Journal of Civil Structural Health Monitoring最新文献

Current predictive maintenance technologies are mostly data-driven, where they identify complex relationships using statistics and machine learning (ML) models for damage prediction. The main disadvantage of data-driven or ML models is their high dependency on training data, making them poor in extrapolating and predicting untrained events. Hence, engineers prefer a physics-based model in most cases due to its strong interpretability that aids in supporting critical engineering decisions. However, high-fidelity physics-based models are computationally exhaustive. To preserve the merits and alleviate the inadequacy of both data-driven and physics-based models, recent years have shown an increase in works on hybrid digital twin (DT) models which integrate both methods. This work presents the development of a medium-fidelity physics-based model of a piping structure and its implementation in a hybrid DT for damage identification. Two modelling approaches for the piping support bolted connections were investigated, i.e., bonded contact and spring-based model. The developed physics-based models were correlated with the modal testing data. Results showed that with suitable spring stiffness, the spring-based model has better dynamical representation than the overly stiff bonded contact model with an average natural frequencies deviation below 10% and an average Modal Assurance Criterion (MAC) value of at least 0.75 for both undamaged and damaged conditions. The correlated medium-fidelity spring-based model was used to simulate damage cases for ML training. Results showed that the trained model achieved an accuracy of 95% in identifying the damage at the physical piping structure, thus validating the proposed hybrid DT in damage identification.

The current bridge routine detection method in Taiwan relies on DER&U visual inspection, emphasizing ease and time efficiency. However, its accuracy is contingent on inspectors' experience and fails to assess internal pillar damage from external attacks. The prevalent direct approach in Taiwan Highway Administration, while obtaining dynamic bridge properties, involves mounting vibration sensors directly on the bridge, incurring significant time and cost. This research introduces an indirect approach, enhancing portability and cost-effectiveness by installing sensors on vehicles rather than bridges. To establish a bridge health standard, the study develops bridge models and conducts pushover analyses to speculate on the variation of the bridge vibration frequency ratio (({R}_{ec}) and ({R}_{sc})). This ratio serves as a crucial reference for determining bridge safety. To mitigate time and cost constraints, the research employs a hybrid symbiotic organisms search-least squares support vector machine (SOS-LSSVM) for ({R}_{ec}) and ({R}_{sc}). The results enable the determination of vibration frequency ratios for the bridge safety standard. The indirect approach proves valuable for the Taiwan Highway Administration, allowing bridge frequency measurement under normal circumstances and post-disaster, facilitating timely decisions on bridge openings and providing a reference for regular maintenance based on bridge health assessments.

The automatic damage detection in buildings using 3D laser scanning is a non-invasive approach to monitoring heritage buildings, especially in places like Liverpool, where the temperature can vary drastically during a year and these changes can damage the components of the buildings. In this paper, the St Luke’s Church mostly known as the Bombed-out Church, an important heritage building, was scanned using a 3D laser scanner. This paper proposed the R–C–C fusion classifier to detect the damage on the heritage building automatically. Utilizing the Roughness method (R) and the CANUPO classification (C) in small sections of the façade, it was possible to determine the shape and the location of the damages (cracks, anomalies, stone decay, stone peeling, etc.) on the surface of the walls, and then, the analysis was carried out to the whole building. Utilizing the R–C–C method was possible to locate and isolate the cracks and anomalies for future reference in monitoring this heritage building. This non-invasive technique for monitoring heritage building has demonstrated that it is possible to detect damages on the surface of buildings using a classifier which will dramatically reduce the computing time.

