Structural Monitoring Research Articles

Multicolor Mechanoluminescence From Lu3Al2Ga3O12: Tb, Eu for Stress and Temperature Visual Sensing

AbstractMechanoluminescence (ML) refers to the luminescence phenomenon that occurs when a material is under external mechanical stimuli. However, relying solely on stress information to obtain ML signals is prone to test errors in complex test conditions. In this work, a Tb3+/Eu3+‐doped Lu3Al2Ga3O12 multicolor ML material is reported, in which the ML color can be adjusted from white to red (CIE color coordinates: from (x = 0.313, y = 0.3293) to (x = 0.6183, y = 0.379)) by changing the Eu3+ concentration. In addition, LAGO: 0.25% Tb3+ and LAGO: 1.5% Eu3+ are physically mixed at different mass ratios, and the varied stimuli‐responsed emission characteristics of Tb3+ and Eu3+ ions are used to develop a stress and temperature dual sensing device. Stress and temperature information can be further reflected simultaneously through the blue/red emission ratio IR (ITb/IEu). As the temperature increases, the color changes from white to red (CIE color coordinates: from (x = 0.3259, y = 0.306) to (x = 0.4707, y = 0.3625)), and the relative temperature sensitivity (Sr) is as high as 1.209% K−1 at 298 K. This sensing device provides a new idea for potential structural safety monitoring, multi‐modal anti‐counterfeiting technology, etc.

Innovative device for crack monitoring and deflection analysis in structures: Improved accuracy, safety and efficiency

It is of the utmost importance to ensure the structural integrity of buildings in order to guarantee the safety of their occupants. Conventional techniques for monitoring cracks and conducting flexural analysis during loading and unloading tests frequently prove inadequate in terms of accuracy and safety. This study introduces ES1293890 U, an innovative dual-purpose device designed for the precise remote monitoring of cracks and comprehensive flexural analysis. The device is integrated seamlessly into structural health monitoring (SHM) and building information modelling (BIM) systems, thereby providing a holistic approach to structural assessment.The ES1293890 U device offers superior accuracy, with a resolution of 0.01 mm and a maximum deviation of 0.007 mm, which makes it a more effective alternative to existing commercial solutions. The utilisation of recycled and biodegradable materials has the effect of reducing production costs, thereby facilitating the undertaking of more frequent and economically viable testing and monitoring. This enhanced affordability is of paramount importance for the maintenance of building safety and structural integrity through regular assessments.A noteworthy attribute of the ES1293890 U is its capacity to conduct remote measurements, obviating the necessity for professionals to enter potentially hazardous test areas. This not only ensures the safety of personnel but also enables continuous and uninterrupted monitoring, with real-time data capture facilitating timely and informed decision-making.In comparison to traditional gauges, the ES1293890 U exhibits enhanced performance in the monitoring of slab deflections, attributable to its elevated accuracy and comprehensive data acquisition capabilities. This device represents a substantial advancement in structural assessment technology, providing a reliable and efficient tool for improving building safety and integrity. The ES1293890 U's innovative approach and cost-effective design have the potential to revolutionise structural monitoring practices, making it an invaluable asset to both the scientific community and the construction industry.

Analysis of the Structure of 14 Therapeutic Antibodies Using Circular Dichroism Spectroscopy.

Understanding the impact of the manufacturing environment on therapeutic monoclonal antibody (mAb) structures requires new process analytical technology. Here, we describe the creation of a new reference set for the circular dichroism (CD) spectra of mAbs. Data sets of the highest quality were collected by synchrotron radiation CD for 14 different mAbs in both native and acid-stressed states. Deconvolution of far-UV spectra for the mAb cohort identified two current reference sets (SP175 and SMP180) as assigning accurate secondary structures, irrespective of the analysis program employed. Scrutiny of spectra revealed significant variation in the far-UV and especially near-UV CD of the 14 mAbs. Two spectral features were found to be sensitive to changes in solution pH, i.e., the far-UV positive peak at 201-202 nm and the near-UV negative exciton couplet around 230-240 nm. The latter feature offers attractive possibilities for in-line CD-based monitoring of the mAb structure during manufacture.

