Search Results (91)

The impact of temperature on bridges represents one of the main long-term challenges of structural health monitoring (SHM). Temperature is an environmental variable that changes both throughout the day and between different seasons, and its variations can induce thermal loads on bridges, potentially resulting in considerable displacements and deformations. Therefore, it is essential to obtain current data on the impact of daily and seasonal temperature variations on bridge displacements. Unfortunately, the maintenance costs associated with using precise estimates of thermal loads in a bridge design are quite high. The introduction of more accessible structural monitoring services is imperative to increase the number of observed structures. Viable solutions to make SHM more efficient include minimizing the costs of equipment, sensors, data loggers, data transmission systems, or monitoring data processing software. This research aims to improve the time intervals for collecting data on external temperature variations measured on a bridge structure through a sensor-based detection system and the integration of results into a regression analysis model. The paper aims to determine the appropriate interval for capturing and transmitting the structural response influenced by temperature variations over a year and to develop a behavioral mathematical model for the concrete structural components of a monitored bridge. The structural behavior was modeled using the statistical software TableCurve 2D, v.5.01. The results indicate that extending the data collection periods from 15 min to 4 h, in a static regime, maintains the accuracy of the regression model; instead, the effects of this integration are a significant reduction in the costs of data collection, transmission, and processing. The practical implications of this study consist of improving the monitoring of the structural behavior of bridges and the prediction under thermal stress, aiding in the design of more resilient structures, and enabling the implementation of efficient maintenance strategies. Full article

Redox-induced magnetic regulation in organic diradicals is distinctly attractive. In this work, taking nitroxide radicals as spin sources, we predict the magnetic properties of 9, 10-anthraquinone, 9, 10-phenaquone, 9, 10-diazanthracene and 9, 10-diazepine-bridged molecular diradical structures in which the couplers are prone to dihydrogenation reduction at positions 9 and 10. As evidenced at both the B3LYP and M06-2X levels of theory, the calculations confirm that the magnetic transitions between ferromagnetism and antiferromagnetism can take place for 9, 10-anthraquinone and 9, 10-diazanthracene-bridged diradicals after dihydrogenation. The differences in the magnetic behaviors and magnetic magnitudes of 9, 10-anthraquinone and 9, 10-diazanthracene-bridged diradicals before and after dihydrogenation could be attributed to their noticeably different spin-interacting pathways. As for 9, 10-phenaquone and 9, 10-diazepine-bridged diradicals, the calculated results indicate that the signs of their magnetic exchange coupling constants J do not change, but the magnitudes remarkably change after dihydrogenation. The connecting bond character and spin polarization are crucial in explaining the different magnetic magnitudes of these designed diradicals. In detail, shorter bonds and larger spin polarization are responsible for strong magnetic coupling. In addition, the diradical with an extensively π-conjugated structure can effectively promote magnetic coupling. The McConnell’s spin alternation rule is the key to understanding the observed ferromagnetism and antiferromagnetism of these diradicals. The work provides useful information for the rational design of redox-regulated magnetic molecular switches. Full article

Understanding the behavior of carbon dioxide (CO₂) under varying thermodynamic conditions is essential for optimizing processes such as Carbon Capture and Storage (CCS) and supercritical fluid extraction. This study employs molecular dynamics (MD) simulations with the EPM2 and TraPPE-small force fields to examine CO₂ phase behavior, structural characteristics, and transport properties across a temperature range of 228–500 K and pressures from 1 to 150 atm. Our findings indicate a good agreement between simulated and experimental liquid–vapor coexistence curves, validating the capability of both force fields to model CO₂ accurately in a wide range of thermodynamical conditions. Radial distribution functions (RDFs) reveal distinct interaction patterns in liquid and supercritical phases, while mean squared displacement (MSD) analyses show diffusivity increasing from 5.2×10−9 m²/s at 300 K to 1.8×10−8 m²/s at 500 K. Additionally, response functions such as the heat capacity effectively capture phase transitions. These findings provide quantitative insights into CO₂ phase behavior and transport properties, enhancing the predictive reliability of simulations for CCS and related industrial technologies. This work bridges gaps in the CO₂ modeling literature and highlights the potential of MD simulations in advancing sustainable applications. Full article

