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

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 to predict flank wear. A… More >

Anwer H. Hussein^*, Hussam K. Risan

Structural Durability & Health Monitoring, Vol.16, No.3, pp. 213-233, 2022, DOI:10.32604/sdhm.2022.018293

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 silos under near-fault ground motions… More >

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

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 same in near future. The… More >

Guansi Liu^1,2, Keqin Ding^2,*, Hui Jin^1,*, Fangxiong Tang², Li Chen²

Structural Durability & Health Monitoring, Vol.16, No.2, pp. 145-161, 2022, DOI:10.32604/sdhm.2022.022241

Abstract With the construction of automated docks, health monitoring technology as a parallel safety assurance technology for unmanned hoisting machinery has become a hot spot in the development of the industry. Hoisting machinery has a huge structure and numerous welded joints. The complexity and nonlinearity of the welded structure itself makes the structural failure parts random and difficult to arrange for monitoring sensors. In order to solve the problem of effectiveness and stability of the sensor arrangement method for monitoring the structure of hoisting machinery. Using the global and local search capabilities enhanced by the complementary search mechanism, a structural vibration… More >

Yisu Xi¹, Binkai Shi², Wei Xu^1,3,*, Jing Ge⁴, Huaxin Zhu⁵, Dragoslav Sumarac^6,7

Structural Durability & Health Monitoring, Vol.16, No.2, pp. 163-177, 2022, DOI:10.32604/sdhm.2022.022082

Abstract With the aid of non-contact measurements of vibrating surfaces through laser scanning, operating deflection shapes (ODSs) with high spatial resolutions can be used to graphically characterize damage in plane structures. Although numerous damage identification approaches relying on laser-measured ODSs have been developed for plate-type structures, they cannot be directly applied to circular cylinders due to the gap between equations of motions of plates and circular cylinders. To fill this gap, a novel approach is proposed in this study for damage identification of circular cylinders. Damage-induced discontinuities of the derivatives of ODSs can be used to graphically manifest the occurrence of… More >

Yongfeng Fang^1,3, Kong Fah Tee^2,*

Structural Durability & Health Monitoring, Vol.16, No.2, pp. 129-143, 2022, DOI:10.32604/sdhm.2022.020301

Abstract In this article, structural probabilistic and non-probabilistic reliability have been evaluated and compared under big data condition. Firstly, the big data is collected via structural monitoring and analysis. Big data is classified into different types according to the regularities of the distribution of data. The different stresses which have been subjected by the structure are used in this paper. Secondly, the structural interval reliability and probabilistic prediction models are established by using the stress-strength interference theory under big data of random loads after the stresses and structural strength are comprehensively considered. Structural reliability is computed by using various stress types,… More >

Krishna Kumar Maurya^*, Anupam Rawat, Rama Shanker

Structural Durability & Health Monitoring, Vol.16, No.2, pp. 97-128, 2022, DOI:10.32604/sdhm.2022.015732

Abstract Structural health monitoring (SHM) is a process for determination of presence, location, severity of damages and remaining life of the infrastructures. SHM is widely applied in aerospace, mechanical and civil engineering systems to assess the conditions of structures to improve the operation, safety, serviceability and reliability, respectively. There are various SHM techniques for monitoring the health of structures such as global response based and local techniques. Damages occur in the structures due to its inability to withstand intended design loadings, physical environment and chemical environment. Therefore, damage identification is necessary to improve the durability of the structures for protection against… More >

Kelong Huang, Jie Yan, Lei Zhang^*, Faye Zhang, Mingshun Jiang, Qingmei Sui

Structural Durability & Health Monitoring, Vol.16, No.2, pp. 179-194, 2022, DOI: 10.32604/sdhm.2022.018202

Abstract A structural displacement field reconstruction method is proposed to aim at the problems of deformation monitoring and displacement field reconstruction of flexible plate-like structures in the aerospace field. This method combines the deep neural network model of the cross-layer connection structure with the fiber grating sensor network. This paper first introduces the principle of strain detection of fiber grating sensor, studies the mapping relationship between strain and displacement, and proposes a strain-displacement conversion model based on an improved neural network. Then the intelligent structure deformation monitoring system is built. By controlling the stepping distance of the motor to produce different… More >

Tareq Al-hababi^1,2, Nizar Faisal Alkayem^1,3, Li Cui⁴, Shixiang Zhang⁵, Cong Liu⁶, Maosen Cao^1,2,*

Structural Durability & Health Monitoring, Vol.16, No.1, pp. 15-35, 2022, DOI:10.32604/sdhm.2022.020418

Abstract A significant amount of research is concerned with dynamic modal parameters for damage detection of structural conditions due to their simplicity in use and feasibility. However, their use for damage detection should be performed with special attention, particularly in operational and environmental conditions subjected to temperature changes. Beams in construction industries experience different loading types, such as temperature changes leading to crack initiation and propagation. Changed physical and dynamic properties such as natural frequencies and mode shapes indicate that damage has occurred within the structures. In this study, vibration analysis of cantilever and cantilever simply supported beams has been carried… More >

Zhanjun Wu¹, Tengteng Li¹, Jiachen Zhang², Yifan Wu³, Jianle Li¹, Lei Yang¹, Hao Xu^1,*

Structural Durability & Health Monitoring, Vol.16, No.1, pp. 1-14, 2022, DOI:10.32604/sdhm.2022.019554

Abstract Shape sensing as a crucial component of structural health monitoring plays a vital role in real-time actuation and control of smart structures, and monitoring of structural integrity. As a model-based method, the inverse finite element method (iFEM) has been proved to be a valuable shape sensing tool that is suitable for complex structures. In this paper, we propose a novel approach for the shape sensing of thin shell structures with iFEM. Considering the structural form and stress characteristics of thin-walled structure, the error function consists of membrane and bending section strains only which is consistent with the Kirchhoff–Love shell theory.… More >