Dynamic Monitoring of Steel Beam Stress Based on PMN-PT Sensor

Steel beams are widely used load-bearing components in bridge construction. They are prone to internal stress concentration under low-frequency vibrations caused by natural disasters and adverse loads, leading to microcracks and fractures, thereby accelerating the instability of steel components. Therefore, dynamic stress monitoring of steel beams under low-frequency vibrations is crucial to ensure structural safety. This study proposed an external stress sensor based on PMN-PT material. The sensor has the advantages of high sensitivity, comprehensive frequency response, and fast response speed. To verify the accuracy and feasibility of the sensor in actual engineering, the LETRY universal testing machine and drop hammer impact system were used to carry out stress monitoring tests and finite element simulations on scaled I-shaped steel beams with PMN-PT sensors attached. The results show that: (1) The PMN-PT sensor has exceptionally high sensitivity, maintained at 1.716~1.726 V/MPa in the frequency range of 0~1000 Hz. The sensor performance is much higher than that of PVDF sensors with the same adhesive layer thickness. (2) Under low-frequency random vibration, the sensor’s time domain and frequency domain output voltages are always consistent with the waveform of the applied load, which can reflect the changes in the structural stress state in real time. (3) Under the impact of a drop hammer, the sensor signal response delay is only 0.001 s, and the sensitivity linear fitting degree is above 0.9. (4) The simulation and experimental results are highly consistent, confirming the superior performance of the PMN-PT sensor, which can be effectively used for stress monitoring of steel structures in low-frequency vibration environments.

Refined analysis of a supporting column assembly in a traditional Tibetan timber building and its limiting tilt angle before collapse

Traditional Tibetan architecture is an important part of the Chinese architecture. Many of the Tibetan structures are suffering from different types of damages after hundreds of years of service. The structural arrangement, types of damage and failure modes of these timber structures have been investigated on site. Tilting of structural members is found to be the most common type of damage. This paper studies the limiting inclination angle of a single-column supporting assembly of these structures before its collapse for proactive maintenance and restoration. A method to estimate the limiting tilt angle of the column is proposed based on the relationship between the lateral force, F, at the column head and the tilt angle, θ, of the column. Simplified analytical models of this column assembly in a traditional Tibetan timber building under in-plane and out-of-plane loadings respectively are proposed. The behavior of the constituting column foot joint, column-Queti joint and Queti-beam joint are analyzed in detail to obtain the global deformation and M-θ relationship of the column assembly under different loads. The F-θ relationship of the column assembly from the intact state to collapse is then obtained. A verified finite element model of the column assembly is adopted as reference to validate the performances of the proposed models throughout the loading process. Comparison of the F-θ curves obtained from the analytical models and the simulated results demonstrates the validity and accuracy of the proposed models. They may be adopted for a quick analysis of practical structures to estimate their limit states for health monitoring, maintenance and strengthening of structures.

Interferometric Radars for Bridge Monitoring: Comparison among X-Bands, Ku-Bands, and W-Bands

Interferometric radars are widely used sensors for structural health monitoring. They are able to perform dynamic measurements of displacement with sub-millimeter precision. Today, the Ku-band is the most common, due to the spread of commercial systems operating in this band. At the same time, the W-band sensors are gaining ever more interest. Other popular systems work in the X-band. Since the characteristics of the measurements dramatically depend on the operative frequency, it is essential to highlight their differences. For instance, higher frequency allows for high displacement resolution, but it is more subject to phase wrapping and decorrelation effects. In this paper, a direct comparison between radars operating in X, Ku, and W-band for bridge monitoring is carried out. The radars provide frequency-modulated continuous-wave signals. Experimental campaigns were performed both in controlled and realistic scenarios (a stayed bridge). The results of the experiments demonstrate that all the three sensors are suitable for performing dynamic structure monitoring despite their differences. It is worth noting that this comparative analysis has highlighted the role of amplitude variation in phase/displacement measurement. Regarding this point, the three different bands exhibit significant differences.

