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Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 23426

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Special Issue Editors

Prof. Dr. David Ayala-Cabrera

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Guest Editor

School of Civil Engineering, University College Dublin, Belfield, Dublin 4, Ireland
Interests: GPR; NDT; health assessment of critical infrastructure; buried asset assessment; intelligent data analysis; machine learning methods; multi-agent systems; artificial intelligence and data mining; complex system analysis; water distribution systems; resilience.

Dr. Mezgeen Rasol

E-Mail Website
Guest Editor

The Urban and Civil Engineering Testing and Modeling Laboratory (EMGCU), Department of Materials and Structures (MAST), Université Gustave Eiffe, 14-20 Boulevard Newton, Champs-sur-Marne, 77447 Marne-la-Vallée Cedex 2, Bâtiment, Paris, France
Interests: ground penetrating radar; NDT applied to structural damage assessment; structural health monitoring; transport infrastructure inspection; masonry structures; seismic risk assessment; civil engineering; numerical modelling and data analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Fabio Tosti

E-Mail Website
Guest Editor

1. School of Computing and Engineering, University of West London, Room BY.03.19, St. Mary’s Rd., Ealing, London W5 5RF, UK
2. The Faringdon Centre for Non-Destructive Testing and Remote Sensing, University of West London, Room BY.GF.015, St. Mary’s Rd., Ealing, London W5 5RF, UK
Interests: ground-penetrating radar; signal processing; remote sensing; deflection-based methods; numerical simulations; forestry engineering; airfield and highway pavement engineering; construction materials; civil engineering
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Franziska Schmidt

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Guest Editor

Laboratoire Expérimentation et Modélisation du Génie Civil Urbain (EMGCU), Université Gustave Eiffel, 14-20 Boulevard Newton - Champs-sur-Marne - 77447 Marne-la-Vallée Cedex 2 - Bâtiment, Bienvenüe, Paris, France
Interests: structural health monitoring; NDT techniques for concrete; scour; assessment of impact of traffic loads; scour resilience

Special Issue Information

Dear Colleagues,

The integration of nondestructive testing (NDT) techniques with intelligent data analysis such as machine learning and artificial intelligence has become crucial to achieve an efficient interpretability of the vast amount of information collected today, both on site and remotely. To this end, advances in the interpretability of NDT data can have a clear impact on aspects such as the development of semi-autonomous/autonomous evaluations of target features and the implementation of characterization analysis tools (e.g., multiagent systems, data clustering approaches) for infrastructure assessment. Progress in these areas of research can ultimately be reflected in the provision of support to expert and less experienced operators in the interpretation of data for infrastructure health monitoring purposes.

In this context, the development of new algorithms and paradigms for the advanced analysis of NDT data is crucial to provide asset owners with more informative decisions based on the actual level of damage occurring on infrastructures. Their resilience to potential unfavorable conditions can therefore be increased and, in general, adequately supported by methodologies based on actual performance analysis. However, a gap in knowledge in terms of exploiting the informative content of NDT measurements at full capacity and making this information more user-friendly and interpretable to end-users has been observed.

The purpose of this Special Issue titled “Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges” therefore aims to collect state-of-the-art material in the remote sensing area for the nondestructive assessment of infrastructures, including transport infrastructures (highways, railways and airfields), buildings, pipes, and soil foundations. A special focus will be on collecting contributions related to the intelligent data analysis of complex systems integrated with NDT techniques. Experimental, numerical, and theoretical research involving the construction, quality control, repair, and maintenance of infrastructures will be considered.

Topics of interest include (but are not limited to) the following:

Nondestructive assessment of infrastructures (transport infrastructures, buildings, pipes, and soil foundations);
NDTs and ML/Artificial Intelligence integration to increase information interpretability;
Intelligent data analysis of complex systems;
Advanced data processing of NDT datasets;
Machine-learning-based models for NDTs;
Physical-based modeling approaches (e.g., artificial neural networks);
Building information modeling (BIM);
Innovative techniques for data collection, treatment, and storage for the application of machine learning analyses.

