Enhancing Road Infrastructure Monitoring: Integrating Drones for Weather-Aware Pothole Detection

The abstract outlines the research proposal focused on the utilization of Unmanned Aerial Vehicles (UAVs) for monitoring potholes in road infrastructure affected by various weather conditions. The study aims to investigate how different materials used to fill potholes, such as water, grass, sand, and snow-ice, are impacted by seasonal weather changes, ultimately affecting the performance of pavement structures. By integrating weather-aware monitoring techniques, the research seeks to enhance the rigidity and resilience of road surfaces, thereby contributing to more effective pavement management systems. The proposed methodology involves UAV image-based monitoring combined with advanced super-resolution algorithms to improve image refinement, particularly at high flight altitudes. Through case studies and experimental analysis, the study aims to assess the geometric precision of 3D models generated from aerial images, with a specific focus on road pavement distress monitoring. Overall, the research aims to address the challenges of traditional road failure detection methods by exploring cost-effective 3D detection techniques using UAV technology, thereby ensuring safer roadways for all users

Pavement Surface Distress Detection, Assessment, and Modeling Using Geospatial Techniques

Roadway pavement surface distress information is essential for effective pavement asset management, and subsequently, transportation agencies at all levels dedicate a large amount of time and money to routinely collect data on pavement surface distress conditions as the core of their asset management programs. These data are used by these agencies to make maintenance and repair decisions. Current methods for pavement surface distress evaluation are time-consuming and expensive. Geospatial technologies provide new methods for evaluating pavement surface distress condition that can supplement or substitute for currently-adopted evaluation methods. However, few previous studies have explored the utility of geospatial technologies for pavement surface distress evaluation. The primary scope of this research is to evaluate the potential of three geospatial techniques to improve the efficiency of pavement surface distress evaluation, including empirical analysis of high-spatial resolution natural color digital aerial photography (HiSR-DAP), empirical analysis of hyper-spatial resolution natural color digital aerial photography (HySR-DAP), and inferential geospatial modeling based on traffic volume, environmental conditions, and topographic factors. Pavement surface distress rates estimated from the aforementioned geospatial technologies are validated against distress data manually collected using standard protocols. Research results reveal that straightforward analysis of the spectral response extracted from HiSR-DAP can permit assessment of overall pavement surface conditions. In addition, HySR-DAP acquired from S-UAS can provide accurate and reliable information to characterize detailed pavement surface distress conditions. Research results also show that overall pavement surface distress condition can be effectively estimated based on the extent of geospatial data and inferential modeling techniques. In the near term, these proposed methods could be used to rapidly and cost-effectively evaluate pavement surface distress condition for roadway sections where field inspectors or survey vehicles cannot gain access. In the long term, these proposed methods are capable of being automated to routinely evaluate pavement surface distress condition and, ultimately, to provide a cost-effective, rapid, and safer alternative to currently-adopted evaluation methods with substantially reduced sampling density

A Robotized Raspberry-Based System for Pothole 3D Reconstruction and Mapping

Repairing potholes is a task for municipalities to prevent serious road user injuries and vehicle damage. This study presents a low-cost, high-performance pothole monitoring system to maintain urban roads. The authors developed a methodology based on photogrammetry techniques to predict the pothole's shape and volume. A collection of overlapping 2D images shot by a Raspberry Pi Camera Module 3 connected to a Raspberry Pi 4 Model B has been used to create a pothole 3D model. The Raspberry-based configuration has been mounted on an autonomous and remote-controlled robot (developed in the InfraROB European project) to reduce workers' exposure to live traffic in survey activities and automate the process. The outputs of photogrammetry processing software have been validated through laboratory tests set as ground truth; the trial has been conducted on a tile made of asphalt mixture, reproducing a real pothole. Global Positioning System (GPS) and Geographical Information System (GIS) technologies allowed visualising potholes on a map with information about their centre, volume, backfill material, and an associated image. Ten on-site tests validated that the system works in an uncontrolled environment and not only in the laboratory. The results showed that the system is a valuable tool for monitoring road potholes taking into account construction workers' and road users' health and safety

