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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 study is to review the existing fatigue and vibration-based structural health monitoring techniques and highlight the advantages of combining both approaches.

Fatigue monitoring requires a fatigue model of the material, the stresses at specific points of the structure, a cycle counting technique and a fatigue damage criterion. Firstly, this paper reviews existing structural health monitoring (SHM) techniques, addresses their principal classifications and presents the main characteristics of each technique, with a particular emphasis on modal-based methodologies. Automated modal analysis, damage detection and localisation techniques are also reviewed. Fatigue monitoring is an SHM technique which evaluate the structural fatigue damage in real time. Stress estimation techniques and damage accumulation models based on the S-N field and the Miner rule are also reviewed in this paper.

A vast amount of research has been carried out in the field of SHM. The literature about fatigue calculation, fatigue testing, fatigue modelling and remaining fatigue life is also extensive. However, the number of publications related to monitor the fatigue process is scarce. A methodology to perform real-time structural fatigue monitoring, in both time and frequency domains, is presented.

Fatigue monitoring can be combined (applied simultaneously) with other vibration-based SHM techniques, which might significantly increase the reliability of the monitoring techniques.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

This research paper adopts the fundamental tenets of advanced technologies in industry 4.0 to monitor the structural health of concrete beam members using cost-effective non-destructive technologies. In so doing, the work illustrates how a coalescence of low-cost digital technologies can seamlessly integrate to solve practical construction problems.

A mixed philosophies epistemological design is adopted to implement the empirical quantitative analysis of “real-time” data collected via sensor-based technologies streamed through a Raspberry Pi and uploaded onto a cloud-based system. Data was analysed using a hybrid approach that combined both vibration-characteristic-based method and linear variable differential transducers (LVDT).

The research utilises a novel digital research approach for accurately detecting and recording the localisation of structural cracks in concrete beams. This non-destructive low-cost approach was shown to perform with a high degree of accuracy and precision, as verified by the LVDT measurements. This research is testament to the fact that as technological advancements progress at an exponential rate, the cost of implementation continues to reduce to produce higher-accuracy “mass-market” solutions for industry practitioners.

Accurate structural health monitoring of concrete structures necessitates expensive equipment, complex signal processing and skilled operator. The concrete industry is in dire need of a simple but reliable technique that can reduce the testing time, cost and complexity of maintenance of structures. This was the first experiment of its kind that seeks to develop an unconventional approach to solve the maintenance problem associated with concrete structures. This study merges industry 4.0 digital technologies with a novel low-cost and automated hybrid analysis for real-time structural health monitoring of concrete beams by fusing several multidisciplinary approaches into one integral technological configuration.

The purpose of this paper is to describe the development of a contactless and batteryless loading sensor system that can measure the internal loading of an object structure through several covering materials for structural health monitoring.

The paper proposed an architecture by which two radio frequency identification (RFID) tags are used in the system. It has been difficult to realize sensing by RFID because of the low power supply. To solve the power supply problem, a method using functional distribution of RFID tags of two kinds of RFID for communication and power supply was proposed. One RFID tag is specialized as a power supply for communication of strain loading information through A/D conversion. Another is specialized to supply power for driving the strain gauges bridge circuit.

By using developed system, the measurement of the structural internal loading with 20.0 mm depth was possible through covering materials such as concrete, but also plaster board, flexible boards, silicate calcium board, blockboard, and polystyrene with a resolution performance from 10 × 10⁻⁶ to 40 × 10⁻⁶.

A sensor system was developed using passive RFID, which enables measurement of load‐deformation information inside a structural object. Moreover, the inexpensive wireless, batteryless devices used in this system require little maintenance, and applications for the user interface are also included in the developed system for uniform management of structural health monitoring. The developed system was evaluated in an actual situation using not only concrete but also other materials as covering materials on a structural object.

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.