Review papers in the above-outlined research areas, methodologies, and applications for the integration of NDTs and machine-learning-based approaches will also be considered.

Prof. Dr. David Ayala-Cabrera
Dr. Mezgeen Rasol
Prof. Dr. Fabio Tosti
Prof. Dr. Franziska Schmidt
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

Nondestructive testing
Remote sensing techniques
Sensors
Radar-based techniques
Infrastructure assessment
Infrastructure resilience enhancement
Intelligent data analysis
Advances in NDT dataset interpretability

Published Papers (10 papers)

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Research

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28 pages, 15541 KiB

Open AccessArticle

Near-Surface Soil Moisture Characterization in Mississippi’s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images

by Rakesh Salunke, Masoud Nobahar, Omer Emad Alzeghoul, Sadik Khan, Ian La Cour and Farshad Amini

Remote Sens. 2023, 15(7), 1888; https://doi.org/10.3390/rs15071888 - 31 Mar 2023

Cited by 4 | Viewed by 1332

Abstract

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R² values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R² = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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19 pages, 2332 KiB

Open AccessArticle

Parametric Study to Evaluate the Geometry and Coupling Effect on the Efficiency of a Novel FMM Tool Embedded in Cover Concrete for Corrosion Monitoring

by Sima Kadkhodazadeh, Amine Ihamouten, David Souriou, Xavier Dérobert and David Guilbert

Remote Sens. 2022, 14(21), 5593; https://doi.org/10.3390/rs14215593 - 06 Nov 2022

Viewed by 1140

Abstract

Rebar corrosion represents a major threat to the durability of reinforced concrete structures, primarily in marine environments. Various Non-Destructive Evaluations (NDE) have been developed to detect rebar corrosion; although most of these have delivered successful results, a lack of reliable techniques for proper corrosion prognosis still remains. Under the French Research Agency (ANR) project’s “LabCom OHMIGOD” framework, we introduce here a novel embedded tool to evaluate the level of cover concrete contamination from aggressive agents responsible for causing corrosion. This tool is divided into two parts: a reactive part exposed to corrosion, and a permanent part protected against corrosion. Using magnetic materials in both parts entails “Functional Magnetic Materials” (FMM) and generates a Magnetic Observable (MO). Through the evolution of corrosion on the reactive part, its magnetic properties become affected, which in turn modifies the MO. By means of regular monitoring of MO variations, it is possible to evaluate the aggressive agent ingress. Consequently, by using a variety of FMM tools placed at different concrete depths, it is possible to indirectly evaluate the rebar corrosion risk. This paper presents a numerical model of the tool employing Ansys software. The underlying objective is to investigate tool accuracy through its key parameters, namely, geometry, relative distance to the receiver, coupling effect, and border effect from the rebar. Simulation results demonstrate that by choosing an efficient geometry for the reactive part (25 mm × 25 mm × 1 mm) and position for the tool (between 1 and 3 mm), both a sufficient MO variation range and a negligible coupling effect can be obtained when the FMM is more than 5 cm from any ferromagnetic material. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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26 pages, 8797 KiB

Open AccessArticle

Multi-Sensor Fusion Based Estimation of Tire-Road Peak Adhesion Coefficient Considering Model Uncertainty

by Cheng Tian, Bo Leng, Xinchen Hou, Lu Xiong and Chao Huang

Remote Sens. 2022, 14(21), 5583; https://doi.org/10.3390/rs14215583 - 05 Nov 2022

Cited by 7 | Viewed by 1596

Abstract

The tire-road peak adhesion coefficient (TRPAC), which cannot be directly measured by on-board sensors, is essential to road traffic safety. Reliable TRPAC estimation can not only serve the vehicle active safety system, but also benefit the safety of other traffic participants. In this paper, a TRPAC fusion estimation method considering model uncertainty is proposed. Based on virtual sensing theory, an image-based fusion estimator considering the uncertainty of the deep-learning model and the kinematic model is designed to realize the accurate classification of the road surface condition on which the vehicle will travel in the future. Then, a dynamics-image-based fusion estimator considering the uncertainty of visual information is proposed based on gain scheduling theory. The results of simulation and real vehicle experiments show that the proposed fusion estimation method can make full use of multisource sensor information, and has significant advantages in estimation accuracy, convergence speed and estimation robustness compared with other single-source-based estimators. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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21 pages, 5597 KiB

