Revisión de métodos para la clasificación de fallas superficiales en pavimentos flexibles

The status of the road infrastructure affects the social, economic, and political environment of a nation. Evaluation of the pavement surface condition is essential to plan timely and effective interventions. Timely actions avoid operating cost overruns, prevent uncontrolled deterioration and reduce operational and safety inconveniences. The problem raises the concern of studying alternatives to evaluate the status of pavement, for which a large number of investigations on automatic detection of surface flaws in flexible pavements through image processing techniques have been developed. The objective of this article is to review and analyze these contributions. Based on the review, it was concluded that the performance of this type of systems is determined by two factors: data collection and processing. The analysis presented herein unfolds based on these factors. The development of systems that take advantage of the qualities of different sensors in data acquisition and that integrate the detection and classification of a variety of faults including severity data is considered opportune.El estado de la infraestructura vial impacta el entorno social, económico y político de una nación. La evaluación de la condición superficial del pavimento es esencial para planificar intervenciones oportunas y eficaces. Las acciones oportunas evitan sobrecostos de operación, impiden el deterioro no controlado y disminuyen los inconvenientes operacionales y de seguridad. El problema expuesto plantea la inquietud de estudiar alternativas para evaluar el estado del pavimento, por lo cual un gran número de investigaciones sobre detección automática de fallas superficiales en pavimentos flexibles a través de técnicas de procesamiento de imágenes han sido desarrolladas. El objetivo de este artículo es revisar y analizar estos aportes. Sobre la base de la revisión, se concluyó que el rendimiento de este tipo de sistemas está determinado por dos factores: la recopilación de los datos y su procesamiento. El análisis presentado se despliega en función de estos factores. Se considera oportuno el desarrollo de sistemas que aprovechen las cualidades de diferentes sensores en la adquisición de datos y que integren la detección y clasificación de variedad de fallas incluyendo datos de severidad

Development and evaluation of a smartphone-based system for inspection of road maintenance work

Abstract. In the road construction industry, doing work inspection is a laborious and resource-consuming job because of the distributed work site. Contractors in Finland require to capture photos of every road fix they have done as proof of their work. It is well-established that with the help of smartphone technology, these kinds of manual work can be reduced. This thesis aims to develop and evaluate a smartphone-based system to capture video evidence of task completion. The system, designed and developed in this thesis, consists of an Android application named ’Road Recorder’ and a web tool for managing the content collected by Road Recorder. While mounted to a vehicle’s dashboard used in construction work, the Road Recorder can record the videos of road surface and geo-location information and some other metadata and send them to a remote server that is inspected using the web tool. Users of different backgrounds were given the system to accomplish some tasks and were observed closely. The users were interviewed at the end, and responses were analyzed to find the usability of the applications. The results indicate the high usability of the Road Recorder application and reveal possible improvements for the Road Recorder management web application. Overall, Road Recorder is a great step towards the automation of such construction work inspection. Though there were some limitations in the evaluation process, it demonstrates that Road Recorder is easy to use and can be a useful tool in the industry

Influencia de las fallas superficiales del pavimento flexible en la transitabilidad del tramo vía nacional San Salvador - Pisac, provincia de Calca, departamento del Cusco - 2021

La presente investigación tiene por finalidad de analizar la influencia de las fallas superficiales del pavimento flexible en la transitabilidad de la vía nacional 28B del tramo San Salvador – Pisac, ubicado en la provincia de Calca, Departamento del Cusco – 2021, la infraestructura vial es un factor importante en el progreso de las regiones, el mal estado de las vías ocasiona una mala calidad en este servicio público, lo que afecta la seguridad vial y aumenta los costos de transporte. Esta investigación es de gran beneficio público, ya que servirá de fuente de información para futuros proyectos debido a que contendrá data verídica. Asimismo, servirá para la toma de decisiones en futuros acuerdos municipales sobre fallas superficiales del pavimento flexible. La metodología que se utilizó para la evaluación de las fallas superficiales fue el método de PCI, y para comprobar la evaluación también se utilizó el método de VIZIR, el método PCI identifica la severidad de las fallas en el pavimento, cuyos resultados fueron procesados con el programa SPSS, para determinar la influencia de las fallas en la transitabilidad. Por otro lado, el cálculo del índice medio diario vehicular (IMD), fue generado por la cantidad de vehículos en general y las cargas que estas ejercen en el pavimento, ocasionando las fallas superficiales del pavimento. El resultado obtenido mediante el método PCI de 35.40% el cual indica que el pavimento se encuentra con severidad de grado malo, y con el método del VIZIR el resultado es un pavimento regular. Por lo tanto, se concluye que las fallas influyen significativamente en la transitabilidad vehicular y peatonal por encontrarse en mal estado la vía. Por lo cual se recomienda realizar el mantenimiento de la vía periódicamente y reposición en un 35%

