Structural Monitoring Research Articles

Nearby Real-Time Earthquake Simulation on an Urban Scale Based on Structural Monitoring

The real-time input of ground motions can effectively assess earthquake disasters in building structures. In this study, we proposed a nearby real-time simulation method that can be applied to assess regional earthquake disasters using structural monitoring recordings based on an urban earthquake simulation system. Real-time accelerations recorded during the 5.1 magnitude Tangshan earthquake were used to update and modify the computing models. El Centro waves were calibrated to 400 cm/s2 for structural seismic resilience analyses. The results indicated that approximately 70% of the structural functions were lost during the rare earthquake. Regional numerical models of 216 buildings were constructed and timeously updated using a geographic information system and measured data. The inputting ground motions recorded in the 5.1 magnitude Tangshan earthquake and typical El Centro waves were selected to analyze the structural seismic response, in which the damage indexes were computed, and the damage predictions of the regional 216 buildings were also simulated in different levels of earthquakes by being combined with the simplified principles of structural damage estimation. Finally, the evaluation results were visualized in three dimensions using ParaView software. The simulated results of earthquake disasters at an urban scale will promote the prediction abilities of local earthquake administration agencies and have considerable potential to provide essential information for emergency responses and regional disaster mitigation.

Health Monitoring of Civil Engineering Structures Using Simulated Annealing Genetic Algorithm

Health Monitoring of Civil Engineering Structures Using Simulated Annealing Genetic Algorithm

Investigations of the Interface Design of Polyetheretherketone Filament Yarn Considering Plasma Torch Treatment

Taking advantage of its high-temperature resistance and elongation properties, conductive-coated polyetheretherketone (PEEK) filament yarn can be used as a textile-based electroconductive functional element, in particular as a strain sensor. This study describes the development of electrical conductivity on an inert PEEK filament surface by the deposition of metallic nickel (Ni) layers via an electroless galvanic plating process. To enhance the adhesion properties of the nickel layer, both PEEK multifilament and monofilament yarn surfaces were metalized by plasma torch pretreatment, followed by nickel plating. Electrical characterizations indicate the potential of nickel-coated PEEK for structural monitoring in textile-reinforced composites. In addition, surface energy measurements before and after plasma torch pretreatment, surface morphology, nickel layer thickness, chemical structure changes, and mechanical properties were analyzed and compared with untreated PEEK. The thickness of the Ni layer was measured and showed an average thickness of 1.25 µm for the multifilament yarn and 3.36 µm for the monofilament yarn. FTIR analysis confirmed the presence of new functional groups on the PEEK surface after plasma torch pretreatment, indicating a successful modification of the surface chemistry. Mechanical testing showed an increase in tensile strength after plasma torch pretreatment but a decrease after nickel plating. In conclusion, this study successfully developed conductive PEEK yarns through plasma torch pretreatment and nickel plating.

Structural monitoring of elevator guide rail bracket under normal running condition

Structural monitoring of elevator guide rail bracket under normal running condition

An Integrated Approach for Structural Crack Monitoring: Combining Plastic Tell Tales and Image Analysis for Enhanced Structural Health Evaluation

Structural Health Monitoring (SHM) calls for the development of the ideas of safety and main ability of civil structures. The most critical of the aspects explored in the paper is the safety monitoring of the structural configuration of any civil engineering work. Supervision of any sign of an odd shift from the usual state of structure enables one to prevent and or counter severe loss. It also depends on other environmental parameters such as load, nature of a seasonal parameter and the type of soil. Within this research project the Al-Beruni Academic Block of Sarhad University of Science and Information Technology is taken to monitor the cracks using Glass Tell-Tales, to collect data on cracks of buildings and during seismic activity, observe, study the cracks that are present.

Enhanced digital twin framework for real-time prediction of fatigue damage on semi-submersible platforms under long-term multi-sea conditions

As the development of offshore oil and gas resources progresses into deeper waters, the impact on marine structures becomes increasingly severe, resulting in significant structural damage. This highlights the importance of health monitoring for marine structures. The emergence of digital twin technology in ocean engineering has greatly advanced the development of marine structure monitoring technologies, improving the intelligence and real-time capabilities of structural health monitoring. This paper proposes an innovative data-driven digital twin framework, applied to the real-time fatigue damage prediction of the semi-submersible platform under long-term multi-sea conditions. Notably, this study introduces a novel stress twinning method along with a high-precision post-processing module that combines field monitoring data with high-fidelity simulation model results. This integration establishes a bidirectional connection between the physical structure and its digital counterpart, enabling real-time mapping of structural hotspot stresses and more accurate fatigue damage predictions. The proposed framework was validated on the semi-submersible platform in the South China Sea, and the results proved its practicality and transformative potential in offshore structure management.

