Open Access
ISSN:1930-2983(print)
ISSN:1930-2991(online)
Publication Frequency:Bi-monthly
In order to maintain a reasonable cost for large scale structures such as airframes, offshore structures, nuclear plants etc., it is generally accepted that improved methods for structural integrity and durability assessment are required. Structural Health Monitoring (SDHM) had emerged as an active area of research for fatigue life and damage accumulation prognostics.
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Refers to the articles published on the journal within the last three years that have gained the most viewing times to date (Statistics provided by TSP database)
Refers to articles published on the journal since 2020 that have received the most frequent citation to date (Statistics provided by TSP database)
Open Access
ARTICLE
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 masonry structures offer relevant
resistance… More >
Open Access
ARTICLE
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. Finally, the cantilever damage problem… More >
Open Access
ARTICLE
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 speed is obtained by fitting.… More >
Graphic Abstract
Open Access
ARTICLE
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 magnetic memory (MMM) method.… More >
Open Access
ARTICLE
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 whale optimization (BWO) algorithm is… More >
Graphic Abstract
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