This study presents a framework for integrating digital twins and knowledge graphs to enhance heritage building conservation, addressing challenges in environmental stress management, material degradation, and structural integrity while preserving historical authenticity. Using validated synthetic data, the framework enables real-time monitoring, predictive maintenance, and emergency response through a digital twin connected to a knowledge graph. Four scenarios were simulated to evaluate system performance: high humidity exceeding a 75% threshold triggered alerts for limestone maintenance; temperature fluctuations caused strain levels up to 0.009 units in load-bearing components at 35 °C, necessitating structural inspection; cumulative degradation monitoring projected re-plastering needs by month eight as the plaster degradation index approached 85%; and sudden impact events simulated emergency responses, with strain spikes over 0.004 units prompting real-time alerts within 2.5 s. Response times averaged 50 ms under normal conditions, peaking at 150 ms during high-frequency updates, showing robust Application Programming Interface (API) performance and data synchronization. SPARQL (SPARQL Protocol and RDF Query Language) queries within the knowledge graph facilitated proactive maintenance scheduling, reducing reactive interventions and supporting sustainable heritage conservation, especially suited to humid–temperate climates. This framework offers a novel, structured approach that bridges modern technology with heritage preservation needs, addressing both urgent conservation challenges and long-term sustainability to ensure the resilience of heritage buildings. Full article

Corrosion and fatigue damage of high-strength steel wires in cable-stayed bridges in coastal environments can seriously affect the reliability of bridges. Previous studies have focused on isolated factors such as corrosion rates or stress ratios, failing to capture the complex interactions between multiple variables. In response to the critical need for accurate fatigue life prediction of high-strength steel wires under corrosive conditions, this study proposes an innovative prediction model that combines Grey Wolf Optimization (GWO) with a Backpropagation Neural Network (BPNN). The optimized GWO-BPNN model significantly enhances prediction accuracy, stability, generalization, and convergence speed. By leveraging GWO for efficient hyperparameter optimization, the model effectively reduces overfitting and strengthens robustness under varying conditions. The test results demonstrate the model’s high performance, achieving an R² value of 0.95 and an RMSE of 140.45 on the test set, underscoring its predictive reliability and practical applicability. The GWO-BPNN model excels in capturing complex, non-linear dependencies within fatigue data, outperforming conventional prediction methods. Sensitivity analysis identifies stress range, average stress, and mass loss as primary determinants of fatigue life, highlighting the dominant influence of corrosion and stress factors on structural degradation. These results confirm the model’s interpretability and practical utility in pinpointing key factors that impact fatigue life. Overall, this study establishes the GWO-BPNN model as a highly accurate and adaptable tool, offering substantial support for advancing predictive maintenance strategies and enhancing material resilience in corrosive environments. Full article

Corrosion-induced degradation in concrete and reinforced concrete (RC) structures, often initiated within the first few decades of their lifespan, significantly challenges seismic resistance. While existing research tools can assess performance, they fall short in predicting changes in seismic resistance resulting from alterations in the core properties of RC structures. To bridge this gap, we introduce a numerical seismic resistance prediction method (SRPM) specifically designed to predict changes in the seismic resistance of structures, including those reinforced with carbon-fiber-reinforced polymer (CFRP), known for its non-corrosive properties. This study utilizes classical models to estimate corrosiveness and employs these models alongside section strength predictions to gauge durability. The nonlinear static pushover analysis (POA) model is implemented utilizing SAP-2000 and Response-2000 software. A comparative analysis between steel-reinforced and carbon-fiber-reinforced polymer concrete (CRC) structures reveals distinct differences in their seismic resistance over time. Notably, steel-reinforced structures experience a significant decrease in their ability to dissipate seismic energy, losing 54.4% of their capacity after 170 years. In contrast, CFRP-reinforced structures exhibit a much slower degradation rate, with only 25.5% reduction over the same period. The discrepancy demonstrates CFRP’s superior durability and ability to maintain structural integrity in the face of seismic stresses. Full article