Open AccessArticle

Experimental Parametric Study of a Functional-Magnetic Material Designed for the Monitoring of Corrosion in Reinforced Concrete Structures

by David Souriou, Sima Kadkhodazadeh, Xavier Dérobert, David Guilbert and Amine Ihamouten

Remote Sens. 2022, 14(15), 3623; https://doi.org/10.3390/rs14153623 - 28 Jul 2022

Cited by 1 | Viewed by 1265

Abstract

The presence of aggressive agents (such as chloride ions brought by seawater) in reinforced concrete structures is responsible for the corrosion of the steel rebars. A Structural Health Monitoring technology is developed as a new passive preventive method that would allow for the detection of and for the ability to follow the presence of chloride ions in the cover concrete of reinforced concrete. This technology, referenced as Functional Magnetic Material (FMM), consists on the measurement with an external interrogator of a Magnetic Observable (MO), partially shielded by a patch and corrodible by chloride ions. This paper presents the results of a parametric experimental study, allowing the validation of the concept of this technology, by highlighting the variation of the MO while considering the geometry and the corrosion level of the patch (based on its Relative Mass Loss—RML), as well as the distance between the samples and the interrogator. The results show that the MO of the FMM significantly varies with the increase in the RML of the patch. A 10%-RML for the patch is sufficient for detecting a variation of the MO of the FMM, and the relative variations of the MO are strongly dependent on the distance between the FMM and the magnetometer, as well as the patch’s thickness. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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12 pages, 1799 KiB

Open AccessArticle

Machine Learning-Based Concrete Crack Depth Prediction Using Thermal Images Taken under Daylight Conditions

by Min Jae Park, Jihyung Kim, Sanggi Jeong, Arum Jang, Jaehoon Bae and Young K. Ju

Remote Sens. 2022, 14(9), 2151; https://doi.org/10.3390/rs14092151 - 30 Apr 2022

Cited by 9 | Viewed by 2797

Abstract

Concrete cracks can threaten the usability of structures and degrade the aesthetics of buildings. Furthermore, minor cracks can develop into large-scale cracks that may lead to structural failure when exposed to excessive external loads. In addition, the concrete crack width and depth should be precisely measured to investigate the effects of concrete cracks on the stability of structures. Thus, a nondestructive and noncontact testing method was introduced for detecting concrete crack depth using thermal images and machine learning. The thermal images of the cracked specimens were obtained using a constant test setup for several months under daylight conditions, which provided sufficient heat for measuring the temperature distributions of the specimens, with recording parameters such as air temperature, humidity, and illuminance. From the thermal images, the crack and surface temperatures were obtained depending on the crack widths and depths using the parameters. Four machine-learning algorithms (decision tree, extremely randomized tree, gradient boosting, and AdaBoost) were selected, and the results of crack depth prediction were compared to identify the best algorithm. In addition, data bias analysis using principal component analysis, singular value decomposition, and independent component analysis were conducted to evaluate the efficiency of machine learning. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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17 pages, 706 KiB

Open AccessArticle

A Preliminary Numerical Study to Compare the Physical Method and Machine Learning Methods Applied to GPR Data for Underground Utility Network Characterization

by Rakeeb Mohamed Jaufer, Amine Ihamouten, Yann Goyat, Shreedhar Savant Todkar, David Guilbert, Ali Assaf and Xavier Dérobert