Excessive deformation of railway tracks caused by thermal loadings critically affects the efficiency and safety of railway transportation. Accurately quantifying the thermal variations in railway tracks is essential for mitigating heat-related risks. Nevertheless, the complex thermal regime influenced by multiple meteorological factors has posed challenges in understanding the nature of heat-related incidents in railway infrastructure. To investigate the thermal behaviors of railway tracks, this study implemented an IoT monitoring system to measure the temperature along a railway stretch from Changdong to Ssangmun station in Seoul, Korea. Furthermore, a railway temperature forecast model was developed based on Bayesian long short-term memory (BLSTM) trained by the monitoring data. Analyzing the 2-year monitoring results revealed the thermal patterns of the railway, characterized by long seasonal periods and trend stationary. The increasing trend of railway temperature during frequent high-temperature occurrences raised urgent concerns for the railway administration to adapt existing infrastructure to the impacts of climate change. The BLSTM model demonstrated comparable performance with the SARIMA model, a well-established statistical model, and physical models in forecasting the railway temperature, exhibiting a relatively low root mean squared error of 2.21 °C and a bias of − 0.04 °C. Moreover, a notable advantage of the presented BLSTM model is its capacity to provide probabilistic upper and lower bounds of railway temperature, making it suitable for supporting railway safety management. Importantly, using monitoring data as the exclusive input enabled the integration of the BLSTM model into the monitoring system, facilitating the development of a hybrid temperature control system for real-time railway safety management.

The paper presents an in-depth analysis of the ambient dynamic behavior of nine masonry buildings monitored by the Italian Seismic Observatory of Structures (OSS). Addressing a significant knowledge gap affecting this structural type, the study reveals how daily and seasonal fluctuations in environmental factors have a notable influence on its experimental modal parameters. A robust frequency-domain tracking algorithm is first developed to identify and follow the evolution of modal parameters over time, exploiting ambient vibration recordings acquired at sub-daily intervals on the structures. The procedure is systematically applied to the entire portfolio of case-study buildings and, in the first year of training, integrated with measurements of environmental parameters provided by nearby weather stations. The multivariate regression analysis indicates that temperature variation is the primary driver of the observed wandering of natural frequencies. The frequency–temperature relationship shows a positive correlation above zero degrees and, in several cases, a significant degree of nonlinearity already present in low-frequency global modes. Simple predictive models are proposed to address such nonlinear behavior, including freezing conditions and accounting for internal heating during winter. Leveraging these novel insights, the work develops strategies to improve the efficiency of data acquisition protocols and training periods, enabling the near-future extension of real-time condition assessment methodologies to the entire OSS network.

This paper presents a study of a 63-m-high (206 feet) RC building’s failure under blast loading and subsequent column removals. The analysis covers the entire process, starting with explosive charge detonation and ending in demolition. LS-DYNA software was used for blast wave propagation and structural interaction, while SAP2000 modeled successive column removal, both focused on columns experiencing the highest loads at the bottom, using nonlinear dynamic analysis (NDA). Three explicit methods—Load blast enhanced (LBE), Arbitrary lagrangian eulerian (ALE), and Coupling—were discussed for their suitability. The LBE method, though time-efficient, faces challenges in limiting affected surface or volume. ALE confines explosive energy to designated columns and the Coupling method emerges as the most appropriate, combining ALE for initial detonation and LBE for subsequent implosion. The research distinguishes itself by exploring a novel method for safely monitoring building demolition. By employing stationary cameras positioned outside the critical collapse area and utilizing Tracker software, it segments footage into frames, tracks point displacement in each frame, and compares the results with real values, providing a comprehensive analysis. Moreover, the study’s examination aligns with the actual demolition, offering insights by comparing simulation results with photographs of real damage, thereby validating the procedure. The findings show explicit analysis aligns closely with real data, while SAP2000 NDA exhibits relatively distant results, although being more time-efficient. The article also explores alternative demolition scenarios, sequentially removing three column groups from the same structure. To deepen the analysis, scenarios were created by varying the time intervals between column removals. Decreasing time intervals resulted in improved alignment between the outcomes of both programs. The global issue of buildings reaching the end of their service life and the 2023 seismic events in Turkiye have highlighted the urgent need to analyze numerical methods for the demolition of hundreds of thousands of structures with specific focus.

The mission of the Society of Civil Structural Health Monitoring (SCSHM, previously known as ISHMII) is to advance the understanding and application of structural monitoring methodologies for the management of civil infrastructure systems. To enable comparative and contrasting studies of various monitoring issues and technologies, the SCSHM Committee on Data-Enhanced Infrastructures Management (DEIMC) identified the need for benchmark problems in the areas of bridge and building structural monitoring. This article reports and briefly discusses the first benchmark study on in-service structural monitoring of bridges that was developed in collaboration with the University of Manitoba, and presents the structure details, study goals, data made available to the engineering community, and other relevant details. This paper has been submitted to the JCSHM as the outcome of the work of the DEIMC Committee of the SCSHM. However, since JCSHM does not publish at present papers without original experimental and or field monitoring components, data from this work cannot be used for publications in JCSHM.

