Structural Health Monitoring and Performance Evaluation of Bridges and Structural Elements

A special issue of Infrastructures (ISSN 2412-3811). This special issue belongs to the section "Infrastructures and Structural Engineering".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 20034

Special Issue Editors


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Guest Editor
Department of Civil and Environmental Engineering, College of Engineering and Computing, Florida International University, 10555 West Flagler Street, EC 3602, Miami, FL 33174, USA
Interests: bridge engineering; non-destructive evaluation of bridges; structural health monitoring; vibration analysis and mitigation; structural performance evaluation; field and laboratory testing; bridge rehabilitation and corrosion mitigation; analysis and modeling of masonry and R/C frames; fiber-reinforced polymer applications
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Guest Editor
Department of Civil and Environmental Engineering, College of Engineering and Computing, Florida International University, 10555 West Flagler Street, EC 3660, Miami, FL 33174, USA
Interests: non-destructive evaluation of bridges; structural health monitoring; structural performance evaluation; field and laboratory testing; analysis and modeling of masonry and R/C frames; fiber reinforced polymer applications

Special Issue Information

Dear Colleagues,

Bridges and other structures that support critical services necessary for communities constitute an important part of the surface infrastructure. It is important to have means and methods for health monitoring and performance evaluation of the existing structures so that their safety can be evaluated and preventive maintenance be performed on time, long before the extent of the damage necessitates drastic actions. This is even more important for the resiliency of communities that are prone to natural hazards and need quick recovery after each extreme event. As the many bridge failures over the past few decades have shown, conventional and routine bridge monitoring is insufficient to effectively evaluate safety, and more effective methods for damage detection and structural monitoring are needed. At the same time, condition assessment and performance evaluation of bridges and structures will allow the decision-makers to allocate their assets on a priority basis for hardening inadequate structures for better resiliency.

This Special Issue will compile articles on a wide range of topics related to the existing and new non-destructive evaluation (NDE) methods, structural health monitoring (SHM) and damage detection techniques applicable to bridges and other structures. Methods including but not limited to hands-on non-destructive testing (NDT), non-contact or vision-based sensors and instrumentation, load testing, vibration analysis, as well as the application of novel sensors and instrumentation are encouraged. This issue will also include condition assessment and performance evaluation methods and maintenance approaches which use the results of NDE and SHM to devise preventive and preservation tactics. The structural health monitoring and condition assessment have evolved significantly in recent years with introduction of innovative sensors, data communication, non-destructive evaluation, and methods of delivery. Therefore, innovative approaches to health monitoring and condition assessment are specially solicited for this Special Issue, along with new approaches to maintenance. 

Dr. Armin Mehrabi
Dr. Seyed Saman Khedmatgozar Dolati
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • structural health monitoring
  • bridges
  • structural elements
  • non-destructive testing
  • condition assessment
  • performance evaluation
  • inspection
  • maintenance

Published Papers (12 papers)

