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5 pages, 171 KiB

Open AccessEditorial

Sensing Advancement and Health Monitoring of Transport Structures

by Andrea Benedetto, Imad L. Al-Qadi, Amir M. Alani, Andreas Loizos and Fabio Tosti

Sensors 2021, 21(22), 7621; https://doi.org/10.3390/s21227621 - 16 Nov 2021

Viewed by 1495

Abstract

Planning, design, construction, maintenance and management of transport infrastructure demand new methods and approaches to optimise utilisation of materials, energy and workforce [...] Full article

(This article belongs to the Special Issue Sensing Advancement and Health Monitoring of Transport Structures)

Research

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28 pages, 8343 KiB

Open AccessArticle

Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis

by Valerio Gagliardi, Luca Bianchini Ciampoli, Sebastiano Trevisani, Fabrizio D’Amico, Amir M. Alani, Andrea Benedetto and Fabio Tosti

Sensors 2021, 21(17), 5769; https://doi.org/10.3390/s21175769 - 27 Aug 2021

Cited by 35 | Viewed by 4707

Abstract

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as [...] Read more.

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as a routine tool for certain critical application areas, such as the assessment of millimetre-scale differential displacements in airport runways, is still debated. This research aims to demonstrate the viability of using medium-resolution Copernicus ESA Sentinel-1A (C-Band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, “Runway n.3” of the “Leonardo Da Vinci International Airport” in Fiumicino, Rome, Italy was investigated as an explanatory case study, in view of historical geotechnical settlements affecting the runway area. In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. The geostatistical analysis provided ample information on the spatial continuity of the Sentinel 1 data in comparison with the high-resolution COSMO-SkyMed data and the ground-based topographic levelling data. Furthermore, a comparison between the PSI outcomes from the Sentinel-1A SAR data—interpolated through Ordinary Kriging—and the ground-truth topographic levelling data demonstrated the high accuracy of the Sentinel 1 data. This is proven by the high values of the correlation coefficient (r = 0.94), the multiple R-squared coefficient (R² = 0.88) and the Slope value (0.96). The results of this study clearly support the effectiveness of using Sentinel-1A SAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways, paving the way for the development of more efficient and sustainable maintenance strategies for inclusion in next generation Airport Pavement Management Systems (APMSs). Full article

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23 pages, 79346 KiB

Open AccessArticle

Multi-Sensors Geophysical Monitoring for Reinforced Concrete Engineering Structures: A Laboratory Test

by Luigi Capozzoli, Giacomo Fornasari, Valeria Giampaolo, Gregory De Martino and Enzo Rizzo

Sensors 2021, 21(16), 5565; https://doi.org/10.3390/s21165565 - 18 Aug 2021

Cited by 2 | Viewed by 2162

Abstract

Non-destructive tests are strongly required in engineering applications for monitoring civil structures. The use of compared and integrated innovative approaches based on geophysical methodologies represents an effective tool for the characterization and monitoring of reinforced concrete (RC) structures. Therefore, the main aim of [...] Read more.

Non-destructive tests are strongly required in engineering applications for monitoring civil structures. The use of compared and integrated innovative approaches based on geophysical methodologies represents an effective tool for the characterization and monitoring of reinforced concrete (RC) structures. Therefore, the main aim of the work was to improve the knowledge on the potentiality and limitations of the Ground Penetrating Radar (GPR) and the Electrical Resistivity Tomography (ERT) with electrodes disposed both on the surface and in the boreholes. The work approach was adopted on an analog model of a reinforced concrete frame built ad hoc at the Hydrogeosite Laboratory (CNR-IMAA), where simulated experiments on full-size physical models are defined. Results show the ability of an accurate use of GPR to reconstruct the rebar dispositions and detect in detail possible constructive defects, both highlighting the lack of reinforcements into the nodes and providing useful information about the safety assessment of the realized structure. The results of the ERT method defined the necessity to develop ad-hoc electrical resistivity methods to support the characterization and monitoring of buried foundation structures for civil engineering applications. Finally, the paper introduces a new approach based on the use of cross-hole ERTs (CHERTs) for the engineering structure monitoring, able to reduce the uncertainties usually affecting the indirect results. Full article

(This article belongs to the Special Issue Sensing Advancement and Health Monitoring of Transport Structures)

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28 pages, 13193 KiB

Open AccessArticle

Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

by Nicholas Fiorentini, Mehdi Maboudi, Pietro Leandri and Massimo Losa

Sensors 2021, 21(10), 3377; https://doi.org/10.3390/s21103377 - 12 May 2021

Cited by 13 | Viewed by 3468

Abstract

This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical [...] Read more.