Bridges are subject to deterioration over time and unexpected disasters, such as traffic-induced fire, explosion, etc. Considering the sensor equipment and maintenance cost, the lightweight monitoring system is highly demanded for in-service small and medium-span bridges or bridges with an emergent request, such as post-disaster monitoring. This paper introduced a real practice of the post-disaster emergency monitoring system designed for a continuous rigid frame bridge in a mountain area in China. Considering bridge structural mechanical features and budget limits, the dynamic deformation of the bridge girder and bridge pier inclination are two major monitored objectives to timely identify the bridge deformation and dynamic features. Moreover, to precisely identify structural performance, the temperature variation is an indispensable monitoring content. Hence, this system consists of only three types of sensors, including the photoelectric deflection meter, the inclinometer, and the temperature–humidity sensor. In detail, this 330 m continuous rigid frame bridge with five spans, 45 + 80 (times) 3 + 45 m, is entirely and efficiently monitored using 8 sensors. The entire system was installed within four days after the traffic-induced fire accident. The dynamic deflection is recorded to evaluate the structural load capacity and dynamic features considering the temperature variations. Three months’ measurements are interpreted and discussed in this paper, which can prove the non-contacted deflection meters are practical for long-term monitoring. But limits exist for the sensors’ stability, because of the considerable temperature difference in mountain areas, which will affect the meters’ supports. In summary, the efforts of this paper contribute to the research and practice gap of lightweight and emergency monitoring systems, especially for post-disaster requests. Based on a three-month data survey, we demonstrate the stability and feasibility of the proposed post-disaster monitoring system for bridge safety assessment.

The application of SHM has been broadened to the issues of preserving existing bridges, which are subjected to many years of usage and exposure to environmental factors. This paper aims to demonstrate the effectiveness of SHM in the maintenance and management of ageing structures, specifically a Pratt-truss steel bridge in Malaysia. The research combines static and dynamic methodologies to describe the ancient bridge’s serviceability. Operational modal analysis and FE analysis were first used to evaluate the structure's inherent frequencies and mode shapes, followed by a successful sensitivity-based model updating method. Next, static measurements were automated using displacement and strain data to evaluate the bridge’s condition. According to the study, the first four crucial bending and torsion modes for the ageing steel truss bridge occur between 4 and 29 Hz. Since the two approaches yield different MAC values and frequencies, employing a sensitivity analysis and model update procedure has become necessary. The frequency reference generated with EN1991-2:2003 bridge frequency constraints was then used to determine the bridge’s integrity, concluding that the bridge is safe and functional. Finally, the static analysis findings showed that the bridge is in a safe service condition regarding its deflection limit and strain limit.

Traditional seismic isolation structures perform poorly due to the impact of velocity pulses from near-field seismic waves. A conical friction pendulum isolator (CFPI) is a variable-curvature seismic isolation system, which can mitigate the resonance effect produced in seismic isolation structures by the velocity pulses of long-period near-field seismic waves. A multi-level friction damper (MFD) has a multistage energy dissipation mechanism and has been proven to have excellent shock absorption effects in structures for earthquakes of different intensities. Therefore, the present study integrated an MFD into a CFPI to develop a seismic isolation system (CFPI + MFD system) with improved safety under near-field seismic waves. Theoretical equations were established for this system to enable numerical simulation analysis. According to the results of numerical simulation analysis, the designed CFPI + MFD system has an excellent seismic isolation effect, whether under near-field seismic waves or large earthquakes. To verify the accuracy of the numerical simulation results, this study performed a shaking table test for a single-degree-of-freedom (SDOF) structure with the designed seismic isolation system. Experimental data derived from the shaking table test and the results of numerical simulation analysis were used to conduct fitting of the superstructure acceleration, base sliding displacement, and hysteresis loop data. The fitting results indicated that the numerical and experimental superstructure acceleration, base sliding displacement, and hysteresis loops exhibited a good fit, which validated the accuracy of the theoretical equations formulated in this study.