The synchronous construction of the pylon and cables of a cable-stayed bridge without backstays has the characteristics of a short construction period and reduced support costs. However, it also increases the difficulty of construction control, making the reasonable completion state of the bridge more complex. To investigate the impact of various load parameters on the structural state of a cable-stayed bridge without backstays during the synchronous construction process, and to ensure a rational final bridge state, this study proposes an assessment framework for evaluating the internal forces of the bridge. The framework initially uses the response surface method to establish explicit equations relating the control indicators of the bridge’s final state to various load parameters. Subsequently, through sensitivity analysis, the degree of influence of each load parameter on the structural response of the cable-stayed bridge without backstays is examined. The most sensitive factors are identified to create a bridge parameter influence library, which helps reduce computational costs. Based on this, a method for controlling construction errors and predicting cable forces is proposed. This method utilizes the pre-established bridge parameter influence library, combined with the internal force state of the bridge at the current construction stage, to accurately predict the tension force of the stay cables in the subsequent stage, thereby ensuring a rational final bridge state. The framework is ultimately validated through a case study of the Longgun River Bridge to assess its rationality and effectiveness. Full article

Monitoring and predicting the dynamic responses of railway bridges under moving trains, including displacement and acceleration, are vital for evaluating the safety and serviceability of the train–bridge system. Traditionally, finite element analysis methods with high computational burden are used to predict the train-induced responses according to the given train loads and, hence, cannot easily be integrated as an available structural-health-monitoring strategy. Therefore, this study develops a novel framework, combining the train–bridge coupling mechanism and deep learning algorithms to efficiently predict the train-induced bridge responses while considering train load duration. Initially, the feasibility of using neural networks to calculate the train–bridge coupling vibration is demonstrated by leveraging the nonlinear relationship between train load and bridge responses. Subsequently, the instantaneous multiple moving axial loads of the moving train are regarded as the equivalent node loads that excite adjacent predefined nodes on the bridge. Afterwards, a deep long short-term memory (LSTM) network is established as a surrogate model to predict the train-induced bridge responses. Finally, the prediction accuracy is validated using a numerical case study of a simply supported railway bridge. The factors that may affect the prediction accuracy, such as network structure, training samples, the number of structural units, and noise level, are discussed. Results show that the developed framework can efficiently predict the train-induced bridge responses. The prediction accuracy of the bridge displacement is higher than that of the acceleration. In addition, the robustness of the displacement prediction is proven to be better than that of the acceleration with the variation of carriage number, riding speed, and measurement noise. Full article

The intersectional relationship in bridge health monitoring refers to the mapping function that correlates bridge responses across different locations. This relationship is pivotal for estimating structural responses, which are then instrumental in assessing a bridge’s service status and identifying potential damage. The current research landscape is heavily focused on high-frequency responses, especially those associated with single-mode vibration. When it comes to low-frequency responses triggered by multi-mode vehicle loading, a prevalent strategy is to regard these low-frequency responses as “quasi-static” and subsequently apply time-series prediction techniques to simulate the intersectional relationship. However, these methods are contingent upon data regarding external loading, such as traffic conditions and air temperatures. This necessitates the collection of long-term monitoring data to account for fluctuations in traffic and temperature, a task that can be quite daunting in real-world engineering contexts. To address this challenge, our study shifts the analytical perspective from a static analysis to a dynamic analysis. By delving into the physical features of bridge responses of the vehicle–bridge interaction (VBI) system, we identify that the intersectional relationship should be inherently time-independent. The perceived time lag in quasi-static responses is, in essence, a result of low-frequency vibrations that are aligned with driving force modes. We specifically derive the intersectional relationship for low-frequency bridge responses within the VBI system and determine it to be a time-invariant transfer matrix associated with multiple mode shapes. Drawing on these physical insights, we adopt a time-independent machine learning method, the backpropagation–artificial neural network (BP-ANN), to simulate the intersectional relationship. To train the network, monitoring data from various cross-sections were input, with the responses at a particular section designated as the output. The trained network is now capable of estimating responses even in scenarios where time-related traffic conditions and temperatures deviate from those present in the training data set. To substantiate the time-independent nature of the derived intersectional relationship, finite element models were developed. The proposed method was further validated through the in-field monitoring of a continuous highway bridge. We anticipate that this method will be highly effective in estimating low-frequency responses under a variety of unknown traffic and air temperature conditions, offering significant convenience for practical engineering applications. Full article