Evaluación de daños en pavimento flexible usando fotogrametría terrestre y redes neuronales

In Colombia, road deterioration is assessed by means of road inventories and visual inspections. For this assessment, the Instituto Nacional de Vías (Colombia's National Road Institute) (abbreviated INVIAS in Spanish) uses the Vision Inspection de Zones et Itinéraires Á Risque (VIZIR) and Pavement Index Condition (PCI) methods. These two methods serve to determine the severity of damages in flexible and rigid pavements. However, they can be tedious and subjective and require an experienced evaluator, hence the need to develop new methods for road condition assessment. In this paper, we present a methodology to evaluate flexible pavement deterioration using terrestrial photogrammetry techniques and neural networks. The proposed methodology consists of six stages: (i) image capture, (ii) image preprocessing, (iii) segmentation via edge detection techniques, (iv) characteristic extraction, (v) classification using neural networks, and (vi) assessment of deteriorated areas. It is verified using real images of three different pavement distresses: longitudinal cracking, crocodile cracking, and pothole. As classifier, we use a multilayer neural network with a (12 12 3) configuration and trained using the Levenberg–Marquardt algorithm for backpropagation. The results show a classifier’s accuracy of 96 %, a sensitivity of 93.33 %, and a Cohen's Kappa coefficient of 93.67 %. Thus, our proposed methodology could pave the way for the development of an automated system to assess road deterioration, which may, in turn, reduce time and costs when designing road infrastructure maintenance plans.La evaluación del deterioro de las vías en Colombia se realiza por medio de inventarios manuales e inspecciones visuales. Los métodos de evaluación del estado de las vías adoptados por el INVIAS (Instituto Nacional de Vías) son VIZIR (Visión Inspection de Zones et Itinéraires Á Risque) y PCI (Paviment Condition Index). Estos determinan la gravedad de daño en pavimento flexible y rígido; sin embargo, pueden ser tediosos, subjetivos y requieren de la experiencia de un evaluador, lo que evidencia la necesidad de desarrollar metodologías de evaluación del estado de las vías. Este documento presenta una metodología para la evaluación de los deterioros presentes en pavimento flexible usando técnicas de fotogrametría terrestre y redes neuronales que está compuesta por seis etapas: i. Captura de las imágenes, ii. Preprocesamiento de las imágenes, iii. Segmentación mediante técnicas de detección de bordes, iv. Extracción de las características, v. Clasificación utilizando redes neuronales, y vi. Evaluación del área de afectación del deterioro. La metodología se evaluó con imágenes reales de pavimento con tres tipos de deterioro: grieta longitudinal, piel de cocodrilo y bache. Como clasificador se utilizó una red neuronal multicapa con configuración (12 12 3), entrenada utilizando el algoritmo Levenberg Marquardt de retropropagación. Se obtuvo una exactitud del 96 % en el clasificador, una sensibilidad de 93.33 % y una índice kappa de 0.936. Esta metodología es la base para la creación de un sistema automatizado de evaluación del deterioro presente en las vías, el cual puede contribuir en la reducción en tiempo y costo en los planes de gestión de mantenimiento de la infraestructura vial

Road conditional mapping using terrestrial laser scanning method

Road transportation plays a vigorous part in the lives of people worldwide, because it bond people for commercial activities or pleasure by connecting small and large cities, urban and rural areas as well as connecting a country with its neighbour. To support the safe movement of people, goods and services, road and their features are carefully designed and constructed to increase road traffic safety, improve the efficient use of the overall network and reduce the harm such as death, injuries and property damage. Crack is the common surface distress of asphalt pavements it is necessary to detect the crack as early as possible to reduce maintenance cost. Terrestrial laser scanning is one of the most capable remote sensing techniques, which can be used to detect and analyse road distress at all levels The main objectives of this research were to acquire the road data using terrestrial laser scanning and close-range photogrammetry method, measure the width, length and area affected by the crack from point cloud data and also to verify the result using close-range photogrammetry and manual method. Ten lengths of the crack ware measured, ten width and area affected by the crack was also measured from point cloud data. The results obtained from point cloud data was verified using close-range photogrammetry and manual measurements. The results shows the potential of terrestrial laser scanning to detect, measure and analyse the road crack with root mean square error of the measured lengths between terrestrial laser scanning and close-range photogrammetry 0.015m and that of terrestrial laser scanning and manual method was 0.018m while the root mean square error of the measured widths between terrestrial laser scanning and close-range photogrammetry 0.001m and that of terrestrial laser scanning and manual method was 0.001m

