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Shiqin He^*, Jiaxing Zhao, Chunyue Wang, Hui Wang
Structural Durability & Health Monitoring, Vol.16, No.3, pp. 195-212, 2022, DOI:10.32604/sdhm.2022.08886 - 18 July 2022
Abstract In marine environments, the durability of reinforced concrete structures such as bridges, which suffer from the coupled effects of corrosion and fatigue damage, is significantly reduced. Fatigue loading can result in severe deterioration of the bonds between reinforcing steel bars and the surrounding concrete, particularly when reinforcing bars are corroded. Uniaxial tension testing was conducted under static loading and fatigue loading conditions to investigate the bonding characteristics between corroded reinforcing bars and concrete. An electrolyte corrosion technique was used to accelerate steel corrosion. The results show that the bond strength was reduced under fatigue loading, More >

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Anwer H. Hussein^*, Hussam K. Risan
Structural Durability & Health Monitoring, Vol.16, No.3, pp. 213-233, 2022, DOI:10.32604/sdhm.2022.018293 - 18 July 2022
Abstract Silos are strategical structures used to stockpile various types of granular materials. They are highly vulnerable to earthquake excitation and have been frequently reported to fail at a higher rate than any other industrial structure. The seismic response of silos within the near-fault region will suffer a complex combination of loadings due to the unique characteristics of the near-fault ground motions; which are usually associated with a large amplitude pulse at the beginning of either the velocity or the displacement time histories. This study aims to numerically evaluate the seismic response of reinforced concrete cylindrical… More >

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Xiaozhong Li^*, Caiyun Sun
Structural Durability & Health Monitoring, Vol.16, No.3, pp. 235-254, 2022, DOI:10.32604/sdhm.2022.013141 - 18 July 2022
Abstract During the construction of subway tunnels, safety issues should not be ignored, so it is necessary to prevent and resolve the risk in time and accurately. However, there are some shortcomings in the research of risk assessment, such as the subjectivity of initial data or the lack of scientific evaluation model, in order to solve the problem, this paper relies on the Changping section of the Guanhui Intercity Metro, in order to establish a dynamic risk-warning model for the construction process of subway tunnel with the CD-Bench composite method. First, a monitoring plan was equationted… More >

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E. Akshay¹, V. Sugumaran^1,*, M. Elangovan²
Structural Durability & Health Monitoring, Vol.16, No.3, pp. 255-270, 2022, DOI:10.32604/sdhm.2022.0271 - 18 July 2022
Abstract Tool wear is inevitable in daily machining process since metal cutting process involves the chip rubbing the tool surface after it has been cut by the tool edge. Tool wear dominantly influences the deterioration of surface finish, geometric and dimensional tolerances of the workpiece. Moreover, for complete utilization of cutting tools and reduction of machine downtime during the machining process, it becomes necessary to understand the development of tool wear and predict its status before happening. In this study, tool condition monitoring system was used to monitor the behavior of a single point cutting tool… More >

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853

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Sujit S. Pardeshi¹, Abhishek D. Patange¹, R. Jegadeeshwaran^2,*, Mayur R. Bhosale³
Structural Durability & Health Monitoring, Vol.16, No.3, pp. 271-290, 2022, DOI:10.32604/sdhm.2022.010622 - 18 July 2022
Abstract The regulation of tyre pressure is treated as a significant aspect of ‘tyre maintenance’ in the domain of autotronics. The manual supervision of a tyre pressure is typically an ignored task by most of the users. The existing instrumental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires regular manual conduct. Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition. In this perspective, the Machine Learning (ML) approach acts appropriate as it exhibits comparison of specific performance in the past with present, intended for predicting the… More >

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