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts on railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data are transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events, like impact detection, that require a rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine-learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications. Full article

The study of the dynamic response characteristics of “W”-type deep canyon terrain to double-span concrete arch bridges under earthquake action holds great practical significance. In this research, a bridge in Sichuan Province is taken as the object of study. The boundary-integral equation method and peak frequency shift method are combined to apply an embedded linear time–history analysis algorithm to the finite element spatial dynamic calculation model of the entire bridge, resulting in an improved model. By comparing these two methods with model test results, the seismic response characteristics of the middle part of a “W” concrete arch bridge under different foundation depths and seismic intensities are examined. The boundary integral equation method was utilized to calculate ground motion response at any point on site, revealing a significant amplifying effect of increased seismic wave intensity on acceleration response at the top of the arch bridge. When input seismic wave intensity increased from 0.1 g to 0.3 g, maximum acceleration at buried depths of 3 m and 8 m in the middle of the arch bridge foundation increased by 102.63% and 79.16%, respectively, indicating that shallow buried depth structures are more sensitive to seismic wave intensity. Furthermore, using peak frequency shift rules for analyzing seismic wave propagation characteristics in “W”-type deep canyon topography confirms the sensitivity of shallow buried depth structures to seismic wave intensity and reveals the mechanism through which topography influences seismic wave propagation. This study provides a helpful method for understanding the propagation law and energy distribution characteristics of seismic waves in complex terrain. It was observed that the displacement at the top of the arch bridge increased significantly with an increase in seismic intensity. When subjected to 0.1 g, 0.2 g, and 0.3 g EI-Centro seismic waves, the maximum displacement at the top of the arch bridge model with a foundation buried depth of 3 m was 8 mm, 32 mm, and 142 mm, respectively. For arch bridge models with an 8-m foundation buried depth, these displacements were measured at 6.2 mm, 21 mm, and 68 mm, respectively. The results from model tests verified that increasing the depth of foundation burial effectively reduces the displacement at the top of the structure. Furthermore, by combining a boundary-integral equation method and peak-frequency shift method, this study accurately predicted significant influences on W-shaped double deep canyon topography from seismic response, and successfully captured stress concentration and seismic wave amplification/focusing effects on arch foot structures. The calculated results from both methods align well with model test data which confirm their effectiveness and complementarity when analyzing seismic responses under complex terrain conditions for bridge structures. Full article

Bridges are critical infrastructures that support our economic activities and daily lives. Aging bridges have been a major issue for decades, prompting researchers to improve resilience and performance through structural health monitoring. While most research focuses on superstructure damage, the majority of bridge failures are associated with support or joint damages, indicating the importance of bridge support. Indeed, bridge support affects the performance of both the substructure and superstructure by maintaining the load path and allowing certain movements to mitigate thermal and other stresses. The support deterioration leads to a change in fixity in the superstructure, compromising the bridge’s integrity and safety. Hence, a reliable method to determine support fixity level is essential to detecting bearing health and enhancing the accuracy of the bridge health monitoring system. However, such research is lacking because of its complexity. In this study, we developed a support fixity quantification method based on thermal responses using an Artificial Neural Network (ANN) model. A finite element (FE) model of a representative highway bridge is used to derive thermal displacement data under different bearing stiffnesses, superstructure damage, and thermal loading. The thermal displacement behavior of the bridge under different support fixity conditions is presented, and the model is trained on the simulated response. The performance of the developed FE model and ANN was validated with field monitoring data collected from two in-service bridges in Connecticut using a real-time Wireless Sensor Network (WSN). Finally, the support stiffnesses of both bridges were predicted using the ANN model for validation. Full article