Detection of road structure composition and geometry changes by processing measured parameters, for the purpose of road network categorization

U ovoj disertaciji razvijen je algoritam za predikciju rizika proklizavanja vozila, čime je omogućeno mapiranje rizičnih zona duž putne infrastrukture. Predloženim algoritmom se realizuju automatska detekcija i prepoznavanje finih promena sastava i geometrije putne površi. Ovo se zasniva na obradi teksture slike dobijene skeniranjem puta, iz specijalnog vozila koje se kreće kolovoznom trakom duž deonice putne mreže. Merni podaci prikupljeni su upotrebom multisenzorske platforme montirane na vozilo. Ovakav pristup analizi putne infrastrukture ima za cilj adekvatnu i blagovremenu reakciju na promene stanja površi puta, koje nisu vidljive golim okom od strane direktnih učesnika u saobraćaju. Ovo je od posebnog značaja i za potrebe službi koje se bave održavanjem puteva i sanacijom oštećenja. Na osnovu eksperimentalnih rezultata i obradom izmerenih parametara, razvijen je i predstavljen algoritam čiji je glavni cilj predviđanje rizika i lokalizacija regiona potencijalnih saobraćajnih nezgoda koje mogu nastati kao posledica proklizavanja vozila sa putne površi. U pogledu strukturnog kvaliteta, putna površ se najčešće opisuje svojom teksturom. Njena geomerijska svojstva direktno utiču na druge činioce bezbednosti u saobraćaju, kao što su interakcija pneumatika sa površinskim slojem puteva, odvođenje tj. drenaža vode i otpornost na proklizavanje. U osnovi razvoja pomenutog algoritma urađene su analize jednodimenzionalnih i dvodimenzionalnih signala dobijenih uređajima za beskontaktno skeniranje. Akvizicija jednodimenzionalnih signala vršena je na osnovu interakcije koherentne svetlosti sa površinskim materijalima puta, upotrebom laserskog profilometra. Dvodimenzionalni signal je dobijen upotrebom video-kamere kojom je snimana putna površ. Oba dijagnostička pristupa realizovana su uređajima sa istog specijalnog vozila. U ovoj disertaciji je najpre potvrđena multifraktalna priroda profila putne površi, čime je dokazana mogućnost primene multifraktalnog pristupa u analizi teksture puta, koja se pokazala kao veoma pouzdan alat za detekciju i lokalizaciju granulometrijskih promena na putnoj površi. Rezultati multifraktalne analize su iskorišćeni kao potvrda stohastičke prirode jednodimenzionalnog signala, i pretpostavka da dvodimenzionalni signal pripada sličnoj familiji slučajnih/pseudoslučajnih vremenskih serija. Novi algoritam predikcije rizika, predložen u disertaciji, bazira se na obradi i analizi dvodimenzionalnog signala. Obrada i analiza slike vršena je testiranjem četiri metode za ekstrakciju obeležja teksture: Gaborovom transformacijom, transformacijom talasićima, matricom kopojavljivanja nivoa sivog i obeležjima histograma ivica. Od svih navedenih metoda, primena Gaborove transformacije je pokazala najbolje rezultate. Ekstrakcija vektora obeležja teksture praćena statističkim algoritmima za merenje sličnosti vektora obeležja i selekcija referentnog vektora, dovela je do klasifikacije teksture slike. Sâm algoritam je nadograđen inkorporiranjem istovremenih merenja temperature površine, kako bi se kreirala i validirala finalna klasifikacija finih tekstura površine. Put je klasifikovan u klase rizika visokog, srednjeg i niskog nivoa, u skladu sa opasnostima od proklizavanja, što je omogućilo formiranje mape rizičnih zona. Algoritam predviđanja rizika je potvrđenna osnovu podataka o saobraćajnim nezgodama, koje su se desile u periodu od tri sukcesivne godine na istoj deonici puta, pribavljenih iz baze Agencije za bezbednost drumskog saobraćaja Srbije. Razvijeni algoritam omogućava predikciju lokacija rizičnih zona sa mapiranjem, koje upozoravaju na potencijalne saobraćajne nezgode usled proklizavanja vozila. Može se koristiti kao podrška za navigaciju, autonomnu vožnju, a moguće je unaprediti celu proceduru sa ciljem adekvatne reakcije u realnom vremenu, putem globalne mreže (IoV - Internet of Vehicles), koja postaje sastavni deo tzv. pametnih gradova (smart cities). Ovakav pristup analizi putne površi će svakako, u svojoj daljoj primeni, rezultirati u smeru precizne i objektivne klasifikacije i kategorizacije