This paper proposes a methodology for correlating products derived by Synthetic Aperture Radar (SAR) measurements and laser profilometric road roughness surveys. The procedure stems from two previous studies, in which several Machine Learning Algorithms (MLAs) have been calibrated for predicting the average vertical displacement (in terms of mm/year) of road pavements as a result of exogenous phenomena occurrence, such as subsidence. Such algorithms are based on surveys performed with Persistent Scatterer Interferometric SAR (PS-InSAR) over an area of 964 km² in the Tuscany Region, Central Italy. Starting from this basis, in this paper, we propose to integrate the information provided by these MLAs with 10 km of in situ profilometric measurements of the pavement surface roughness and relative calculation of the International Roughness Index (IRI). Accordingly, the aim is to appreciate whether and to what extent there is an association between displacements estimated by MLAs and IRI values. If a dependence exists, we may argue that road regularity is driven by exogenous phenomena and MLAs allow for the replacement of in situ surveys, saving considerable time and money. In this research framework, results reveal that there are several road sections that manifest a clear association among these two methods, while others denote that the relationship is weaker, and in situ activities cannot be bypassed to evaluate the real pavement conditions. We could wrap up that, in these stretches, the road regularity is driven by endogenous factors which MLAs did not integrate during their training. Once additional MLAs conditioned by endogenous factors have been developed (such as traffic flow, the structure of the pavement layers, and material characteristics), practitioners should be able to estimate the quality of pavement over extensive and complex road networks quickly, automatically, and with relatively low costs. Full article

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21 pages, 5928 KiB

Open AccessArticle

Integrating Pavement Sensing Data for Pavement Condition Evaluation

by Konstantinos Gkyrtis, Andreas Loizos and Christina Plati

Sensors 2021, 21(9), 3104; https://doi.org/10.3390/s21093104 - 29 Apr 2021

Cited by 21 | Viewed by 3873

Abstract

Highway pavements are usually monitored in terms of their surface performance assessment, since the major cause that triggers maintenance is reduced pavement serviceability due to surface distresses, excessive pavement unevenness and/or texture loss. A common way to detect pavement surface condition is by [...] Read more.

Highway pavements are usually monitored in terms of their surface performance assessment, since the major cause that triggers maintenance is reduced pavement serviceability due to surface distresses, excessive pavement unevenness and/or texture loss. A common way to detect pavement surface condition is by the use of vehicle-mounted laser sensors that can rapidly scan huge roadway networks at traffic speeds without the need for traffic interventions. However, excessive roughness might sometimes indicate structural issues within one or more pavement layers or even issues within the pavement foundation support. The stand-alone use of laser profilers cannot provide the related agencies with information on what leads to roughness issues. Contrariwise, the integration of multiple non-destructive data leads to a more representative assessment of pavement condition and enables a more rational pavement management and decision-making. This research deals with an integration approach that primarily combines pavement sensing profile and deflectometric data and further evaluates indications of increased pavement roughness. In particular, data including Falling Weight Deflectometer (FWD) and Road Surface Profiler (RSP) measurements are used in conjunction with additional geophysical inspection data from Ground Penetrating Radar (GPR). Based on pavement response modelling, a promising potential is shown that could proactively assist the related agencies in the framework of transport infrastructure health monitoring. Full article

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15 pages, 60979 KiB

Open AccessArticle

Development of a Numerical Model to Predict the Dielectric Properties of Heterogeneous Asphalt Concrete

by Qingqing Cao and Imad L. Al-Qadi

Sensors 2021, 21(8), 2643; https://doi.org/10.3390/s21082643 - 09 Apr 2021

Cited by 16 | Viewed by 2477

Abstract

Ground-penetrating radar (GPR) has been used for asphalt concrete (AC) pavement density prediction for the past two decades. Recently, it has been considered as a method for pavement quality control and quality assurance. A numerical method to estimate asphalt pavement specific gravity from [...] Read more.

Ground-penetrating radar (GPR) has been used for asphalt concrete (AC) pavement density prediction for the past two decades. Recently, it has been considered as a method for pavement quality control and quality assurance. A numerical method to estimate asphalt pavement specific gravity from its dielectric properties was developed and validated. A three-phase numerical model considering aggregate, binder, and air void components was developed using an AC mixture generation algorithm. A take-and-add algorithm was used to generate the uneven air-void distribution in the three-phase model. The proposed three-phase model is capable of correlating pavement density and bulk and component dielectric properties. The model was validated using field data. Two methods were used to calculate the dielectric constant of the AC mixture, including reflection amplitude and two-way travel time methods. These were simulated and compared when vertical and longitudinal heterogeneity existed within the AC pavement layers. Results indicate that the reflection amplitude method is more sensitive to surface thin layers than the two-way travel time methods. Effect of air-void content, asphalt content, aggregate gradation, and aggregate dielectric constants on the GPR measurements were studied using the numerical model. Full article