Long-term monitoring of water quality and phytoplankton community structure of Lake Manzala, Mediterranean Coast of Egypt

Coastal wetlands play a crucial role in supporting biodiversity, providing valuable ecosystem services, and contributing to the resilience of coastal ecosystems, making the preservation and restoration of these wetlands essential for sustainable development in coastal regions. This study focuses on Lake Manzala, a coastal wetland located on the Mediterranean Coast of Egypt, highlighting the significance of conserving and managing this unique environment. The objective of this research was to evaluate the water quality and phytoplankton structure of Lake Manzala and establish an updated long-term ecological database for the region, specifically evaluating the effectiveness of development plans that were carried out in 2017. Surface water samples were collected seasonally at eleven sites between 2010 to 2022. The findings revealed that prior to the development plans, the phytoplankton abundance in Lake Manzala exhibited high levels of eutrophication, characterized by increased abundance and species richness. The dominant phytoplankton classes in Lake Manzala were Bacillariophyceae, Chlorophyceae, and Cyanophyceae. Prior to development plans, they accounted for 46.5% and 45.0% respectively. Post-development, Bacillariophyceae increased to 62.8%, while Chlorophyceae decreased to 25.1%. Dinophyceae increased from 1.3% to 9.04%, while Cyanophyceae decreased from 6.1% to 1.6%. Based on the Trophic State Index for chlorophyll a, Lake Manzala underwent a shift from predominantly hypertrophic to eutrophic conditions. The study explored the relationship between biological factors and environmental conditions using principal component analysis, cluster analysis, and the modified water quality index (WQI). The results indicated positive signs of improvement in Lake Manzala during the post-development phase, as it transitioned from a poor to a moderate state. This research emphasizes the need for integrated land and water management approaches. By informing policy direction and development, this research underscores the importance of preserving and restoring ecosystems for the long-term well-being of both local communities and the global environment.

Research on Outlier Detection Methods for Dam Monitoring Data Based on Post-Data Classification

Safety monitoring of hydraulic structures is a critical task in the field of hydraulic engineering construction. This study developed a method for preprocessing and classifying monitoring data for the identification of gross errors in hydraulic structures. By utilizing linear regression and wavelet analysis techniques, it effectively differentiated various waveform characteristics in data sets, such as Sinusoidal Wave Cyclical, Triangular Wave Cyclical, Seasonal Cyclical, and Weakly Cyclical growth types. In the experiments for gross error identification, the 3σ algorithm, K-medoids algorithm, and Isolation Forest algorithm were applied to test the data. The results showed that the K-medoids algorithm excelled in processing Sinusoidal Wave Cyclical Data Sets; the 3σ algorithm adapted better to Triangular Wave Cyclical Data Sets; the Isolation Forest algorithm performed well in handling data sets with significant anomalies or atypical fluctuations and excelled in scenarios with strong seasonality and large data fluctuations; and for complex Weakly Cyclical Growth Data Sets, all three algorithms were less effective, indicating the potential need for more advanced analysis methods or a combination of multiple techniques. Testing on actual engineering data further confirmed the importance of using specific gross error identification techniques for special data types after data set pre-classification, providing a more effective technical solution for the safety monitoring of hydraulic structures.

Vibration-based health monitoring and damage detection in beam-like structures with innovative approaches based on signal processing: A numerical and experimental study

Beams and columns serve as foundational components within structures, bridges, and industrial machinery. Detecting even minor defects in these crucial elements before they escalate into more significant damage holds paramount importance. Employing an innovative methodology grounded in the two-dimensional discrete wavelet transform alongside curvature analysis, this study scrutinizes the one-dimensional mode shape signals extended to two-dimensional. More precisely, it introduces the Wavelet Expansion Index (WEI) and the Normalized Wavelet Discontinuity Index (NWDI) to effectively pinpoint various damage locations. Numerical and laboratory investigations demonstrate that fractures and discontinuities in the WEI graph and peaks arising from irregularities in the NWDI graph are generated. Consequently, damage locations are identified, such that using NWDI, damaged areas can be detected with an error of less than 4%.