Pavement distress detection using terrestrial laser scanning point clouds – Accuracy evaluation and algorithm comparison

In this paper, we compared five crack detection algorithms using terrestrial laser scanner (TLS) point clouds. The methods are developed based on common point cloud processing knowledge in along- and across-track profiles, surface fitting or local pointwise features, with or without machine learning. The crack area and volume were calculated from the crack points detected by the algorithms. The completeness, correctness, and F1 score of each algorithm were computed against manually collected references. Ten 1-m-by-3.5-m plots containing 75 distresses of six distress types (depression, disintegration, pothole, longitudinal, transverse, and alligator cracks) were selected to explain variability of distresses from a 3-km-long-road. For crack detection at plot level, the best algorithm achieved a completeness of up to 0.844, a correctness of up to 0.853, and an F1 score of up to 0.849. The best algorithm’s overall (ten plots combined) completeness, correctness, and F1 score were 0.642, 0.735, and 0.685 respectively. For the crack area estimation, the overall mean absolute percentage errors (MAPE) of the two best algorithms were 19.8% and 20.3%. In the crack volume estimation, the two best algorithms resulted in 19.3% and 14.5% MAPE. When the plots were grouped based on crack detection complexity, in the ‘easy’ category, the best algorithm reached a crack area estimation MAPE of 8.9%, while for crack volume estimation, the MAPE obtained from the best algorithm was 0.7%

Undergraduate engineering and built environment project conference 2018: book of abstracts - Toowoomba, Australia, 24-28 September 2018

Book of Abstracts of the USQ Undergraduate Engineering and Built Environment Conference 2018, held Toowoomba, Australia, 24-28 September 2018. These proceedings include extended abstracts of the verbal presentations that are delivered at the project conference. The work reported at the conference is the research undertaken by students in meeting the requirements of courses ENG4111/ENG4112 Research Project

Quantifying road roughness: multiresolution and near real-time analysis

Road roughness is a key parameter for road construction and for assessing ride quality during the life of paved and unpaved road systems. The quarter-car model (QC model), is a standard mathematical tool for estimating suspension responses and can be used for summative or pointwise analysis of vehicle response to road geometry. In fact, transportation agencies specify roughness requirements as summative values for pavement projects that affect construction practices and contractor pay factors. The International Roughness Index (IRI), a summative statistic of quarter-car suspension response, is widely used to characterize overall roughness profiles of pavement stretches but does not provide sufficient detail about the frequency or spatial distribution of roughness features. This research focuses on two pointwise approaches, continuous roughness maps and wavelets analysis, that both characterize overall roughness and identify localized features and compares these findings with IRI results. Automated algorithms were developed to preform finite difference analysis of point cloud data collected by three-dimensional (3D) stationary terrestrial laser scans of paved and unpaved roads. This resulted in continuous roughness maps that characterized both spatial roughness and localized features. However, to address the computational limitations of finite difference analysis, Fourier and wavelets (discrete and continuous wavelet transform) analyses were conducted on sample profiles from the federal highway administration (FHWA) Long Term Pavement Performance data base. The Fourier analysis was performed by transforming profiles into frequency domain and applying the QC filter to the transformed profile. The filtered profiles are transformed back to spatial domain to inspect the location of high amplitudes in the suspension rate profiles. Finite difference analysis provides suspension responses in spatial domain, on the other hand Fourier analysis can be performed in either frequency or spatial domains only. To describe the location and frequency content of localized features in a profile, wavelet filters were customized to separate the suspension response profiles into sub profiles with known frequency bands. Other advantages of wavelets analysis includes data compression, making inferences from compressed data, and analyzing short profiles (\u3c 7.6 m). The proposed approaches present the basis for developing real-time autonomous algorithms for smoothness based quality control and maintenance