(This article belongs to the Special Issue Sensing Advancement and Health Monitoring of Transport Structures)

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16 pages, 7467 KiB

Open AccessArticle

Non-Destructive and Quantitative Evaluation of Rebar Corrosion by a Vibro-Doppler Radar Method

by Takashi Miwa

Sensors 2021, 21(7), 2546; https://doi.org/10.3390/s21072546 - 05 Apr 2021

Cited by 11 | Viewed by 3228

Abstract

It is well known that evaluation of rebar corrosion is important for the maintenance of reinforced concrete structures, but, it is difficult to simply, quickly and quantitatively evaluate the amount of corrosion of rebars embedded in concrete by conventional non-destructive evaluation (NDE) methods [...] Read more.

It is well known that evaluation of rebar corrosion is important for the maintenance of reinforced concrete structures, but, it is difficult to simply, quickly and quantitatively evaluate the amount of corrosion of rebars embedded in concrete by conventional non-destructive evaluation (NDE) methods such as electrical, electromagnetic and mechanical method. This paper proposes a vibro-Doppler radar (VDR) measurement method to quantitatively evaluate rebar corrosion by measuring the vibration ability of the rebar forcibly vibrated in concrete by an excitation coil. It is experimentally demonstrated in RC test pieces that the rebar vibration displacement obtained by developed VDR method is valid and is less affected by the moisture in the concrete. In addition, simultaneous monitoring of the rebar vibration displacement of the test pieces is performed through an electrolytic corrosion test and the measured vibration displacement is compared to the rebar corrosion loss evaluated. As the results, it is cleared that the rebar vibration displacement starts to increase from slightly before the occurrences of corrosion crack on the concrete surface as the corrosion loss increases. It is also shown that the rebar vibration displacement becomes 4 times higher than that in initial condition at the rebar corrosion loss of 250 mg/cm². This implies that the VDR has potential to nondestructively and quantitatively evaluate rebar corrosion in concrete. Full article

(This article belongs to the Special Issue Sensing Advancement and Health Monitoring of Transport Structures)

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17 pages, 5640 KiB

Open AccessArticle

Detection of Road-Surface Anomalies Using a Smartphone Camera and Accelerometer

by Taehee Lee, Chanjun Chun and Seung-Ki Ryu

Sensors 2021, 21(2), 561; https://doi.org/10.3390/s21020561 - 14 Jan 2021

Cited by 40 | Viewed by 4581

Abstract

Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable [...] Read more.

Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the acceleration of the vehicle upon their detection was developed to explore the complementarity benefits of the two different methods. A road test was conducted in which 1896 road-surface images and corresponding three-axis acceleration data were acquired. All images were classified based on the presence and type of anomalies, and histograms of the maximum variations in the acceleration in the gravitational direction were comparatively analyzed. When the types of anomalies were not considered, it was difficult to identify their effects using the histograms. The differences among histograms became evident upon consideration of whether the vehicle wheels passed over the anomalies, and when excluding longitudinal anomalies that caused minor changes in acceleration. Although the image-based monitoring system used in this research provided poor performance on its own, the severity of road-surface anomalies was accurately inferred using the specific range of the maximum variation of acceleration in the gravitational direction. Full article

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23 pages, 4822 KiB

Open AccessArticle

BwimNet: A Novel Method for Identifying Moving Vehicles Utilizing a Modified Encoder-Decoder Architecture

by Yuhan Wu, Lu Deng and Wei He

Sensors 2020, 20(24), 7170; https://doi.org/10.3390/s20247170 - 14 Dec 2020

Cited by 9 | Viewed by 2613

Abstract

Traffic loading monitoring plays an important role in bridge structural health monitoring, which is helpful in overloading detection, transportation management, and safety evaluation of transportation infrastructures. Bridge weigh-in-motion (BWIM) is a method that treats traffic loading monitoring as an inverse problem, which identifies [...] Read more.