kompletne putne infrastrukture, a sve u pravcu povećanja bezbednosti učesnika u saobraćaju, sa naročitim akcentom na rešenje problema predviđanja rizika na putu za donošenje odluka pri autonomnoj vožnji.This dissertation describes the development of an algorithm for predicting the risk of vehicle skidding by mapping high-risk zones along road surfaces. The algorithm enables the automatic detection and recognition of fine changes in the composition and geometry of road surfaces. It is based on image texture processing of the metrics obtained from scanning the road surface using a vehicle-mounted multi-sensory platform. The objective of this algorithm is to provide the means to a real time response to invisible to the bare eye changes in road surface conditions for the benefit of road maintenance and damage repair services, as well as general motorists and autonomously driven vehicles. The algorithm will be capable of being used to identify and assess the accident risk posed by inadequate and compromised road surfaces that potentiate the possibility of vehicles skidding and sliding. In terms of structural quality, road surface is most often described according to its texture. Its geometric properties have a direct impact on other road safety factors, such as interaction with vehicle tires, water drainage and skid resistance. The development of the algorithm was based on analysis of onedimensional and two-dimensional signals obtained by contactless scanning devices. The acquisition of one-dimensional signals was performed with a laser profiler, and the two-dimensional signal was obtained with a combination of a video camera and a surface temperature sensor. All diagnostic devices were mounted on the same special vehicle. For this research, the multifractal nature of the road surface profile was firstly confirmed, thus proving the feasibility of applying a multifractal approach to analyze road texture. This has proven to be a very reliable tool for detecting and locating real changes in the geometry of road surfaces. The results of multifractal analysis were used to confirm the stochastic nature of the one-dimensional signal, and the assumption that the two-dimensional signal belongs to a similar family of random / pseudorandom time series. The new risk prediction algorithm proposed in this dissertation is based on processing and analyzing a two-dimensional signal. Image processing and analysis were tested by comparing four texture extraction methods: Gabor transform, wavelet transform, gray level co-occurrence matrix and edge histogram descriptor. Of all the above methods, the Gabor transform produced the best results. Texture feature vector extraction, followed by statistical algorithms to measure feature vector similarity and reference vector selection, led to the classification of the image texture. The algorithm itself has been upgraded by incorporating simultaneous surface temperature measurements to create and validate the final classification of fine surface textures. The road was classified into high, medium and low level risk areas according to skid hazard, which enabled the formation of a map of risk zones. The algorithm for risk prediction was validated on the basis of traffic accidents which occurred over three successive years on the same section of road, information for which was obtained from the database of the Road Traffic Safety Agency of Serbia.The algorithm that has been developed enables risk assessment mapping of dangerous locations. In this way, potential traffic accident sites due to vehicle skidding can be flagged. It could be used as a support for navigation or for autonomous driving. The entire procedure could be improved and updated by integrating real time responses through the global network (Internet of Vehicles - IoV), to become an integral part of so-called smart cities. The approach to road surface analysis described in this research paper could potentially be applied to the precise and objective classification and categorization of the entire road surface infrastructure. Road safety could be increased, with particular emphasis on solving the risk prediction problems for decision making for autonomous driving