Structural Health Monitoring and Failure Analysis of Large-Scale Hydro-Steel Structures, Based on Multi-Sensor Information Fusion

Large-scale hydro-steel structures (LS-HSSs) are vital to hydraulic engineering, supporting critical functions such as water resource management, flood control, power generation, and navigation. However, due to prolonged exposure to severe environmental conditions and complex operational loads, these structures progressively degrade, posing increased risks over time. The absence of effective structural health monitoring (SHM) systems exacerbates these risks, as undetected damage and wear can compromise safety. This paper presents an advanced SHM framework designed to enhance the real-time monitoring and safety evaluation of LS-HSSs. The framework integrates the finite element method (FEM), multi-sensor data fusion, and Internet of Things (IoT) technologies into a closed-loop system for real-time perception, analysis, decision-making, and optimization. The system was deployed and validated at the Luhun Reservoir spillway, where it demonstrated stable and reliable performance for real-time anomaly detection and decision-making. Monitoring results over time were consistent, with stress values remaining below allowable thresholds and meeting safety standards. Specifically, stress monitoring during radial gate operations (with a current water level of 1.4 m) indicated that the dynamic stress values induced by flow vibrations at various points increased by approximately 2 MPa, with no significant impact loads. Moreover, the vibration amplitude during gate operation was below 0.03 mm, confirming the absence of critical structural damage and deformation. These results underscore the SHM system’s capacity to enhance operational safety and maintenance efficiency, highlighting its potential for broader application across water conservancy infrastructure.

Combining long short-term memory and shape superposition for estimating moving load-induced global responses of bridges

Recently, a method of estimating the global responses of bridges was introduced, by combining a deep neural network (DNN) and shape superposition method (SSM). This DNN-SSM model demonstrated superior performance to the traditional estimation method based on the least-squares method. However, the application and validation of this approach have been restricted to static problems. For bridges, the dynamic effect induced by the moving load requires consideration. Therefore, this study proposes a long short-term memory (LSTM)-SSM model to improve the estimation performance for dynamic global responses. The LSTM-SSM model consists of LSTM layers, followed by fully connected DNN layers and a post-process part based on SSM. Limited displacement, slope, and strain data were used as the inputs, and the global displacement, slope, and strain were estimated. To validate the effectiveness and improvement of the LSTM-SSM model, a numerical model of a cable-stayed bridge was used to compare the proposed model with the previously developed DNN-SSM model. The validation results indicated that the LSTM-SSM model can provide more accurate estimates of the dynamic global response than DNN-SSM models. Finally, a technique for structural safety monitoring based on the proposed method was introduced.

TRAINERS CONTINUOUS PROFESSIONAL DEVELOPMENT AND STUDENTS LEARNING OUTCOMES IN TVET INSTITUTIONS

The study sought to establish the influence of Continuous Professional Development (CPD) programs on students learning outcomes in Technical and Vocational Education and Training (TVET) institutions in Ruhango District, Rwanda. The study aimed to determine the influence of CPD programs on course completion rates, acquisition of practical skills and competencies and students participation/engagement among TVET students. Using cross-sectional research design with mixed methods, data were collected from a sample of 427 respondents, including instructors, headteachers, and Sector Education Inspectors (SEIs). Simple random sampling was used for schools and instructors, while headteachers and SEIs were selected through purposive sampling. Quantitative data were analyzed using frequency percentages and correlation analysis, while qualitative data were processed through thematic analysis.The findings revealed that CPD programs significantly enhance student learning outcomes in TVET institutions. A majority of respondents (80%) participated in CPD initiatives, with 65% reporting that CPD had a strong influence on improving course completion rates, and 85% highlighting its positive impact on the acquisition of practical skills and competencies. Additionally, 60% of respondents recognized a substantial influence of CPD on student engagement. Correlation analysis confirmed strong positive relationships between CPD and course completion (r = 0.72), practical skills acquisition (r = 0.85), and student engagement (r = 0.72). The study concluded that CPD programs play a critical role in improving educational outcomes in TVET institutions. Recommendations include tailoring CPD programs to specific technical fields, enhancing hands-on workshops, providing more frequent CPD opportunities, and implementing structured monitoring and evaluation mechanisms. Future research should explore the influence of CPD on specific technical skills and examine its long-term impact on both instructors and students.