Traffic loading monitoring plays an important role in bridge structural health monitoring, which is helpful in overloading detection, transportation management, and safety evaluation of transportation infrastructures. Bridge weigh-in-motion (BWIM) is a method that treats traffic loading monitoring as an inverse problem, which identifies the traffic loads of the target bridge by analyzing its dynamic strain responses. To achieve accurate prediction of vehicle loads, the configuration of axles and vehicle velocity must be obtained in advance, which is conventionally acquired via additional axle-detecting sensors. However, problems arise from additional sensors such as fragile stability or expensive maintenance costs, which might plague the implementation of BWIM systems in practice. Although data-driven methods such as neural networks can estimate traffic loadings using only strain sensors, the weight data of vehicles crossing the bridge is difficult to obtain. In order to overcome these limitations, a modified encoder-decoder architecture grafted with signal-reconstruction layer is proposed in this paper to identify the properties of moving vehicles (i.e., velocity, wheelbase, and axle weight) using merely the bridge dynamic response. Encoder-decoder is an unsupervised method extracting higher features from original data. The numerical bridge model based on vehicle-bridge coupling vibration theory is established to illustrate the applicability of this new encoder-decoder method. The identification results demonstrate that the proposed approach can predict traffic loadings without using additional sensors and without requiring vehicle weight labels. Parametric studies also show that this new approach achieves better stability and reliability in identifying the properties of moving vehicles, even under the circumstances of large data pollution. Full article

(This article belongs to the Special Issue Sensing Advancement and Health Monitoring of Transport Structures)

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23 pages, 3171 KiB

Open AccessArticle

An Innovative Approach to Surveying the Geometry of Visibility Triangles at Railway Level Crossings

by Arkadiusz Kampczyk

Sensors 2020, 20(22), 6623; https://doi.org/10.3390/s20226623 - 19 Nov 2020

Cited by 7 | Viewed by 3526

Abstract

Railway level crossings (RLCs) in Poland are classified according to their protection systems. Category D, which is a form of passive RLC, aims to ensure safe and efficient operation. Surveying is essential to prepare and control the geometry of the visibility triangles used [...] Read more.

Railway level crossings (RLCs) in Poland are classified according to their protection systems. Category D, which is a form of passive RLC, aims to ensure safe and efficient operation. Surveying is essential to prepare and control the geometry of the visibility triangles used at RLCs. This article presents a new approach to monitoring the geometry of visibility triangles of RLCs using an electronic total station and a magnetic measuring square (MMS). Its main assumptions are presented together with the application of the innovative measuring instruments. Visibility is demonstrated taking into account the angles of intersection of the road axis with the track axis of the railway line and additional attributes related to the analysis and evaluation of general visibility conditions. The research highlights controversies that have received special attention against the background of the safety status of railway level crossings. As a case study, the RLC located on a single-track railway line in Poland is examined. The final section presents applications of the results obtained according to the proposed methodology. It is shown that the proposed approach is practical and effective. In addition to surveyors, the survey methodology can be used by road and rail traffic engineers and policy makers to further improve traffic safety at RLCs. This is an important global research task. Full article

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24 pages, 3750 KiB

Open AccessArticle

Fracture Behavior of Permeable Asphalt Mixtures with Steel Slag under Low Temperature Based on Acoustic Emission Technique

by Bing Zhu, Hanbing Liu, Wenjun Li, Chunli Wu and Chao Chai

Sensors 2020, 20(18), 5090; https://doi.org/10.3390/s20185090 - 07 Sep 2020

Cited by 16 | Viewed by 2553

Abstract

Acoustic emission (AE), as a nondestructive testing (NDT) and real-time monitoring technique, could characterize the damage evolution and fracture behavior of materials. The primary objective of this paper was to investigate the improvement mechanism of steel slag on the low-temperature fracture behavior of [...] Read more.

Acoustic emission (AE), as a nondestructive testing (NDT) and real-time monitoring technique, could characterize the damage evolution and fracture behavior of materials. The primary objective of this paper was to investigate the improvement mechanism of steel slag on the low-temperature fracture behavior of permeable asphalt mixtures (PAM). Firstly, steel slag coarse aggregates were used to replace basalt coarse aggregates with equal volume at different levels (0%, 25%, 50%, 75%, and 100%). Then, the low-temperature splitting test with slow loading was used to obtain steady crack growth, and the crack initiation and propagation of specimens were monitored by AE technique in real time. From the low-temperature splitting test results, SS-100 (permeable asphalt mixtures with 100% steel slag) has the optimal low-temperature cracking resistance. Therefore, the difference of fracture behavior between the control group (permeable asphalt mixtures without steel slag) and SS-100 was mainly discussed. From the AE test results, a slight bottom-up trend of sentinel function was founded in the 0.6–0.9 displacement level for SS-100, which is different from the control group. Furthermore, the fracture stages of the control group and SS-100 could be divided based on cumulative RA and cumulative AF curves. The incorporation of 100% steel slag reduced the shear events and restrained the growth of shear cracking of the specimen in the macro-crack stage. Finally, the considerable drops of three kinds of b-values in the final phase were found in the control group, but significant repeated fluctuations in SS-100. In short, the fracture behavior of PAM under low temperature was significantly improved after adding 100% steel slag. Full article

