Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

Impact of Embedded Carbon Fiber Heating Panel on the Structural/Mechanical Performance of Roadway Pavement

INE/AUTC 12.3

Road pavement crack automatic detection by MMS images

none4noThe research topic was to test different feature extraction methods to localize road pavement cracks useful to construct a spatial database for the pavement distress monitoring. Several images were acquired by means of a line scan camera that assembled in a Mobile Mapping System (MMS) allows tracking directly the position of the images by a GPS-INS system. Following an automatic digital image processing was performed by means of several algorithms based on different approaches (edge detection and fuzzy set theory). The detected cracks were described with some parameters in relation to some shape characteristics (dimension, typology, direction), which are necessary to recognize the gravity of the road pavement conditions. The edge detection techniques tested in this research allowed identifying fatigue cracking or alligator cracking and also thin linear cracks in images with strong radiometric jumps by applying filters, gradient functions and morphological operators. The snake approach was one of them, in particular the type called Gradient Vector Flow (GVF). Another approach was based on the fuzzy theory. The advantage of this method is that the pixels, necessary to identify the cracks in road pavement, are darker than their surroundings in an image. The last stage was the pavement distress spatial database collection. The Mobile Mapping System (MMS) has allowed localizing the raster data and consequently the vector features of the detected cracks, associating into the table their attributes too. The proposed approaches allow to automatically localize and classify the kind of road pavement crack.Automatic Detection, Feature extraction methods, Gradient function, Gradient vector flow, Line-scan cameras, Mobile mapping systems, Morphological operator, Shape characteristicsA. Mancini;E. S. Malinverni;E. Frontoni;P. ZingarettiMancini, Adriano; Malinverni, Eva Savina; Frontoni, Emanuele; Zingaretti, Prim

Bi-objective modeling approach for repairing multiple feature infrastructure systems

A bi-objective decision aid model for planning long-term maintenance of infrastructure systems is presented, oriented to interventions on their constituent elements, with two upgrade levels possible for each element (partial/full repairs). The model aims at maximizing benefits and minimizing costs, and its novelty is taking into consideration, and combining, the system/element structure, volume discounts, and socioeconomic factors. The model is tested with field data from 229 sidewalks (systems) and compared to two simpler repair policies, of allowing only partial or full repairs. Results show that the efficiency gains are greater in the lower mid-range budget region. The proposed modeling approach is an innovative tool to optimize cost/benefits for the various repair options and analyze the respective trade-offs.info:eu-repo/semantics/publishedVersio

Deep Learning Approaches in Pavement Distress Identification: A Review

This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical aspect in modern pavement management systems. The conventional manual inspection process conducted by human experts is gradually being superseded by automated solutions, leveraging machine learning and deep learning algorithms to enhance efficiency and accuracy. The ability of these algorithms to discern patterns and make predictions based on extensive datasets has revolutionized the domain of pavement distress identification. The paper investigates the integration of unmanned aerial vehicles (UAVs) for data collection, offering unique advantages such as aerial perspectives and efficient coverage of large areas. By capturing high-resolution images, UAVs provide valuable data that can be processed using deep learning algorithms to detect and classify various pavement distresses effectively. While the primary focus is on 2D image processing, the paper also acknowledges the challenges associated with 3D images, such as sensor limitations and computational requirements. Understanding these challenges is crucial for further advancements in the field. The findings of this review significantly contribute to the evolution of pavement distress detection, fostering the development of efficient pavement management systems. As automated approaches continue to mature, the implementation of deep learning techniques holds great promise in ensuring safer and more durable road infrastructure for the benefit of society

Analytical study of computer vision-based pavement crack quantification using machine learning techniques