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Research

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15 pages, 11142 KiB  
Article
Inertial Methodology for the Monitoring of Structures in Motion Caused by Seismic Vibrations
by Julio C. Rodríguez-Quiñonez, Jorge Alejandro Valdez-Rodríguez, Moises J. Castro-Toscano, Wendy Flores-Fuentes and Oleg Sergiyenko
Infrastructures 2024, 9(7), 116; https://doi.org/10.3390/infrastructures9070116 - 19 Jul 2024
Viewed by 268
Abstract
This paper presents a non-invasive methodology for structural health monitoring (SHM) integrated with inertial sensors and signal conditioning techniques. The proposal uses the signal of an IMU (inertial measurement unit) tri-axial accelerometer and gyroscope to continuously measure the displacements of a structure in [...] Read more.
This paper presents a non-invasive methodology for structural health monitoring (SHM) integrated with inertial sensors and signal conditioning techniques. The proposal uses the signal of an IMU (inertial measurement unit) tri-axial accelerometer and gyroscope to continuously measure the displacements of a structure in motion due to seismic vibrations. A system, called the “Inertial Displacement Monitoring System” or “IDMS”, is implemented to attenuate the signal error of the IMU with methodologies such as a Kalman filter to diminish the influence of white noise, a Chebyshev filter to isolate the frequency values of a seismic motion, and a correction algorithm called zero velocity observation update (ZVOB) to detect seismic vibrations and diminish the influence of external perturbances. As a result, the IDMS is a methodology developed to measure displacements when a structure is in motion due to seismic vibration and provides information to detect failures opportunely. Full article
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13 pages, 4432 KiB  
Article
Research on the Protection Scheme of a High-Speed Railway Crossing 1000 KV Ultra-High Voltage Transmission Line
by Yi Song and Wei Xiao
Infrastructures 2024, 9(7), 110; https://doi.org/10.3390/infrastructures9070110 - 15 Jul 2024
Viewed by 415
Abstract
The high-speed railway project and the ultra-high-voltage transmission project represent two crucial components of China’s “new infrastructure”. As the construction of these two projects progresses rapidly, it is inevitable that instances of intersections will occur. Extreme conditions may cause damage to ultra-high voltage [...] Read more.
The high-speed railway project and the ultra-high-voltage transmission project represent two crucial components of China’s “new infrastructure”. As the construction of these two projects progresses rapidly, it is inevitable that instances of intersections will occur. Extreme conditions may cause damage to ultra-high voltage transmission cables. When a high-speed train passes by an ultra-high voltage transmission line, it poses a serious safety hazard. To address this issue, engineering examples were utilized to examine the protection structure scheme, protection distance, protection load, and construction procedures when a high-speed railway intersects a 1000 KV ultra-high voltage transmission line. A shed structure form and construction method for the electric power protection were proposed to ensure the safe operation of the high-speed railway while also achieving the safe and rapid construction of the high-speed railway protection structure in the safety zone of the approaching 1000 kV ultra-high voltage transmission line. The results indicated that the protection of high-speed railway crossings and 1000 kV ultra-high voltage transmission lines primarily focuses on line-break protection. The concrete shed structure with a straight wall and a flat roof was designed to meet the requirements of high-speed railway crossings. The line-break protection method enables the construction of an automatic warning protection corridor and a complete movable trolley quickly and safely within the safety zone near the transmission line. The implementation effect is, therefore, positive. It can be used as a reference point for other projects of a similar nature. Full article
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21 pages, 14704 KiB  
Article
Damage Detection in External Tendons of Post-Tensioned Bridges
by Dariya Tabiatnejad, Bardia Tabiatnejad, Seyed Saman Khedmatgozar Dolati and Armin Mehrabi
Infrastructures 2024, 9(7), 103; https://doi.org/10.3390/infrastructures9070103 - 30 Jun 2024
Viewed by 585
Abstract
This study investigates damage detection in the tendons of post-tensioned segmental box girder bridges, focusing on the vibration-based technique and its application in conjunction with the Precursor Transformation Matrix (PTM). Due to the critical role of bridge tendons in structural integrity, efficient and [...] Read more.
This study investigates damage detection in the tendons of post-tensioned segmental box girder bridges, focusing on the vibration-based technique and its application in conjunction with the Precursor Transformation Matrix (PTM). Due to the critical role of bridge tendons in structural integrity, efficient and timely detection methods are essential. The methodology combines theoretical modeling with Finite Element Method (FEM) simulations and empirical data collection to evaluate the PTM’s effectiveness in identifying tendon damage. Key results indicate that the PTM, when paired with vibration analysis, enhances the identification and localization of damage, proving to be a robust method in structural health monitoring. This approach not only speeds up damage detection but also potentially lowers maintenance costs by pinpointing specific damage sites, thereby preventing widespread structural failures. The findings offer a promising tool for ensuring the longevity and safety of post-tensioned bridges. Full article
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13 pages, 3236 KiB  
Article
Improved Blob-Based Feature Detection and Refined Matching Algorithms for Seismic Structural Health Monitoring of Bridges Using a Vision-Based Sensor System