The paper aims to investigate effectiveness of the strengthening method, the construction process monitoring, fielding-load tests before and after strengthening, and health monitoring after reinforcement were carried out. The results of concrete strain and deflection show that the flexural strength and stiffness of the strengthened beam are improved.

This paper describes prestressed steel strand as a way to strengthen a 25-year-old continuous rigid frame bridge. High strength, low relaxation steel strand with high tensile strain and good corrosion resistance were used in this reinforcement. The construction process for strengthening with prestressed steel strand and steel plate was described. Ultimate bearing capacity of the bridge after strengthening was discussed based on finite element model.

The cumulative upward deflection of the second span the third span was 39.7 mm, which is basically consistent with the theoretical value, and the measured value is smaller than the theoretical value. The deflection value of the second span during data acquisition was −20 mm–10 mm, which does not exceed the maximum deflection value of live load, and the deflection of the bridge is in a safe state during normal use. Thus, this strengthened way with prestressed steel wire rope is feasible and effective.

This paper describes prestressed steel strand as a way to strengthen a 25-year-old continuous rigid frame bridge. To investigate effectiveness of the strengthening method, the construction process monitoring, fielding-load tests before and after strengthening and health monitoring after reinforcement were carried out.

In this work, the paper aims to demonstrate the feasibility of plastic optical fiber (POF) based accelerometers for the structural health monitoring (SHM) of civil engineering structures based on measurements of their dynamic response, namely to estimate natural frequencies. These sensors use POFs, combining the advantages of the optical technology with the robustness of this particular kind of fiber. The POF sensor output is directly compared with the signal from an electrical sensor, demonstrating the potential use of such sensors in structural monitoring applications.

Within this work, the paper demonstrates the feasibility of using a low-cost acceleration system based on a POF accelerometer on the dynamic monitoring of a civil engineering structure, aiming its natural frequency evaluation, which is a primary parameter to be used in SHM methods and numerical models calibration.

A low-cost POF-based accelerometer was used in the characterization of a civil engineering structural component, located in a building at the University of Aveiro Campus, being used to estimate its natural frequency with a relative error of 0.36 percent, comparatively to the value estimated recurring to a calibrated electronic sensor.

Optical fiber sensors take advantage of the fibers properties, such as immunity to electromagnetic interference and electrical isolation. They are very attractive for use in hostile environments, like submerse environments or flammable atmospheres where electrical currents might pose a hazard. The advantages of POF itself should also be considered, like resistance to hash environments, robustness, flexibility, low-cost interrogation units and high numeric aperture (lower cost components). The paper demonstrates the feasibility of using a low-cost acceleration system based on a POF accelerometer on the dynamic monitoring of a civil engineering structure, aiming its natural frequency evaluation.

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 report the results of a recent project of complex tests on the survival of structural health monitoring (SHM) technology with piezo wafer active sensors (PWAS) and electromechanical impedance spectroscopy (EMIS) at simulating the concomitant action of harsh conditions of outer space: extreme temperatures, radiations, vacuum.

The tests were conducted on PWAS, consists in adhesive and aluminium discs as structural specimens, with PWAS bonded on them. The substantiating of PWAS-EMIS-based SHM technique consists the fact that real part of the PWAS electromechanical impedance spectrum follows with fidelity the resonance behaviour of the structure vibrating under the PWAS excitation. This EMIS signature is very sensitive to any structural changes and, on this basis, can be monitored the onset and progress of structural damages such as fatigue, cracks, corrosion, etc.

The conclusion of the tests is that the cumulative impact of severe conditions of temperature, radiation and vacuum has not generated decommissioning of sensors or adhesive, which would have meant the compromise of the methodology. A second important outcome is linked to the capability of this methodology to distinguish between the damages of mechanical origin and the false ones, caused by environmental conditions, which are, basically, harmless.

The question of transfer of PWAS-EMIS-based SHM technology to space vehicles and applications received, as a novelty, a first and encouraging response.