A Proof-of-Concept Study of Stability Monitoring of Implant Structure by Deep Learning of Local Vibrational Characteristics

This study develops a structural stability monitoring method for an implant structure (i.e., a single-tooth dental implant) through deep learning of local vibrational modes. Firstly, the local vibrations of the implant structure are identified from the conductance spectrum, achieved by driving the structure using a piezoelectric transducer within a pre-defined high-frequency band. Secondly, deep learning models based on a convolutional neural network (CNN) are designed to process the obtained conductance data of local vibrational modes. Thirdly, the CNN models are trained to autonomously extract optimal vibration features for structural stability assessment of the implant structure. We employ a validated predictive 3D numerical modeling approach to demonstrate the feasibility of the proposed approach. The proposed method achieved promising results for predicting material loss surrounding the implant, with the best CNN model demonstrating training and testing errors of 3.7% and 4.0%, respectively. The implementation of deep learning allows optimal feature extraction in a lower frequency band, facilitating the use of low-cost active sensing devices. This research introduces a novel approach for assessing the implant’s stability, offering promise for developing future radiation-free stability assessment tools.

Distributed acoustic sensing signal event recognition and localization based on improved YOLOv7

Abstract The distributed acoustic sensing (DAS) system based on phase-sensitive optical time domain reflection ( Φ-OTDR) technology is widely used in pipeline safety monitoring, perimeter security, structure monitoring, etc. Accurate localization and recognition of multi-scene events over long distances has always been a challenge. This paper proposes an improved YOLOv7 algorithm for multi-event real-time detection of DAS system. The algorithm employs space-to-depth Conv(SPD-Conv) to replace the strided convolutions and pooling operations in YOLOv7, reducing fine-grained information loss and learning of inefficient feature representations. In addition, the Convolutional Block Attention Module (CBAM) is introduced in YOLOv7 to improve the model performance. We collected spatial–temporal signal data for six types of pipeline safety events, and passed them into the improved YOLOv7 algorithm in the form of data matrixes for training and evaluation. Experiments have shown that the proposed method achieves an mAP@.5 (mean Average Precision) of 99.7% for the identification of six pipeline safety event types. Positioning loss reduced to 0.2%, and detection speed can reach 70 frames per second(FPS). Our scheme achieves significant improvements in localization and classification accuracy compared to Faster R-CNN, etc. The event recognition localization method proposed in this paper has the advantage of fast speed and high accuracy.

Modeling of Scattered Wavefield in Complex Structures Based on Physics-informed Neural Networks

Abstract The scattered wavefield of complex structures, bearing various information about the medium, serves as an effective basis for structural defect monitoring. The solution of the scattered wavefield has consistently drawn a considerable amount of attention. The commonly used approaches for solving scattered wavefields include analytical methods, numerical methods, and deep learning methods. However, it is well known that analytical methods are complex and computationally demanding, often accompanied by specific assumptions during the solving process. Numerical methods face a contradiction between computational complexity and accuracy. Deep learning methods are relatively dependent on data sets. In this work, we apply physics-informed neural networks (PINNs) to the modeling of scattered wavefield in a 2D plate. Wave equation loss and initial condition losses are represented by automatic differentiation technique, and then weighted together to form the total loss function, which constrains the iteration of the network. We demonstrate the performance of the proposed method in the modeling of scattered wavefield for both single and multiple damage models, and validate the effectiveness through a 3D printed sphere wavefield scanning experiments.

The Effects of Seismic Behavior on High Ground Stress Soft Rock Tunnel: A Review

The purpose of this review is to critically assess seismic activity's effects on soft rock tunnels under high ground stress scenarios. The paper seeks to identify key novelties and research gaps in the existing literature, offering new insights into analytical techniques, excavation methods, support systems, and monitoring technologies. A comprehensive review of recent studies was conducted, focusing on seismic behavior, analytical techniques, and mitigation strategies for soft rock tunnels. Case studies were selected based on their relevance to high ground stress conditions and their contribution to understanding seismic resilience. Significant findings include the identification of specific geological conditions that exacerbate seismic risks and the comparative effectiveness of various analytical techniques and support systems. Novel insights into the interaction of structural reinforcements and monitoring systems are also discussed. The review highlights new analytical techniques and advanced monitoring systems that improve predictive accuracy and early detection of seismic risks. It also proposes a refined approach to integrating mitigation strategies for enhanced tunnel resilience. Doi: 10.28991/CEJ-2024-010-09-020 Full Text: PDF