by Luna Ngeljaratan, Mohamed A. Moustafa, Agung Sumarno, Agus Mudo Prasetyo, Dany Perwita Sari and Maidina Maidina
Infrastructures 2024, 9(6), 97; https://doi.org/10.3390/infrastructures9060097 - 14 Jun 2024
Viewed by 721
Abstract
The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, [...] Read more.
The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, traffic, or drone cameras, may assist in preventing future impacts due to structural deficiency and are critical to the emergence of sustainable and smart transportation infrastructure. This study evaluates several feature detection and tracking algorithms and implements them in the vision-based SHM of bridges along with their systematic procedures. The proposed procedures are implemented via a two-span accelerated bridge construction (ABC) system undergoing a large-scale shake-table test. The research objectives are to explore the effect of refined matching algorithms on blob-based features in improving their accuracies and to implement the proposed algorithms on large-scale bridges tested under seismic loads using vision-based SHM. The procedure begins by adopting blob-based feature detectors, i.e., the scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE algorithms, and their stability is compared. The least medium square (LMEDS), least trimmed square (LTS), random sample consensus (RANSAC), and its generalization maximum sample consensus (MSAC) algorithms are applied for model fitting, and their sensitivity for removing outliers is analyzed. The raw data are corrected using mathematical models and scaled to generate displacement data. Finally, seismic vibrations of the bridge are generated, and the seismic responses are compared. The data are validated using target-tracking methods and mechanical sensors, i.e., string potentiometers. The results show a good agreement between the proposed blob feature detection and matching algorithms and target-tracking data and reference data obtained using mechanical sensors. Full article
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20 pages, 1924 KiB  
Article
Comprehensive Empirical Modeling of Shear Strength Prediction in Reinforced Concrete Deep Beams
by Eyad K. Sayhood, Nisreen S. Mohammed, Salam J. Hilo and Salih S. Salih
Infrastructures 2024, 9(4), 67; https://doi.org/10.3390/infrastructures9040067 - 28 Mar 2024
Viewed by 1381
Abstract
This paper presents comprehensive empirical equations to predict the shear strength capacity of reinforced concrete deep beams, with a focus on improving the accuracy of existing codes. Analyzing 198 deep beams imported from 15 existing investigations, this study considers various parameters such as [...] Read more.
This paper presents comprehensive empirical equations to predict the shear strength capacity of reinforced concrete deep beams, with a focus on improving the accuracy of existing codes. Analyzing 198 deep beams imported from 15 existing investigations, this study considers various parameters such as concrete compressive strength (f′c), the shear span-to-effective depth ratio (av/d), and reinforcement ratios (ps, pv, and ph). Introducing a novel predictive empirical equation, this study conducts a rigorous evaluation using statistical metrics and a linear regression analysis (MAE, RMSE, and R2). The proposed model demonstrates a significant reduction in the coefficient of variation (CV) to 27.08%, compared to the existing codes’ limitations. Comparative analyses highlight the accuracy of the empirical equation, revealing an improved convergence of data points and minimal sensitivity to variations in key parameters. The results proved that the proposed empirical equation enhanced the accuracy to predict the shear strength capacity of the reinforced concrete deep beams in various scenarios, making it a valuable tool for structural engineers. This research contributes to advancing the understanding of shear strength capacity in reinforced concrete deep beams, offering a reliable empirical equation with implications for refining design methodologies and enhancing safety with the efficiency of structural systems. Full article
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20 pages, 3172 KiB  
Article
Machine Learning and Signal Processing for Bridge Traffic Classification with Radar Displacement Time-Series Data
by Matthias Arnold and Sina Keller
Infrastructures 2024, 9(3), 37; https://doi.org/10.3390/infrastructures9030037 - 22 Feb 2024
Viewed by 1663
Abstract
This paper introduces a novel nothing-on-road (NOR) bridge weigh-in-motion (BWIM) approach with deep learning (DL) and non-invasive ground-based radar (GBR) time-series data. BWIMs allow site-specific structural health monitoring (SHM) but are usually difficult to attach and maintain. GBR measures the bridge deflection contactless. [...] Read more.
This paper introduces a novel nothing-on-road (NOR) bridge weigh-in-motion (BWIM) approach with deep learning (DL) and non-invasive ground-based radar (GBR) time-series data. BWIMs allow site-specific structural health monitoring (SHM) but are usually difficult to attach and maintain. GBR measures the bridge deflection contactless. In this study, GBR and an unmanned aerial vehicle (UAV) monitor a two-span bridge in Germany to gather ground-truth data. Based on the UAV data, we determine vehicle type, lane, locus, speed, axle count, and axle spacing for single-presence vehicle crossings. Since displacement is a global response, using peak detection like conventional strain-based BWIMs is challenging. Therefore, we investigate data-driven machine learning approaches to extract the vehicle configurations directly from the displacement data. Despite a small and imbalanced real-world dataset, the proposed approaches classify, e.g., the axle count for trucks with a balanced accuracy of 76.7% satisfyingly. Additionally, we demonstrate that, for the selected bridge, high-frequency vibrations can coincide with axles crossing the junction between the street and the bridge. We evaluate whether filtering approaches via bandpass filtering or wavelet transform can be exploited for axle count and axle spacing identification. Overall, we can show that GBR is a serious contender for BWIM systems. Full article