Image-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack grouping, and crack detection. Bottom-hat morphological technique was used to remove the random background of pavement images and extract cracks, selectively based on their shapes, sizes, and intensities using a relatively small number of user-defined parameters. A technical challenge with crack extraction algorithms, including the Bottom-hat transform, is that extracted crack pixels are usually fragmented along crack paths. For de-fragmenting those crack pixels, a novel crack-grouping algorithm is proposed as an image segmentation method, so called MorphLink-C. Statistical validation of this method using flexible pavement images indicated that MorphLink-C not only improves crack-detection accuracy but also reduces crack detection time. Crack characterization was performed by analysing imagerial features of the extracted crack image components. A comprehensive statistical analysis was conducted using filter feature subset selection (FSS) methods, including Fischer score, Gini index, information gain, ReliefF, mRmR, and FCBF to understand the statistical characteristics of cracks in different deterioration stages. Statistical significance of crack features was ranked based on their relevancy and redundancy. The statistical method used in this study can be employed to avoid subjective crack rating based on human visual inspection. Moreover, the statistical information can be used as fundamental data to justify rehabilitation policies in pavement maintenance. Finally, the application of four classification algorithms, including Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability of results for highly imbalanced datasets in which, the number of crack objects are significantly smaller than the number of non-crack objects, 4) stability of the classifiers performance for pavements in different deterioration stages, and 5) interpretability of results and clarity of the procedure. Comparison results indicate the advantages of white-box classification methods for computer vision based pavement evaluation. Although black-box methods, such as ANN provide superior classification performance, white-box methods, such as ANFIS, provide useful information about the logic of classification and the effect of feature values on detection results. Such information can provide further insight for the image-based pavement crack detection application

Research trends in pavement management during the first years of the 21st century: a bibliometric analysis during the 2000-2013 period

A bibliometric analysis based on Scopus database was carried out to identify the global research trends related to pavement management area from 2000 to 2013, and to improve the understanding of the research topics in that period. The results reveal two stable periods of annual publications, from 2000 to 2002 with an average rate of 27, and from 2003 to 2010 with a value of 51; and a period with an increasing production rate of 20 publications per year after 2010. According to the document-type distribution, articles and conference papers have almost the same contribution. The most productive country was the United States, followed by Canada and China. The research trend in the field of pavement management could be grouped into three main areas. The first one is related to pavement management systems, which attracted the greatest attention, especially optimization processes with various objectives and lifecycle cost analysis. The second group is about pavement performance modeling, where calibration of mechanical empirical models was largely developed. Lastly, data collection had also occupied several papers, mainly about cracking classification. Sustainability aspects in pavement management became an emergent issue. The trending issues in that period, in these categories, were summarized in the paper.This research was funded by Erasmus Mundus Action 2 ACTIVE “Atlantic Caucasus Technical universities Initiative for Valuable Education” programme grant number 2013-2523/001-001 EMA2; by Education Council of the Castille and Leon Government of Spain (Junta de Castilla y León) grant number BU0009A06 and UB 07/03, by Spanish Ministry of Economy and Competitiveness (MINECO) grant number CTQ2014-56820-JIN Project, co-financed by FEDER funds from European Union and by Basque Government grant number IT781-13

Segmentation of highway networks for maintenance operations

Pavement maintenance and rehabilitation (M&R) is important for transportation agencies to have a sustainable transportation infrastructure. In maintenance operations, obtaining limits of homogeneous sections is a key problem because appropriate segmentation can help yield a more cost effective M&R plan. The purpose of this study is to present the result of investigation on various research works and to suggest the direction of developing an enhanced methodological framework. Existing approaches for pavement segmentation was explored through a literature review and data analysis. Autocorrelation tests, change-point approaches, a Bayesian method, and a hidden Markov model were performed using pavement condition data. Future work directions were suggested to develop a segmentation method capable of handling the issues found in the study.Statistic

On the Use of Low-Cost RGB-D Sensors for Autonomous Pothole Detection with Spatial Fuzzy <em>c</em>-Means Segmentation

The automated detection of pavement distress from remote sensing imagery is a promising but challenging task due to the complex structure of pavement surfaces, in addition to the intensity of non-uniformity, and the presence of artifacts and noise. Even though imaging and sensing systems such as high-resolution RGB cameras, stereovision imaging, LiDAR and terrestrial laser scanning can now be combined to collect pavement condition data, the data obtained by these sensors are expensive and require specially equipped vehicles and processing. This hinders the utilization of the potential efficiency and effectiveness of such sensor systems. This chapter presents the potentials of the use of the Kinect v2.0 RGB-D sensor, as a low-cost approach for the efficient and accurate pothole detection on asphalt pavements. By using spatial fuzzy c-means (SFCM) clustering, so as to incorporate the pothole neighborhood spatial information into the membership function for clustering, the RGB data are segmented into pothole and non-pothole objects. The results demonstrate the advantage of complementary processing of low-cost multisensor data, through channeling data streams and linking data processing according to the merits of the individual sensors, for autonomous cost-effective assessment of road-surface conditions using remote sensing technology