Correlations between coagulation abnormalities and inflammatory markers in trauma-induced coagulopathy

IntroductionIn multiple trauma patients, the occurrence of trauma-induced coagulopathy (TIC) is closely associated with tissue damage and coagulation function abnormalities in the pathophysiological process.MethodsThis study established a multiple trauma and shock model in Sprague-Dawley (SD) rats and comprehensively utilized histological staining and radiographic imaging techniques to observe injuries in the intestine, liver, skeletal muscles, and bones. Monitoring activated partial thromboplastin time (APTT), platelet (PLT) count, respiratory rate, blood pressure, and other physiological indicators revealed time-dependent alterations in coagulation function and physiological indicators. Enzyme-linked immunosorbent assay (ELISA) measurements of inflammatory factors Tumor Necrosis Factor-alpha (TNF-α), Interleukin-6 (IL-6) and vascular endothelial injury marker (Syndecan-1) were also conducted.ResultsExperimental results demonstrated significant changes in tissue structure after multiple traumas, although widespread necrosis or hemorrhagic lesions were not observed. There were time-dependent alterations in coagulation function and physiological indicators. ELISA measurements showed a strong positive correlation between the significant decrease in PLT count and the increase in TNF-α and IL-6 concentrations.DiscussionThe study provides crucial information for the early diagnosis and treatment of TIC. The findings suggest that structured monitoring of coagulation and inflammatory indicators can help in understanding the pathophysiological changes and aid in the management of TIC in multiple trauma patients.

Ductility and cracking behavior of UHPC-RC composite beam under bending test based on acoustic emission parameters

Ultrahigh-performance concrete (UHPC) possesses improved mechanical properties due to its high strength, durability, and toughness. Currently, there is a single method for detecting the cracking behavior and evaluating the ductility of the UHPC-reinforced concrete (RC) composite beam. As an important part of structural non-destructive monitoring techniques, the acoustic emission (AE) technique can be used to analyze the damage pattern of the structure by the variation characteristics of its individual parameters. On this basis, four-point bending tests were carried out on the RC beam and UHPC-RC composite beam, and the AE technique was used for ductility analysis and cracking behavior monitoring. The results showed that the UHPC material enhanced the flexural capacity of the UHPC-RC composite beam. The AE ring counts and energy parameters could be used to analyze the cracking process of the UHPC-RC composite beam during flexural damage. The proposed new ductility indices, AE ring counts ductility index (ARD) and AE energy ductility index (AED), differed from the displacement ductility indices within the range of 2 %, providing a new method for calculating ductility indices of concrete structures. During the failure stage, the UHPC-RC composite beam produced more tensile cracks at the early stage of cracking and the stage of damage compared to the RC beam. In addition, the b value of the RC beam had a large range of fluctuations during the failure stage, indicating that the RC beam produced more micro-cracks and macro-cracks and more damage compared to the UHPC-RC composite beam.

Machine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy)

The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect decisions can also result in unnecessary or misdirected actions. For example, an inadequate assessment of the structural health can lead to the modernization and replacement of some components that are still sound. Structural Health Monitoring (SHM) involves the use of time series derived from periodic measurements of the structure’s behavior, considered in its operational and load environment. The goal is to determine its response to various solicitations and, in particular, to highlight any critical issue in the structure’s behavior that may affect its reliability and safety due to anomalies and deterioration. This paper proposes an SHM method applied to the Valgadena bridge, one of the tallest viaducts in Italy and Europe (maximum height 160 m), located on the Altopiano dei Sette Comuni in the Province of Vicenza. Despite the fact that the viaduct itself had already been monitored during its construction using classical geometric leveling techniques, the methodology proposed here is based instead on the use of affordable dual-frequency GNSS (Global Navigation Satellite System) receivers to determine static and dynamic components of the bridge movements. Specifically, an effective combination of time series analysis methods and machine learning techniques is proposed in order to determine the vibration modes of the monitored viaduct. Monitoring is performed in regular operation conditions of the bridge (operational modal analysis (OMA)), and the use of certain machine learning methods aims at supporting the development of an effective automatic OMA procedure. To be more specific, the random decrements technique is used in order to make the vibration characteristics of the collected signals more apparent. Time-domain-based subspace identification is applied in order to determine a proper model of the collected measurements. Then, clustering methods, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and GMMs (Gaussian Mixture Models), are used in order to reliably estimate the system poles, and hence the corresponding vibration characteristics. The performance of the considered methods is compared on the Valgadena bridge case study, showing that the use of GMM clustering reduces, with respect to DBSCAN, the impact of the choice of certain parameter values in the considered case.