Development and engineering application of fiber bragg grating intelligent cable in large-span hyperbolic parabolic spatial cable network

In order to accurately control the prestress force of cables in long-span cable net structures, a new type of fiber Bragg grating (FBG) intelligent cable was developed. The FBG sensor was directly encapsulated inside the cable, and the sensing performance of the intelligent cable was verified by the cyclic tensile tests. The results show that the sensitivity coefficients of FBG monitoring are basically consistent, and the linearity deviation of the fiber Bragg grating is less than 4.67 %. Based on a real project Shunde Desheng Sports Center, a 114 m long-span saddle-shaped cable net structure model was established using finite element analysis, and the distribution and correlation of internal forces of cables in different tension stages were calculated and compared with the actual monitoring results of intelligent cables. The analysis results indicate that the force changes of intelligent cables is basically consistent with the value of finite element simulation. According to the data of long-term intelligent cable monitoring, the change of cable force caused by the temperature deformation of the steel ring beam is much greater than the influence of the temperature rise and fall of the cable itself on the cable force. Intelligent cables can provide a reference for cable force monitoring of long-span cable net structures.

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data. Many complex and dangerous impact scenarios are difficult to conduct real-world experiments on to collect sufficient samples. To capture all impact scenarios and fully leverage the advantages of AI-based detection technologies, advanced methods involve combining real-world structural monitoring data with corresponding numerical models to construct digital twins. These methods continuously refine the created numerical models with limited real-world data and provide diverse impact scenarios through numerical model simulations. However, there are inevitable differences between digital models and physical models that are challenging to correct through mechanical means. This discrepancy in data distribution between the two models significantly hinders the application of digital twin technology in impact/event identification tasks. To address this challenge, this study proposes a novel model based on autoencoders, named Transfer-AE. Transfer-AE encodes the common features of digital twins in the latent space to bridge the uncertainty gap at a macro scale between numerical models and physical models and synchronously fits the magnitude and location of the impact load in the decoder. This enables consistent detection results for the same impact event, whether the sample comes from the numerical model or the physical model. Transfer-AE includes two operating modes: Mode 1 has a fixed computational complexity with stable inference speed, but the training cost and difficulty increase with data distribution. Mode 2's computational complexity increases with data distribution, but it has a fixed training cost and speed. In both cases involving the geodesic dome structure simulating a deep space habitat and the IASC-ASCE benchmark structure, Transfer-AE demonstrated the best performance in impact localization and quantification tasks compared to mainstream domain-adaptive transfer models.

Study on Strain Field Reconstruction Method of Long-Span Hull Box Girder Based on iFEM

The box girder’s condition significantly impacts the safety and overall performance of the entire ship because it is the primary stress component of the hull construction. This work used experimental research on the long-span hull box girder based on IFEM (Inverse Finite Element Method) technology to ensure the structural safety of the hull box girder. Due to the limitations of conventional experiments in this technical field, such as their reliance on finite element data and lack of input from physical tests, numerous research methods combining the strain sensing data from physical tests with the strain data from virtual sensors were conducted. The strain fields of the top plate, side plate, and bottom plate were each reconstructed in turn, and the verifier measuring points in the physical model test were used to assess the accuracy of the reconstruction results. The findings demonstrate that the top plate, side plate, and bottom plate reconstructions had relative errors of 0.24–7.86%, 0.75–8.13%, and 3.31–2.52%, respectively. This enables the reconstruction of the strain field of the long-span hull box girder using physical test data and promotes the use of iFEM technology in the field of structural health monitoring of large marine structures.