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24 pages, 8021 KiB  
Article
The “M and P” Technique for Damage Identification in Reinforced Concrete Bridges
by Athanasios Bakalis, Triantafyllos Makarios and Vassilis Lekidis
Infrastructures 2024, 9(2), 18; https://doi.org/10.3390/infrastructures9020018 - 25 Jan 2024
Viewed by 1749
Abstract
The seismic damage in reinforced concrete bridges is identified in this study using the “M and P” hybrid technique initially developed for planar frames, where M signifies “Monitoring” and P denotes “Pushover analysis”. The proposed methodology involves a series of pushover [...] Read more.
The seismic damage in reinforced concrete bridges is identified in this study using the “M and P” hybrid technique initially developed for planar frames, where M signifies “Monitoring” and P denotes “Pushover analysis”. The proposed methodology involves a series of pushover and instantaneous modal analyses with a progressively increasing target deck displacement along the longitudinal direction of the bridge. From the results of these analyses, the diagram of the instantaneous eigenfrequency of the bridge, ranging from the health state to near collapse, is plotted against the inelastic seismic deck displacement. By pre-determining the eigenfrequency of an existing bridge along its longitudinal direction through “monitoring and frequency identification”, the target deck displacement corresponding to the damage state can directly be found from this diagram. Subsequently, the damage can be identified by examining the results of the pushover analysis at the step where the target deck displacement is indicated. The effectiveness of this proposed technique is evaluated in the context of straight multiple span bridges with unequal pier heights, illustrated through an example of a four-span bridge. The findings demonstrate that the damage potential in bridge piers can be successfully identified by combining the results of a monitoring process and pushover analysis. Full article
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16 pages, 9253 KiB  
Article
Deep Learning-Based Steel Bridge Corrosion Segmentation and Condition Rating Using Mask RCNN and YOLOv8
by Zahra Ameli, Shabnam Jafarpoor Nesheli and Eric N. Landis
Infrastructures 2024, 9(1), 3; https://doi.org/10.3390/infrastructures9010003 - 20 Dec 2023
Cited by 3 | Viewed by 3298
Abstract
The application of deep learning (DL) algorithms has become of great interest in recent years due to their superior performance in structural damage identification, including the detection of corrosion. There has been growing interest in the application of convolutional neural networks (CNNs) for [...] Read more.
The application of deep learning (DL) algorithms has become of great interest in recent years due to their superior performance in structural damage identification, including the detection of corrosion. There has been growing interest in the application of convolutional neural networks (CNNs) for corrosion detection and classification. However, current approaches primarily involve detecting corrosion within bounding boxes, lacking the segmentation of corrosion with irregular boundary shapes. As a result, it becomes challenging to quantify corrosion areas and severity, which is crucial for engineers to rate the condition of structural elements and assess the performance of infrastructures. Furthermore, training an efficient deep learning model requires a large number of corrosion images and the manual labeling of every single image. This process can be tedious and labor-intensive. In this project, an open-source steel bridge corrosion dataset along with corresponding annotations was generated. This database contains 514 images with various corrosion severity levels, gathered from a variety of steel bridges. A pixel-level annotation was performed according to the Bridge Inspectors Reference Manual (BIRM) and the American Association of State Highway and Transportation Officials (AASHTO) regulations for corrosion condition rating (defect #1000). Two state-of-the-art semantic segmentation algorithms, Mask RCNN and YOLOv8, were trained and validated on the dataset. These trained models were then tested on a set of test images and the results were compared. The trained Mask RCNN and YOLOv8 models demonstrated satisfactory performance in segmenting and rating corrosion, making them suitable for practical applications. Full article
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16 pages, 8284 KiB  
Article
Evaluation of the Szapáry Long-Span Box Girder Bridge Using Static and Dynamic Load Tests
by Marame Brinissat, Richard Paul Ray and Rajmund Kuti
Infrastructures 2023, 8(5), 91; https://doi.org/10.3390/infrastructures8050091 - 9 May 2023
Cited by 4 | Viewed by 1555
Abstract
This paper presents the results of a recent field test carried out before the opening phases of the Szapáry motorway bridge across the Tisza River in central Hungary. The evaluation test was based on static and dynamic load tests that provided information on [...] Read more.
This paper presents the results of a recent field test carried out before the opening phases of the Szapáry motorway bridge across the Tisza River in central Hungary. The evaluation test was based on static and dynamic load tests that provided information on deflection, stresses, and dynamic mode shapes along the bridge. The structure has two large continuous independent steel box girders that cover spans across the floodplain and river. Various configurations of truck loading applied up to 6400 kN of loading on the deck. During the static tests, string potentiometers recorded deflections at mid-span. Additionally, strain gauges enabled strain/stress measurements at the mid-point of the longest span and directly above one support. Dynamic loadings showed variation in deflection response due to vehicle speed, and ambient vibration testing led to determining vibration modes and frequencies. A three-dimensional finite-element model produced similar deflections, stresses, and modal behavior. Measured and modeled deflections and stresses indicated that the bridge performed within design margins. The testing and analysis results will be part of a future program assessing conditioned-based maintenance. Full article
