹,还能对不同深度、不同尺寸的疲劳裂纹进行可靠检出,检出深度范围可达0.35mm至5mm。结合主成分分析(PCA)与张量分解等图像增强算法,显著提升了缺陷的信噪比与可视化效果,为高铁轮轨疲劳裂纹的在线、高效、精准检测提供了重要的理论依据与技术支撑。展开更多
Unidirectional carbon/carbon（C/C） composites modified with in situ grown carbon nanofibers（CNFs） were prepared by catalysis chemical vapor deposition. The effect of in situ grown CNFs on the flexural properties of...Unidirectional carbon/carbon（C/C） composites modified with in situ grown carbon nanofibers（CNFs） were prepared by catalysis chemical vapor deposition. The effect of in situ grown CNFs on the flexural properties of the C/C composites was investigated by detailed analyses of destructive process. The results show that there is a sharp increase in the flexural load-displacement curve in the axial direction of the CNF-C/C composites, followed by a serrated yielding phenomenon similar to the plastic materials. The failure mode of the C/C composites modified with in situ grown CNFs is changed from the pull-out of single fiber to the breaking of fiber bundles. The existence of interfacial layer composed by middle-textured pyrocarbon, CNFs and high-textured pyrocarbon can block the crack propagation and change the propagation direction of the main crack, which leads to the higher flexural strength and modulus of C/C composites.展开更多
文摘To investigate the evolution of load-bearing characteristics of pre-stressed beams throughout their service life and to provide a basis for accurately assessing the actual working state of damaged pre-stressed concrete T-beams,destructive tests were conducted on full-scale pre-stressed concrete beams.Based on the measurement and ana-lysis of beam deflection,strain,and crack development under various loading levels during the research tests,combined with the verification coefficient indicators specified in the codes,the verification coefficients of bridges at different stages of damage can be examined.The results indicate that the T-beams experience complete,incom-plete linear,and non-linear stages during the destructive test process.In the complete linear elastic stage,both the deflection and bottom strain verification coefficients comply with the specifications,indicating a good structural load-bearing capacity no longer adheres to the code’s requirements.In the non-linear stage,both coefficients exhi-bit a sharp increase,resulting in a further decrease in the structure’s load-bearing capacity.According to the pro-visions of the current code,the beam can be in the incomplete linear stage when both values fall within the code’s specified range.The strain verification coefficient sourced from the compression zone at the bottom of theflange is not recommended for assessing the bridge’s load-bearing capacity.
文摘Evaluating the performance of existing concrete structures is essential in civil engineering,with compressive strength serving as an indicator of performance.Non-destructive testing(NDT)techniques are commonly employed due to their cost-effectiveness and the ability to assess structural integrity without causing damage.However,NDT methods often yield less accurate results than destructive testing(DT),which,although highly reliable,is costly and invasive.To address this limitation,recent research has focused on developing predictive models that correlate DT and NDT outcomes using machine learning techniques.This study explores the application of Support Vector Machine(SVM)models,enhanced with optimization techniques,to improve prediction accuracy.Experimental concrete practical samples,ranging from M10 to M40 grade,were prepared and tested at 14 and 28 days of curing,totaling 126 laboratory specimens.Additionally,231 field samples were collected from a 20-year-old structure to reflect in situ conditions.The performance of SVM was improved using optimization algorithms such as Bayesian Optimization and Genetic Algorithms(GA).Among various kernel functions tested,the Gaussian non-linear kernel proved most effective in modeling the complex relationship between NDT and DT results.The SVM model optimized using Bayesian methods and a Gaussian kernel achieved superior performance,with a high coefficient of determination(R²=0.9771)and significantly lower error metrics,including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).Bayesian-optimized SVM with a Gaussian kernel offers a highly accurate and practical tool for predicting compressive strength from NDT data,enhancing decision-making in structural assessment.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2244227,42277126 and 41977215).
文摘Air blasts induced by rock-ice avalanches are common natural phenomena known for their far-field destructive impact.In this study,remote sensing images,eyewitness videos and numerical modelling were comprehensively applied to analyze the initiation and propagation of the 2021 Chamoli avalancheinduced air blast.Our findings indicate that air blasts are observed from the avalanche source area to the Rishiganga valley,but nearly disappear in the Dhauliganga valley.The most intense air blast is concentrated on the left side of Ronti Gad valley,with maximum velocity and pressure estimated at over 70 m/s and 20 kPa,respectively.Such high pressure results in widespread tree breakage in the area.Based on the analysis of the Chamoli event,we further discussed the potential contribution of the avalanche flow regime,avalanche dynamics and geomorphology to the destructive potential of air blasts.Rapidly moved sliding mass can impart the air blast a high initial momentum,and this process will be exaggerated when the avalanche impacts valley walls at bends.However,when the rock-ice avalanche transforms into a debris-enriched flash flood,free water within the flowing mass can displace air,inhibiting the generation of air blasts.Our work offers new insights into the generation and propagation of rock-ice avalanche-induced air blasts,underscoring the importance of including this type of hazard during avalanche risk assessment in high-altitude glacial regions.
文摘高速铁路轮轨系统在服役过程中产生的疲劳裂纹是威胁行车安全的重大隐患。传统的无损检测方法难以有效识别处于闭合或半闭合状态的早期微小裂纹,尤其是在列车运行载荷作用下的动态工况。针对这一挑战,本文提出并系统研究了一种基于涡流脉冲热成像(Eddy Current Pulsed Thermography,ECPT)技术的轮轨疲劳裂纹检测方法。研究首先构建了负载作用下的非稳态疲劳裂纹多物理场模型,通过有限元仿真与实验相结合,深入探究了局部接触(闭合)裂纹的涡流-热响应机理,并揭示了裂纹闭合深度与表面温度场特征(如等温线内凹现象)之间的定量关系。在此基础上,自主研制了适用于车轮与钢轨的动态ECPT检测平台及专用磁轭传感器,并开展了高铁轮轨实物的动态检测试验。结果表明,所提方法不仅能有效区分开口与闭合裂纹,还能对不同深度、不同尺寸的疲劳裂纹进行可靠检出,检出深度范围可达0.35mm至5mm。结合主成分分析(PCA)与张量分解等图像增强算法,显著提升了缺陷的信噪比与可视化效果,为高铁轮轨疲劳裂纹的在线、高效、精准检测提供了重要的理论依据与技术支撑。
基金Project(2011CB605804)supported by the National Basic Research Program of ChinaProject(51165006)supported by the National Natural Science Foundation of China+1 种基金Project(BY2013015-32)supported by Cooperative Innovation Fund-Prospective Project of Jiangsu Province,ChinaProject(JUSRP1045)supported by the Fundamental Research Funds for the Central Universities,China
文摘Unidirectional carbon/carbon（C/C） composites modified with in situ grown carbon nanofibers（CNFs） were prepared by catalysis chemical vapor deposition. The effect of in situ grown CNFs on the flexural properties of the C/C composites was investigated by detailed analyses of destructive process. The results show that there is a sharp increase in the flexural load-displacement curve in the axial direction of the CNF-C/C composites, followed by a serrated yielding phenomenon similar to the plastic materials. The failure mode of the C/C composites modified with in situ grown CNFs is changed from the pull-out of single fiber to the breaking of fiber bundles. The existence of interfacial layer composed by middle-textured pyrocarbon, CNFs and high-textured pyrocarbon can block the crack propagation and change the propagation direction of the main crack, which leads to the higher flexural strength and modulus of C/C composites.