Remote Sens. 2022, 14(4), 1047; https://doi.org/10.3390/rs14041047 - 21 Feb 2022

Cited by 8 | Viewed by 2668

Abstract

In the field of geophysics and civil engineering applications, ground penetrating radar (GPR) technology has become one of the emerging non-destructive testing (NDT) methods thanks to its ability to perform tests without damaging structures. However, NDT applications, such as concrete rebar assessments, utility network surveys or the precise localization of embedded cylindrical pipes still remain challenging. The inversion of geometric parameters, such as depth and radius of embedded cylindrical pipes, as well as the dielectric parameters of its surrounding material, is of great importance for preventive measures and quality control. Furthermore, the precise localization is mandatory for critical underground utility networks, such as gas, power and water lines. In this context, innovative signal processing techniques associated with GPR are capable of performing physical and geometric characterization tasks. This paper evaluates the performance of a supervised machine learning and ray-based methods on GPR data. Support vector machines (SVM) classification, support vector machine regression (SVR) and ray-based methods are all used to correlate information about the radius and depth of embedded pipes with the velocity of stratified media in various numerical configurations. The approach is based on the hyperbola trace emerging in a set of B-scans, given that the shape of the hyperbola varies greatly with pipe depth and radius as well as with velocity of the medium. According to the ray-based method, an inversion of the wave velocity and pipe radius is performed by applying an appropriate nonlinear least mean squares inversion technique. Feature selection within machine learning models is also implemented on the information chosen from observed hyperbola travel times. Simulated data are obtained by means of the finite-difference time-domain (FDTD) method with the 2D numerical tool GprMax. The study is carried out on mono-static, ground-coupled GPR datasets. The preliminary study showed that the proposed machine learning methods outperforms the ray-based method for estimating radius, depth and velocity. SVR, for instance, calculates depth and radius values with mean absolute relative errors of 0.39% and 6.3%, respectively, with regard to the ground truth. A parametric comparison of the aforementioned methodologies is also included in the performance analysis in terms of relative error. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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16 pages, 2874 KiB

Open AccessArticle

Orthogonal Set of Indicators for the Assessment of Flexible Pavement Stiffness from Deflection Monitoring: Theoretical Formalism and Numerical Study

by Jean-Michel Simonin, Jean-Michel Piau, Vinciane Le-Boursicault and Murilo Freitas

Remote Sens. 2022, 14(3), 500; https://doi.org/10.3390/rs14030500 - 22 Jan 2022

Cited by 1 | Viewed by 1356

Abstract

The monitoring of pavements along roads is generally based on the use of indicators directly derived from measurements. More specifically, the bearing capacity of pavements is often simply deduced from either the maximum deflection value measured or the difference between two measured values along the deflection basin. This paper proposes a methodology to define a set of orthogonal indicators adapted to the structure being evaluated. This methodology is presented for deflection measurements recorded on a flexible pavement simulated by the Burmister model and consists of searching the weighting functions to calculate different indicators as linear forms of the deflection bowl. Weighting functions are defined for each indicator in order to maximize its sensitivity to a given structural parameter without being sensitive to the other structural parameters. The paper presents the various steps involved in constructing the indicators. A numerical example of an application shows that variations of each indicator follow the Young’s modulus variations specific to this indicator. Several extensions of this method are also introduced for other mechanical models or instrumented pavements. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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17 pages, 5860 KiB

Open AccessArticle

Hyperspectral Remote Sensing of TiO₂ Concentration in Cementitious Material Based on Machine Learning Approaches

by Tae-Min Oh, Seungil Baek, Tae-Hyun Kong, Sooyoon Koh, Jaehun Ahn and Wonkook Kim