Investigation of a piezoelectric ultrasonic surface wave transducer that minimizes structural scattering

Abstract The Structural of ultrasonic surface wave transducers affects structural scattering. A numerical model of an ultrasonic surface wave transducer was established. Numerical simulations and dynamic acoustic analysis were performed to systematically assess, the effects of the transducer parameters, such as the front and back angles, matching layer, and front wedge shape on structural scattering. The results show that the acoustic amplitude of the transducer structure scattered waves is basically stable when the back angle of the wedge is 64° -68 °and the front angle is 65°-85°. The amplitude obtained from a transducer with orthogonal slots and a front uniform matching layer is 61% lower for the primary structure scattering, 56% lower for the secondary structure scattering.
When equipped with the upper matching layer, the amplitude of the secondary structural scattering echo is reduced by 78.2%. The experimental results show that the transducer with orthogonal slots on the wedge and a matching layer at the front and upper edges has a signal-to-noise ratio of 19.6dB when detecting the echo signal of a ϕ8 through-hole in a welded steel plate, indicating good detection capability. The experiment validates the numerical simulation results. The proposed transducer can be used for structural monitoring of welded structures and detecting flaws.

Bayesian model averaging and Bayesian inference-based probabilistic inversion method for arch dam zonal material parameters

Inversion analysis based on structural monitoring data is an important part of evaluating the working behaviour of structures. In this paper, a probabilistic inversion method for the elastic modulus of concrete in arch dams based on Bayesian model averaging (BMA) and Bayesian inference is proposed. According to the measured displacement data of an arch dam project, the influence of the separation accuracy of water pressure component, the inversion method and the selection of displacement measuring points on the inversion results of the elastic modulus of the dam body is studied. Example analysis results show that the accuracy of the multi-measurement point displacement and hydraulic pressure component separation model based on BMA-HST (Hydrostatic-seasonal-time) is at least 18.97 % higher than that of the single measurement point, and the relative error of the elastic modulus value of the multi-measurement point zonal inversion based on the Polynomial chaos-Kriging (PCK) proxy model and the Bayesian inference is only 6 % at the maximum, which indicates that the inversion model described in this paper is able to realize the high-precision inversion analysis of the material parameters of the zonal analysis of concrete dams at a relatively low computational cost.

Towards seismic risk reduction of critical facilities combining earthquake early warning and structural monitoring: a demonstration study

Towards seismic risk reduction of critical facilities combining earthquake early warning and structural monitoring: a demonstration study

Assessment of impact load effect on self-sensing of cement composites incorporating hybrid silicon carbide-graphite under various environmental conditions

This study investigates the self-sensing abilities of hybrid silicon carbide-graphite cement composites (HSCGC) under various environmental conditions, focusing on dynamic impacts. Integrating silicon carbide (SiC) and graphite enhances the mechanical and electrical properties of these composites. Tests showed that composites with 30 % SiC and 2 % graphite had a 14.1 % increase in compressive strength and up to 85 % decrease in electrical resistivity. Water absorption reduced by 3.25 %, indicating better durability. Impact resistance ratios (IRR) varied with humidity and impact angles, with the highest IRR observed at 70 % humidity and a 30-degree angle. Self-sensing responses indicated that higher SiC and graphite content maintained stable conductivity, especially at 60 and 90-degree angles. Temperature changes affected resistivity, negligible at −10°C and increased at 45°C. The study concludes that HSCGCs are suitable for smart construction materials, capable of real-time environmental and structural monitoring, advancing predictive models and control systems.

An improved generalized flexibility matrix-based damage identification technique for derrick steel structures

An improved generalized flexibility matrix-based method using matrix partitioning technique for structure damage identification is proposed. As an effective damage identification method, the generalized flexibility matrix requires fewer vibration frequencies and corresponding modal shapes compared with the traditional flexibility matrix method. By introducing a new partitioning technique of the matrices involved in the constructive process of the governing equation, the improved method simplifies the process of calculation with a dramatic reduction in computational cost while maintaining accuracy of identification results. In the case of incomplete measured modal data, an improved matrix partitioning technique based on the model expansion method is applied and the damaged indicators can be identified using partial measured modal data. A K-type derrick steel structure in service is considered as a numerical example for inspection the validity and feasibility of the proposed method. Numerical results show better accuracy and efficiency of this method for various damage scenarios, and can be used for damage diagnosis or health monitoring of other large structures.