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22 pages, 6213 KiB

Open AccessArticle

Estimation for Runway Friction Coefficient Based on Multi-Sensor Information Fusion and Model Correlation

by Yadong Niu, Sixiang Zhang, Guangjun Tian, Huabo Zhu and Wei Zhou

Sensors 2020, 20(14), 3886; https://doi.org/10.3390/s20143886 - 13 Jul 2020

Cited by 13 | Viewed by 6538

Abstract

Friction is a crucial factor affecting air accident occurrence on landing or taking off. Tire–runway friction directly contributes to aircraft stability on land. Therefore, an accurate friction estimation is a rising issue for all stakeholders. This paper summarizes the existing measurement methods, and [...] Read more.

Friction is a crucial factor affecting air accident occurrence on landing or taking off. Tire–runway friction directly contributes to aircraft stability on land. Therefore, an accurate friction estimation is a rising issue for all stakeholders. This paper summarizes the existing measurement methods, and a multi-sensor information fusion scheme is proposed to estimate the friction coefficient between the tire and the runway. Acoustic sensors, optical sensors, tread sensors, and other physical sensors form a sensor system that is used to measure friction-related parameters and fuse them through a neural network. So far, many attempts have been made to link the ground friction coefficient with the aircraft braking friction coefficient. The models that have been developed include the International Runway Friction Index (IRFI), Canada Runway Friction Index (CRFI), and other fitting models. Additionally, this paper attempts to correlate the output of the neural network (estimated friction coefficient) with the correlation model to predict the friction coefficient between the tire and the runway when the aircraft brakes. The sensor system proposed in this paper can be regarded as a mobile weather–runway–tire system, which can estimate the friction coefficient by integrating the runway surface conditions and the tire conditions, and fully consider their common effects. The role of the correlation model is to convert the ground friction coefficient to the grade of the aircraft braking friction coefficient and the information is finally reported to the pilots so that they can make better decisions. Full article

(This article belongs to the Special Issue Sensing Advancement and Health Monitoring of Transport Structures)

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17 pages, 9812 KiB

Open AccessArticle

Experimental Research on Shear Failure Monitoring of Composite Rocks Using Piezoelectric Active Sensing Approach

by Yang Liu, Yicheng Ye, Qihu Wang and Weiqi Wang

Sensors 2020, 20(5), 1376; https://doi.org/10.3390/s20051376 - 03 Mar 2020

Cited by 6 | Viewed by 2799

Abstract

Underground space engineering structures are generally subject to extensive damages and significant deformation. Given that composite rocks are prone to shear failure, which cannot be accurately monitored, the piezoelectric active sensing method and wavelet packet analysis method were employed to conduct a shear [...] Read more.

Underground space engineering structures are generally subject to extensive damages and significant deformation. Given that composite rocks are prone to shear failure, which cannot be accurately monitored, the piezoelectric active sensing method and wavelet packet analysis method were employed to conduct a shear failure monitoring test on composite rocks in this study. For the experiment, specimens were prepared for the simulation of the composite rocks using cement. Two pairs of piezoelectric smart aggregates (SAs) were embedded in the composite specimens. When the specimens were tested using the direct shear apparatus, an active sensing-based monitoring test was conducted using the embedded SAs. Moreover, a wavelet packet analysis was conducted to compute the energy of the monitoring signal; thus allowing for the determination of the shear damage index of the composite specimens and the quantitative characterization of the shear failure process. The results indicated that upon the shear failure of the composite specimens, the amplitudes and peak values of the monitoring signals decreased significantly, and the shear failure and damage indices of the composite specimens increased abruptly and approached a value of 1. The feasibility and reliability of the piezoelectric active sensing method, with respect to the monitoring of the shear failure of composite rocks, was therefore experimentally demonstrated in this study. Full article

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Review

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29 pages, 918 KiB

Open AccessReview

Vehicle-Assisted Techniques for Health Monitoring of Bridges

by Hoofar Shokravi, Hooman Shokravi, Norhisham Bakhary, Mahshid Heidarrezaei, Seyed Saeid Rahimian Koloor and Michal Petrů

Sensors 2020, 20(12), 3460; https://doi.org/10.3390/s20123460 - 19 Jun 2020

Cited by 66 | Viewed by 6197

Abstract

Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic [...] Read more.

Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges. Full article

(This article belongs to the Special Issue Sensing Advancement and Health Monitoring of Transport Structures)

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