Remote Sens. 2022, 14(1), 189; https://doi.org/10.3390/rs14010189 - 01 Jan 2022

Viewed by 1678

Abstract

Titanium dioxide (TiO₂) is a photocatalyst that can be used to remove nitrogen oxide (NO_x). When applied to cementitious materials, it reacts with photons in sunlight or artificially generated light to reduce the concentration of particulate matter in the atmosphere. The concentration of TiO₂ applied to the cementitious surface is difficult to quantify in a non-destructive manner after its application; however, knowledge of this residual amount is important for inspection and the evaluation of life expectancy. This study proposes a remote sensing technique that can estimate the concentration of TiO₂ in the cementitious surface using a hyperspectral sensor. In the experiment, cement cores of varying TiO₂ concentration and carbon contents were prepared and the surfaces were observed by TriOS RAMSES, a directional hyperspectral sensor. Machine-learning-based algorithms were then trained to estimate the TiO₂ concentration under varying base material conditions. The results revealed that the best-performing algorithms produced TiO₂ concentration estimates with a ~6% RMSE and a correlation close to 0.8. This study presents a robust machine learning model to estimate TiO₂ and activated carbon concentration with high accuracy, which can be applied to abrasion monitoring of TiO₂ and activated carbon in concrete structures. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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16 pages, 16063 KiB

Open AccessArticle

Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles

by Mohammad Aldibaja and Naoki Suganuma

Remote Sens. 2021, 13(24), 5066; https://doi.org/10.3390/rs13245066 - 14 Dec 2021

Cited by 6 | Viewed by 3349

Abstract

This paper proposes a unique Graph SLAM framework to generate precise 2.5D LIDAR maps in an XYZ plane. A node strategy was invented to divide the road into a set of nodes. The LIDAR point clouds are smoothly accumulated in intensity and elevation images in each node. The optimization process is decomposed into applying Graph SLAM on nodes’ intensity images for eliminating the ghosting effects of the road surface in the XY plane. This step ensures true loop-closure events between nodes and precise common area estimations in the real world. Accordingly, another Graph SLAM framework was designed to bring the nodes’ elevation images into the same Z-level by making the altitudinal errors in the common areas as small as possible. A robust cost function is detailed to properly constitute the relationships between nodes and generate the map in the Absolute Coordinate System. The framework is tested against an accurate GNSS/INS-RTK system in a very challenging environment of high buildings, dense trees and longitudinal railway bridges. The experimental results verified the robustness, reliability and efficiency of the proposed framework to generate accurate 2.5D maps with eliminating the relative and global position errors in XY and Z planes. Therefore, the generated maps significantly contribute to increasing the safety of autonomous driving regardless of the road structures and environmental factors. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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Review

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26 pages, 3031 KiB

Open AccessReview

Progress and Monitoring Opportunities of Skid Resistance in Road Transport: A Critical Review and Road Sensors

by Mezgeen Rasol, Franziska Schmidt, Silvia Ientile, Lucas Adelaide, Boumediene Nedjar, Malal Kane and Christophe Chevalier

Remote Sens. 2021, 13(18), 3729; https://doi.org/10.3390/rs13183729 - 17 Sep 2021

Cited by 23 | Viewed by 4214

Abstract

Skid resistance is a significant feature that provides consistent traffic safety management for road pavements. An appropriate level of Skid resistance describes the contribution that the pavement surface makes to tire/road friction, and the surface of the road pavement can reduce vehicle operation cost, traffic accidents, and fatalities, particularly in wet conditions. Wet conditions decrease the level of the skid resistance (pavement friction), and this may lead to serious struggles related to driving on the road pavement (e.g., skidding or hydroplaning), which contributes to higher crash rates. The knowledge of skid resistance is essential to ensure reliable traffic management in transportation systems. Thus, a suitable methodology of skid resistance measurement and the understanding of the characterization of the road pavement are key to allow safe driving conditions. This paper presents a critical review on the current state of the art of the research conducted on skid resistance measurement techniques, taking into account field-based and laboratory-based methodologies, and novel road sensors with regard to various practices of skid resistance, factors influencing the skid resistance, the concept of the minimum skid resistance and thresholds. In conclusion, new trends that are relevant to data collection approaches and innovative procedures to further describe the data treatment are discussed to achieve better understanding, more accurate data interoperability, and proper measurement of skid resistance. Full article

(This article belongs to the Special Issue Remote Sensing for Infrastructure Assessment Using NDTs and Intelligent Data Analysis: New Trends and Challenges)

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