Pedestrian bridges, which are essential in urban and rural infrastructures, are vulnerable to vibrations induced by pedestrian traffic owing to their low mass, stiffness, and damping. This paper presents a novel predictive model of Human-Structure Interaction (HSI) that integrates a three-dimensional biomechanical model of the human body, and a pedestrian bridge represented as a simply supported Euler-Bernoulli beam. Using inverse dynamics, the human model accurately captures three-dimensional gait and its interaction with structural vibrations. The results show that this approach provides precise estimates of human gait kinematics and kinetics, as well as the bridge response under pedestrian loads. The incorporation of a three-dimensional human gait model reflects the changes induced by bridge vibrations, providing a robust tool for evaluating and improving the effect of structural vibrations on the properties and gait patterns. Full article

Steel fibre-reinforced concrete (SFRC) consists of short, hooked steel fibres that are randomly distributed and oriented within the cementitious matrix. This paper presents a new analytical load-bounding approach that captures the tensile response of misaligned fibres embedded in the matrix. The contribution of fibres in bridging cracks to provide the required stress transfer relies on the orientation of the fibres in the concrete. Bridging fibres aligned with a crack are less effective than those inclined to it. Therefore, understanding the pull-out behaviour of misaligned fibres is a key factor in quantifying and optimising the design of SFRC in structural applications. In the laboratory, a single-oriented fibre embedded in a solid cylinder of concrete was subjected to a pull-out test, where the axis of the tensile force is aligned with the axis of the cylinder. Based on the observed behaviour, this paper presents a new analytical bounding approach to capture the pull-out response of misaligned hooked-end steel fibres embedded in a concrete matrix. The analysis was based on a transversely isotropic failure criterion assumed for the plasticity that occurs in the cold-drawn fibre. Lower and upper bounds to the loading failure were derived from fibre pull-out and fibre fracture, respectively. The division between bounds depended upon the fibre orientation, fibre diameter and the combined strengths of the steel and concrete. Bounding predictions were drawn from ratios between a fibre’s shear strength and its transverse and axial uniaxial strengths, as found from a novel testing proposal. The two bounds were compared with new data and other experimental results published in the literature. The results showed that the region between the bounds captured the failure loads of embedded fibres with effective load-bearing orientations. A critical orientation was observed at maximum strength. The present interpretation of the plasticity occurring within off-axis, hooked-end steel fibres suggests that it is possible to optimise the strength of concrete using this method of reinforcement. Full article

The role of amino acid transporter (AAT) genes in facilitating the transmembrane movement of amino acids between cells and various cellular components has been characterized in several plant species. Quinoa (Chenopodium quinoa Willd.), a renowned nutritious crop known for its amino acid composition, has not yet had its AAT genes characterized. Therefore, the identification and characterization of AAT genes in quinoa will help bridge this knowledge gap and offer valuable insights into the genetic mechanisms underlying amino acid transport and metabolism. This study focuses on gene expression, gene structure, duplication events, and a comparison of functions studied to establish the role of AAT genes. A total of 160 non-redundant AAT genes were identified in quinoa and classified into 12 subfamilies, with 8 subfamilies belonging to the amino acid/auxin permease (AAAP) family and 4 to the amino acid-polyamine-organocation (APC) superfamily family. The chromosomal localization, gene structures, and conserved motifs of these genes were systematically analyzed. Expression profiling revealed diverse expression patterns across various tissues and in response to drought and salt stresses. Segmental and tandem duplications were found to contribute to the gene duplication and expansion of the CqAAT gene family. Additionally, CqCAT6 and CqAAP1 were predicted to regulate the long-distance transportation and distribution of amino acids, making them potential candidate genes for further research. Overall, this information could serve as a foundation for the identification and utilization of CqAATs in Quinoa, enhancing our understanding of amino acid transport mechanisms in this important crop. Full article