Inductive Frequency-Coded Sensor for Non-Destructive Structural Strain Monitoring of Composite Materials

Structural composite materials have gained significant appeal because of their ability to be customized for specific mechanical qualities for various applications, including avionics, wind turbines, transportation, and medical equipment. Therefore, there is a growing demand for effective and non-invasive structural health monitoring (SHM) devices to supervise the integrity of materials. This work introduces a novel sensor design, consisting of three spiral resonators optimized to operate at distinct frequencies and excited by a feeding strip line, capable of performing non-destructive structural strain monitoring via frequency coding. The initial discussion focuses on the analytical modeling of the sensor, which is based on a circuital approach. A numerical test case is developed to operate across the frequency range of 100 to 400 MHz, selected to achieve a balance between penetration depth and the sensitivity of the system. The encouraging findings from electromagnetic full-wave simulations have been confirmed by experimental measurements conducted on printed circuit board (PCB) prototypes embedded in a fiberglass-based composite sample. The sensor shows exceptional sensitivity and cost-effectiveness, and may be easily integrated into composite layers due to its minimal cabling requirements and extremely small profile. The particular frequency-coded configuration enables the suggested sensor to accurately detect and distinguish various structural deformations based on their severity and location.

Adaptive probe position correction for automated complex weld inspection based on structural signal monitoring

ABSTRACT To ensure comprehensive coverage of welds, multi-axis motions are necessary for the probe during the automatic detection of complex welds. Guaranteeing the effectiveness of ultrasonic data during this process is crucial for ensuring detection accuracy and reliability. Taking tubular Y-joints as an example, this paper presents an adaptive probe posture correction method based on structural signal monitoring: (1) The structural and acoustic model of tubular Y-joints is established to select and predict the target structural signal. (2) Feature extraction and signal tracking algorithms are developed to achieve real-time monitoring of signals. (3) A posture correction method is proposed based on signal characteristics: time-domain characteristics correspond to position adjustments, while amplitudes correspond to orientation corrections. Building upon this, a feedback mechanism for probe posture is established. The effectiveness of the developed method is verified through underwater automatic detection experiments of tubular Y-joints. Results demonstrate that, with the assistance of the probe posture correction method, the coverage of the effective area increases to more than 95%, and embedded defects are fully detected through a single scan. In conclusion, the adaptive probe posture correction method based on structural signal monitoring presents a promising approach for the automatic ultrasonic examination of complex welds.

A Drone-Based Structure from Motion Survey, Topographic Data, and Terrestrial Laser Scanning Acquisitions for the Floodgate Gaps Deformation Monitoring of the Modulo Sperimentale Elettromeccanico System (Venice, Italy)

The structural deformation monitoring of civil infrastructures can be performed using different geomatic techniques: topographic measurements with total stations and levels, TLS (terrestrial laser scanning) acquisitions, and drone-based SfM (structure from motion) photogrammetric surveys, among others, can be applied. In this work, these techniques are used for the floodgate gaps and the rubber joints deformation monitoring of the MOSE system (Modulo Sperimentale Elettromeccanico), the civil infrastructure that protects Venice and its lagoon (Italy) from high waters. Since the floodgates are submerged most of the time and cannot be directly measured and monitored using high-precision data, topographic surveys were performed in accessible underwater tunnels. In this way, after the calculation of the coordinates of some reference points, the coordinates of the floodgate corners were estimated knowing the geometric characteristics of the system. A specific activity required the acquisition of the TLS scans of the stairwells in the shoulder structures of the Treporti barrier because many of the reference points fixed on the structures were lost during the placement of elements on the seabed. They were replaced with new points whose coordinates in the project/as-built reference system were calculated by applying the Procrustean algorithm by means of homologous points. The procedure allowed the estimation of the transformation parameters with maximum residuals of less than 2.5 cm, a value in agreement with the approximation of the real concrete structures built. Using the obtained parameters, the coordinates of the new reference points were calculated in the project reference system. Once the 3D orientation of all caissons in the barrier was reconstructed, the widths of the floodgate gaps were estimated and compared with the designed values and over time. The obtained values were validated in the Treporti barrier using a drone-based SfM photogrammetric survey of the eight raised floodgates, starting from the east shoulder caisson. The comparison between floodgate gaps estimated from topographic and TLS surveys, and those obtained from measurements on the 3D photogrammetric model, provided a maximum difference of 1.6 cm.