Enhancing Data Collection Time Intervals and Modeling the Structural Behavior of Bridges in Response to Temperature Variations

Buildings, Год журнала: 2025, Номер 15(3), С. 418 - 418

Опубликована: Янв. 28, 2025

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

Язык: Английский

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Multi-step cable force prediction based on hypergraph spatiotemporal deep learning

Engineering Structures, Год журнала: 2025, Номер 333, С. 120173 - 120173

Опубликована: Март 29, 2025

Язык: Английский

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Experimental study on failure detection of sandwich panels with different cores under bending, vibration and cyclic impact loading, use of piezoelectric technology

Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications, Год журнала: 2025, Номер unknown

Опубликована: Март 18, 2025

The main objective of this work is to characterise sandwich panels with carbon/epoxy composite skins and cores different materials (Expanded polystyrene foam, polyurethane foam injection sample) under three-point bending tests in free/free vibration repeated impact. analysis the behavior aims determine flexibility or stiffness these panels. However, impact failure modes strength three Indeed, damage was carried out using piezoelectric sensors connected an acquisition chain advanced signal processing technique, which allows precise detection quantification damage. Using technology, system ensures structures’ safety reliability by allowing early accurate potential relying on bonded capture responses can improve its safety, thus reducing maintenance costs ensuring extension service life. results various showed that carbon fibre resin matrix skin offers good protection types used In addition, panel expanded core responded better than other tests. tests, injected proved effective absorbing initial impacts. On hand, ability absorb impacts while more extended degradation two

Язык: Английский

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Dynamic Deformation Analysis of Super High-Rise Buildings Based on GNSS and Accelerometer Fusion

Sensors, Год журнала: 2025, Номер 25(9), С. 2659 - 2659

Опубликована: Апрель 23, 2025

To accurately capture the dynamic displacement of super-tall buildings under complex conditions, this study proposes a data fusion algorithm that integrates NRBO-FMD optimization with Adaptive Robust Kalman Filtering (ARKF). The method preprocesses GNSS and accelerometer to mitigate multipath effects, unmodeled errors, high-frequency noise in signals. Subsequently, ARKF fuses preprocessed achieve high-precision reconstruction. Numerical simulations varying conditions validated algorithm’s accuracy. Field experiments conducted on Hairong Square Building Changchun further demonstrated its effectiveness estimating three-dimensional displacement. Key findings are as follows: (1) significantly reduced while preserving essential signal characteristics. For data, root mean square error (RMSE) was 0.7 mm for 100 s dataset 1.0 200 dataset, corresponding signal-to-noise ratio (SNR) improvements 3.0 dB 6.0 dB. RMSE (100 s) 6.2 (200 s), 4.1 SNR gain. (2) NRBO-FMD–ARKF achieved high accuracy, values 1.9 s). Consistent PESD POSD long-term stability effective suppression irregular errors. (3) successfully fused 1 Hz overcoming limitations single-sensor approaches. yielded an 3.6 mm, 2.6 4.8 demonstrating both precision robustness. Spectral analysis revealed key response frequencies ranging from 0.003 0.314 Hz, facilitating natural frequency identification, structural stiffness tracking, early-stage performance assessment. This shows potential improving integration health monitoring. Future work will focus real-time predictive estimation enhance monitoring responsiveness early-warning capabilities.

Язык: Английский

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Study on noise reduction method for bridge temperature signal using adaptive parameter selection and improved wavelet threshold function

Measurement, Год журнала: 2025, Номер unknown, С. 117683 - 117683

Опубликована: Апрель 1, 2025

Язык: Английский

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Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review

Applied Sciences, Год журнала: 2025, Номер 15(9), С. 4855 - 4855

Опубликована: Апрель 27, 2025

Concrete has been one of the most essential building materials for decades, valued its durability, cost efficiency, and wide availability required components. Over time, number concrete bridges drastically increasing, highlighting need timely structural health monitoring (SHM) to ensure their safety long-term durability. Therefore, a narrative review was conducted examine use Artificial Intelligence (AI)-integrated techniques in SHM more effective monitoring. Moreover, this also examined significant damage observed various types bridges, with particular emphasis on cracking, detection methods, identification accuracy. Evidence points fact that conventional relies manual inspections are time-consuming, error-prone, require frequent checks, while AI-driven methods have emerged as promising alternatives, especially through Machine Learning- Deep Learning-based solutions. In addition, it noticeable integrating multimodal AI approaches improved accuracy reliability bridge assessments. Furthermore, is addresses critical gaps suggests developing accurate techniques, providing enhanced spatial resolution bridges.

Язык: Английский

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Unified framework for digital twins of bridges

Automation in Construction, Год журнала: 2025, Номер 175, С. 106214 - 106214

Опубликована: Май 1, 2025

Язык: Английский

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Population-based Markov Chain Monte Carlo method with delayed-acceptance applied to structural damage identification

Journal of the Brazilian Society of Mechanical Sciences and Engineering, Год журнала: 2025, Номер 47(6)

Опубликована: Май 3, 2025

Язык: Английский

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