Leveraging Multi-Temporal InSAR Technique for Long-Term Structural Behaviour Monitoring of High-Speed Railway Bridges

The effective monitoring of railway facilities is crucial for safety and operational efficiency. This study proposes an enhanced remote monitoring technique for railway facilities, specifically bridges, using satellite radar InSAR (Interferometric Synthetic Aperture Radar) technology. Previous studies faced limitations such as insufficient data points and challenges with topographical and structural variations. Our approach addresses these issues by analysing displacements from 30 images captured by the X-band SAR satellite, TerraSAR-X, over two years. We tested each InSAR parameter to develop an optimal set of parameters, applying the technique to a post-tensioned PSC (pre-stressed concrete) box bridge. Our findings revealed a recurring arch-shaped elevation along the bridge, attributed to temporal changes and long-term deformation. Further analysis showed a strong correlation between this deformation pattern and average surrounding temperature. This indicates that our technique can effectively identify micro-displacements due to temperature changes and structural deformation. Thus, the technique provides a theoretical foundation for improved SAR monitoring of large-scale social overhead capital (SOC) facilities, ensuring efficient maintenance and management.

Analysis of Fill Dam Using Finite Element Method and Comparison with Monitoring Results

Nowadays, a detailed safety policy is applied for dams. These policies cover structural safety, monitoring, inspection, safe operation, and emergency plans. For high-risk dams, all these policy elements need to be included in dam safety programs. Deficiencies in embankment dams, which suffer the most damage, can be detected by visual inspection and programmed monitoring of dams. In dams, horizontal and vertical deformation, leakage, pressure, stress, loads acting on structural elements, and environmental factors are generally measured. These behaviors can be numerically modeled to determine the dam behavior. Numerical analysis methods are important for monitoring the safety of the dam. Models created with software such as Plaxis provide information about dam behavior. Although numerical analysis is very important for dams, obtaining the material parameters used in the construction of the dam needed for modeling, recording the construction stages of the dam, not taking the water level change in the dam reservoir instantaneously, and not taking the measurement records of the dam measurement instruments correctly for different reasons constitute problems and difficulties for the analyses. Within the scope of this study, İkizdere Dam in Turkey was modeled with the Plaxis finite element program; the survey and piezometer measurement data taken from the dam were evaluated by comparing with the analysis results; the difficulties and problems encountered in the modeling and analysis phase were stated, and recommendations were made on dam safety and numerical analysis. Thus, in addition to other studies, it was emphasized that it is important for dam engineers to monitor the use of numerical analysis models throughout the entire process, not only in the planning phase but also from the planning phase to the life of the dam, and to keep records of all recording intervals that will be needed in digital analysis models.

Metamaterials based passive wireless sensor for concrete structures

—Increasing attention has been payed to wireless passive sensors because of the ever-increasing demand for long-term and accurate monitoring of chloride ion penetration and structure of concrete infrastructures. Extraordinary properties of metamaterials results in applications in sensors. In this study, a wireless passive SRR metamaterials based sensor system was designed and manufactured which was used to detect Cl− concentration and thickness of concrete. CST was used for simulation and observation of the performance of metamaterials sensors in X-band (8–12 GHz). High Q and sensitivity sensors were designed by continuously adjusting the dimensions of the SRRs. X-band microstrip antenna was designed by HFSS and the sensing performance of the proposed sensor system is simulated and experimental measured. The results demonstrate the feasibility of the wireless passive sensor system. The system was used to test Cl− and thickness of concrete structure and the experimental results indicate the theoretical significance and practicality.

In situ stereomicroscopy chemical and color etching

Abstract In the field of materials analysis, chemical etching continues to play a crucial role in sample preparation, yet its approach is often more empirical. While classical metallographic methods dominate, insufficient understanding of the process creates room for innovation. To improve this situation, the development of new techniques allowing for structure observation during sample preparation is necessary. This is a critical step towards mitigating the influence of stochastic factors, thereby increasing the reliability and predictability of the outcomes. For real-time structural analysis during sample preparation, professional equipment was recently introduced. This contribution aims to verify in situ monitoring of structure during chemical etching using common laboratory equipment and a stereomicroscope with a camera and tablet. The method may encounter limitations due to the magnification and resolution capabilities of the stereomicroscope. Manipulation with a pipette and the risk of lens contamination with etchant can also be challenging. Additionally, etching in deeply dark etchants and ensuring adequate stirring of the etchant can be difficult. Nevertheless, this method offers new possibilities and can be beneficial for certain applications. To achieve a more comprehensive understanding, experiments with various materials and etchants are important to better grasp the limitations and possibilities of this innovative technique.