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The purpose of this article is to suggest that Fraby‐Perot optic sensor is a practical measurement gage to monitor the strain of great structures such as railway bridges.

A remote strain monitoring system based on F‐P optic fiber and virtual instrument is designed to monitor the strains of a railway bridge.

The application results show that the Fraby‐Perot optical fiber sensors can accurately measure strain and they are suitable for the long‐term and automatic monitoring. In addition, the system has several advantages over conventional structural instruments including fast response, ability of both static and dynamic monitoring, absolute measurement, immunity to interferences such as lightning strikes, electromagnetic noise and radio frequency, low attenuation of light signals in long fiber optic cables.

Health monitoring of structures is getting more and more recognition all over the world because it can minimize the cost of reparation and maintenance and ensure the safety of structures. A strain monitoring system based on F‐P optic fiber sensor was developed according to the health monitoring requirements of Wuhu Yangtze River Railway Bridge, which is the first cable‐stayed bridge with a maximum span of 312 m carrying both railway and highway traffic in China. It has run stably in the monitoring field more than two years and fulfilled the monitoring requirement very well. Now the system has been transplanted successfully to the Zhengzhou Yellow Railway Bridge for strain monitoring. So the work can be referenced by other similar health monitoring projects.

Long‐term, real‐time monitoring of strain using FP fiber optic sensors in railway bridge is an innovation. A remote strain data acquisition and real‐time processing are another character of the system. The work studied can be referenced by other structures monitoring, such as tunnel, concrete bridges, concrete and earth dams.

The purpose of this paper is to provide a contemporary look at the current state‐of‐the‐art in wireless sensor networks (WSNs) for structure health monitoring (SHM) applications and discuss the still‐open research issues in this field and, hence, to make the decision‐making process more effective and direct.

This paper presents a comprehensive review of WSNs for SHM. It also introduces research challenges, opportunities, existing and potential applications. Network architecture and the state‐of‐the‐art wireless sensor communication technologies and standards are explained. Hardware and software of the existing systems are also clarified.

Existing applications and systems are presented along with their advantages and disadvantages. A comparison landscape and open research issues are also presented.

The paper presents a comprehensive and recent review of WSN systems for SHM applications along with open research issues.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

The purpose of this paper is to present the problems of the electromechanical impedance (EMI), especially its applications for structural health monitoring of aircraft bolt-joints and innovative approach of EMI prediction at loosening of bolt-joints.

This experimental study includes the results of a full-scale test of the Mi-8 helicopter tail beam, particularly, its bolt-joints of a beam with other parts of the structure. One of the connecting frames of the tail beam was equipped with piezoelectric transducers (PZT) glued on the surface of the frame near the bolts. The bolts' loosening was investigated by using the EMI technology.

It was demonstrated that loosening of the bolt-joint produces a significant and statistically stable change of the EMI metric. Presumably, both the small shift of resonance frequencies and the EMI magnitude and resistance change are caused mainly by damping variation at the bolt-joint loosening. In this analytical study, the 2D model of a constrained PZT is proposed. In contrast with the existing model, the modal decomposition analysis is used as a universal mean to express the dynamic properties and dynamic responses of both the transducer and the host structure. This approach, together with the finite element modal analysis, allows simulation of any complex system “PZT-host structure”. The model can be easily transformed also to the 3D one. The bolt-joint of the Mi-8 helicopter with the EMI measurement system was simulated by using the developed 2D model. The simulation results satisfactorily correspond to the test.

The results of this research can be used for implementation in the structural health monitoring of bolt-joints and other aerospace structural components.

The new experimental results on aircraft real bolt-joints were obtained. Especially significant is the original 2D model of the electromechanical impedance, based on the modal decomposition method, which can significantly improve the accuracy and the realistic description of the dynamic interaction between PZT and structure, as well as the dynamic response to the appearance of structural damage.

Compression is critical loading condition for composite airframes. Compression behaviour of structures with or without damages is a weak point for composite fuselage panels. This is one of the reasons for need of continuous in-service health monitoring of composite structures. The purpose of this paper is to characterize the compression panel behaviour on the base of a developed and implemented structural health monitoring (SHM) system.

The SHM system based on fibre optic Bragg grating (FOBG) sensors and standard resistance strain gauges (SGs) was placed onto/into (embedded or bonded) three stiffened carbon fibre reinforced polymer (CFRP) fuselage panels. The FOBG sensor system was used to monitor the structural integrity of the reference, impacted, and fatigued panels under compression loading. Both barely visible impact damage and visible impact damage were created to evaluate their influence on the panel behaviour. The functionality of the SHM system was verified through mechanical testing.

Experimental data showed the presence of impact damages significantly changes the buckling modes development and deformation behaviour of the panels. Some differences between the optical and SG sensors during buckling were observed. The buckling waves and failure development were very well indicated during loading by all sensors located on the panel surface but not by the embedded sensors. Good agreement between the data from the SGs and FOBG sensors was achieved for all sensors placed on the stringers, which did not buckle. The good reliability of FOBG sensors during the fatigue and static testing up to panel failure was verified.

The paper gives information about different buckling behaviour of CFRP fuselage stiffened panels in compression. The paper gives detailed information about measured signals from different sensors based on their location on/in the panel structure for realistic loading scenario of composite aerostructures. The paper gives an integrated overview of sensors placement considering possibilities to predicate structure behaviour.

