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Italy features a high concentration of sites inscribed on the UNESCO World Heritage List. 58 Italian sites are recognized as the Intangible Cultural Heritage of Humanity placing.  Also, Italy is characterized by a high exposure to natural hazards. Structural Health Monitoring (SHM) can be considered a nondestructive technique for structural assessment of architectural heritage, suitable for conservation according to the principle of minimum intervention. Given the complexity of the main technical activities to be performed after an earthquake, there is a need for more accurate methods based on Structural Health Monitoring.

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  • Open AccessOpen Access

    ARTICLE

    An Overview of Seismic Risk Management for Italian Architectural Heritage

    Lucio Nobile*
    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 353-368, 2023, DOI:10.32604/sdhm.2023.028247
    Abstract The frequent occurrence of seismic events in Italy poses a strategic problem that involves either the culture of preservation of historical heritage or the civil protection action aimed to reduce the risk to people and goods (buildings, bridges, dams, slopes, etc.). Most of the Italian architectural heritage is vulnerable to earthquakes, identifying the vulnerability as the inherent predisposition of the masonry building to suffer damage and collapse during an earthquake. In fact, the structural concept prevailing in these ancient masonry buildings is aimed at ensuring prevalent resistance to vertical gravity loads. Rarely do these ancient… More >

  • Open AccessOpen Access

    ARTICLE

    Ensemble 1D DenseNet Damage Identification Method Based on Vibration Acceleration

    Chun Sha1,*, Chaohui Yue2, Wenchen Wang3
    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 369-381, 2023, DOI:10.32604/sdhm.2023.027948
    Abstract Convolution neural networks in deep learning can solve the problem of damage identification based on vibration acceleration. By combining multiple 1D DenseNet submodels, a new ensemble learning method is proposed to improve identification accuracy. 1D DenseNet is built using standard 1D CNN and DenseNet basic blocks, and the acceleration data obtained from multiple sampling points is brought into the 1D DenseNet training to generate submodels after offset sampling. When using submodels for damage identification, the voting method ideas in ensemble learning are used to vote on the results of each submodel, and then vote centrally. More >

  • Open AccessOpen Access

    ARTICLE

    A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

    Guangfei Jia*, Fengwei Guo, Zhe Wu, Suxiao Cui, Jiajun Yang
    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 383-405, 2023, DOI:10.32604/sdhm.2023.026885
    Abstract With the development of multi-signal monitoring technology, the research on multiple signal analysis and processing has become a hot subject. Mechanical equipment often works under variable working conditions, and the acquired vibration signals are often non-stationary and nonlinear, which are difficult to be processed by traditional analysis methods. In order to solve the noise reduction problem of multiple signals under variable speed, a COT-DCS method combining the Computed Order Tracking (COT) based on Chirplet Path Pursuit (CPP) and Distributed Compressed Sensing (DCS) is proposed. Firstly, the instantaneous frequency (IF) is extracted by CPP, and the… More >

    Graphic Abstract

    A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

  • Open AccessOpen Access

    ARTICLE

    Quantitative Detection of Corrosion State of Concrete Internal Reinforcement Based on Metal Magnetic Memory

    Zhongguo Tang1, Haijin Zhuo1, Beian Li1, Xiaotao Ma2, Siyu Zhao2, Kai Tong2,*
    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 407-431, 2023, DOI:10.32604/sdhm.2023.026033
    Abstract Corrosion can be very harmful to the service life and several properties of reinforced concrete structures. The metal magnetic memory (MMM) method, as a newly developed spontaneous magnetic flux leakage (SMFL) non-destructive testing (NDT) technique, is considered a potentially viable method for detecting corrosion damage in reinforced concrete members. To this end, in this paper, the indoor electrochemical method was employed to accelerate the corrosion of outsourced concrete specimens with different steel bar diameters, and the normal components BBz and its gradient of the SMFL fields on the specimen surfaces were investigated based on the metal… More >

  • Open AccessOpen Access

    ARTICLE

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

    Tiantian Liang*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang
    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331
    Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga… More >

    Graphic Abstract

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

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