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17 pages, 7050 KiB  
Article
Study on Deflection-Span Ratio of Cable-Stayed Suspension Cooperative System with Single-Tower Space Cable
by Lin Xiao, Yaxi Huang and Xing Wei
Infrastructures 2023, 8(3), 62; https://doi.org/10.3390/infrastructures8030062 - 22 Mar 2023
Cited by 3 | Viewed by 2206
Abstract
This study uses the wind–vehicle–bridge coupling vibration analysis method to investigate the bridge stiffness problem of a large-span cable-stayed-suspension cooperative system. On the basis of the particle-damping-spring vehicle model, the TMeasy surface contact tire model is introduced, and a set of universal wind–vehicle–bridge [...] Read more.
This study uses the wind–vehicle–bridge coupling vibration analysis method to investigate the bridge stiffness problem of a large-span cable-stayed-suspension cooperative system. On the basis of the particle-damping-spring vehicle model, the TMeasy surface contact tire model is introduced, and a set of universal wind–vehicle–bridge coupling analysis algorithm is built in the framework of the whole process iterative method. Based on the Latin supercube sampling principle, random traffic flow is generated and loaded onto bridge structures with different stiffness conditions to analyze the driving comfort and safety under each stiffness condition. Combining the specification requirements, engineering experience, and research results, the vertical stiffness limit applicable to the bridge of the highway cable-stayed-suspension collaborative system is proposed. Existing engineering experience shows that the vertical deflection-to-span ratio of a cable-stayed bridge under live load is distributed between 1/400 and 1/1600, and the vertical deflection span ratio under the action of lane load is recommended based on numerical analysis. Full article
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23 pages, 9026 KiB  
Article
Micro-Scale Experimental Approach for the Seismic Performance Evaluation of RC Frames with Improper Lap Splices
by Ali Javed, Chaitanya Krishna, Khawaja Ali, Muhammad Faheem Ud Din Afzal, Armin Mehrabi and Kimiro Meguro
Infrastructures 2023, 8(3), 56; https://doi.org/10.3390/infrastructures8030056 - 15 Mar 2023
Cited by 10 | Viewed by 1990
Abstract
Reinforced concrete (RC) frames are an integral part of modern construction as they resist both gravity and lateral loads in beams and columns. However, the construction methodologies of RC frames are vulnerable to non-engineering defects, particularly in developing countries. The most common non-engineering [...] Read more.
Reinforced concrete (RC) frames are an integral part of modern construction as they resist both gravity and lateral loads in beams and columns. However, the construction methodologies of RC frames are vulnerable to non-engineering defects, particularly in developing countries. The most common non-engineering defect occurs due to improper lap splice, which can compromise the structural integrity. This research demonstrates an easy, low-cost, and verifiable experimental technique incorporating micro-concrete to evaluate the seismic performance of a completely engineered RC frame with the defect of improper lap splice. The micro-concrete was prepared by using the locally available material for a target compressive strength and then two scaled-down RC frames (1/16 scale) were prepared, including one proper frame and another with improper lap splice. Finally, these frames were tested on a shake table to study their behavior under various seismic loading conditions. This study quantifies the severity of high-risk structural systems due to non-engineering defects. The experimental results demonstrate that improper lap splice can alter the frame’s damage points, triggering the failure of the whole structure. Full article
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Review

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36 pages, 1992 KiB  
Review
Operational Modal Analysis on Bridges: A Comprehensive Review
by Hamed Hasani and Francesco Freddi
Infrastructures 2023, 8(12), 172; https://doi.org/10.3390/infrastructures8120172 - 4 Dec 2023
Cited by 2 | Viewed by 3031
Abstract
Structural health monitoring systems have been employed throughout history to assess the structural responses of bridges to both natural and man-made hazards. Continuous monitoring of the integrity and analysis of the dynamic characteristics of bridges offers a solution to the limitations of visual [...] Read more.
Structural health monitoring systems have been employed throughout history to assess the structural responses of bridges to both natural and man-made hazards. Continuous monitoring of the integrity and analysis of the dynamic characteristics of bridges offers a solution to the limitations of visual inspection approaches and is of paramount importance for ensuring long-term safety. This review article provides a thorough, straightforward examination of the complete process for performing operational modal analysis on bridges, covering everything from data collection and preprocessing to the application of numerous modal identification techniques in both the time and frequency domains. It also incorporates advanced methods to address and overcome challenges encountered in previous approaches. The paper is distinguished by its thorough examination of various methodologies, highlighting their specific advantages and disadvantages, and providing concrete illustrations of their implementation in practical settings. Full article
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