The purpose of this paper is to discuss the characteristics of several stochastic simulation methods applied in computation issue of structure health monitoring (SHM).

On the basis of the previous studies, this research focusses on four promising methods: transitional Markov chain Monte Carlo (TMCMC), slice sampling, slice-Metropolis-Hasting (M-H), and TMCMC-slice algorithm. The slice-M-H is the improved slice sampling algorithm, and the TMCMC-slice is the improved TMCMC algorithm. The performances of the parameters samples generated by these four algorithms are evaluated using two examples: one is the numerical example of a cantilever plate; another is the plate experiment simulating one part of the mechanical structure.

Both the numerical example and experiment show that, identification accuracy of slice-M-H is higher than that of slice sampling; and the identification accuracy of TMCMC-slice is higher than that of TMCMC. In general, the identification accuracy of the methods based on slice (slice sampling and slice-M-H) is higher than that of the methods based on TMCMC (TMCMC and TMCMC-slice).

The stochastic simulation methods evaluated in this paper are mainly two categories of representative methods: one introduces the intermediate probability density functions, and another one is the auxiliary variable approach. This paper provides important references about the stochastic simulation methods to solve the ill-conditioned computation issue, which is commonly encountered in SHM.

Structural health monitoring (SHM) has become an attractive subject in aerospace engineering field considering the opportunity to avoid catastrophic failures by detecting damage in advance and to reduce maintenance costs. Fibre Bragg Grating (FBG) sensors are denoted as one of the most promising sensors for SHM applications as they are lightweight, immune to electromagnetic effects and able to be embedded between the layers of composite structures. The purpose of this paper is to research on and demonstrate the feasibility of FBG sensors for SHM of composite structures.

Applications on thin composite beams intended for SHM studies are presented. The sensor system, which includes FBG sensors and related interrogator system, and manufacturing of the beams with embedded sensors, are detailed. Static tension and torsion tests are conducted to verify the effectiveness of the system. Strain analysis results obtained from the tests are compared with the ones obtained from the finite element analyses conducted using ABAQUS® software. In addition, the comparison between the data obtained from the FBG sensors and from the strain gauges is made by also considering the noise content. Finally, fatigue test under torsion load is conducted to observe the durability of FBG sensors.

The results demonstrated that FBG sensors are feasible for SHM of composite structures as the strain data are accurate and less noisy compared to that obtained from the strain gauges. Furthermore, the convenience of obtaining reliable data between the layers of a composite structure using embedded FBG sensors is observed.

Observing the advantages of the FBG sensors for strain measurement will promote using FBG sensors for damage detection related to the SHM applications.

This paper presents applications of FBG sensors on thin composite beams, which reveal the suitability of FBG sensors for SHM of lightweight composite structures.

This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model on which hammer blows are applied for excitation. The vibration response signals of the bridge structure are collected using sensors placed at different nodes. The different damaged states such as no damage, single damage, double damage and triple damage are introduced by cutting members of the bridge. The masked noise with recorded vibration responses generates challenge to properly analyze the health of bridge structure.

The analytical modal properties are obtained from finite element model (FEM) developed using SAP2000 software. The response signals are analyzed in frequency domain by power spectrum and in time-frequency domain using spectrogram and Stockwell transform. Various low pass signal-filtering techniques such as variational filter, lowpass sparse banded (AB) filter and Savitzky–Golay (SG) differentiator filter are also applied to refine vibration signals. The proposed methodology further comprises application of Hilbert transform in combination with MUSIC and ESPRIT techniques.

The outcomes of SG filter provided the denoised signals using appropriate polynomial degree with proper selected window length. However, certain unwanted frequency peaks still appeared in the outcomes of SG filter. The SG-filtered signals are further analyzed using fused methodology of Hilbert transform-ESPRIT, which shows high accuracy in identifying modal frequencies at different states of the steel truss bridge.

The sequence of proposed methodology for denoising vibration response signals using SG filter with Hilbert transform-ESPRIT is a novel approach. The outcomes of proposed methodology are much refined and take less computational time.

The purpose of this paper is to discuss strategies for benchmarking patient safety using Lombardy region administrative archives. Patient safety indicators and statistical methods are presented that allow risk adjustment. The analysis benchmarks regional health structures, focusing on two patient safety indicators: failure to rescue; and death in low mortality diagnostic related group.

Data were drawn from a research project promoted by the Italian Agency of Regional Health Services in 2002 to furnish statistical evidence regarding adverse events based on Agency for Healthcare Research and Quality indicators and methods. Hierarchical models for an equitable benchmark analyses are proposed.

Empirical analysis shows that hierarchical approaches, based on comparing health structures within homogenous specialties, disaggregates and moderates failure to rescue variabilities existing between hospitals, especially in oncology, intensive care and general medicine.

The paper proposes using hierarchical models for properly benchmarking health structures, resolving logistic regression drawbacks and limitations.

The paper strengthens the theory that accurate coding supported by software and administrative databases could provide a valuable and economical source for patient safety research.

The paper analyses and suggests strategies for consistent benchmark analyses based on patient safety outcomes, applicable to several situations and different health structure typologies.