Machine learning algorithms for monitoring pavement performance

ABSTRACT: This work introduces the need to develop competitive, low-cost and applicable technologies to real roads to detect the asphalt condition by means of Machine Learning (ML) algorithms. Specifically, the most recent studies are described according to the data collection methods: images, ground penetrating radar (GPR), laser and optic fiber. The main models that are presented for such state-of-the-art studies are Support Vector Machine, Random Forest, Naïve Bayes, Artificial neural networks or Convolutional Neural Networks. For these analyses, the methodology, type of problem, data source, computational resources, discussion and future research are highlighted. Open data sources, programming frameworks, model comparisons and data collection technologies are illustrated to allow the research community to initiate future investigation. There is indeed research on ML-based pavement evaluation but there is not a widely used applicability by pavement management entities yet, so it is mandatory to work on the refinement of models and data collection methods

Predicting the Remaining Service Life of Railroad Bearings: Leveraging Machine Learning and Onboard Sensor Data

By continuously monitoring train bearing health in terms of temperature and vibration levels of bearings tested in a laboratory setting, statistical regression models have been developed to establish relationships between the sensor-acquired bearing health data with several explanatory factors that potentially influence the bearing deterioration. Despite their merits, statistical models fall short of reliable prediction accuracy levels since they entail restrictive assumptions, such as a priori known functional relationship between the response and input variables. A data-driven machine learning algorithm is presented, which can unravel the nonlinear deterioration model purely based on the bearing health data, even when the structure is not apparent. More specifically, a Gradient Boosting Machine is trained using vast amounts of laboratory data collected over the course of over a decade. This will help predict bearing failure, thus, providing railroads and railcar owners the opportunity to schedule preventive maintenance cycles rather than costly reactive ones

Evaluation of pavement skid resistance using computational intelligence

Pavement micro-texture is affected by the aggregate characteristics contained within the surface. It is long desired to develop friction prediction models using pavement surface and aggregate textural properties. However, the development of such models has proven to be challenging because of two reasons: (1) The acquiring of complete and high quality pavement surface data for friction studies remains difficult. (2) No consistent and reliable methodologies and models have been developed for friction prediction and evaluation.The objective of this dissertation is to investigate the most influencing factors for pavement skid resistance, and develop reliable and consistent friction prediction models based on aggregate physical properties and pavement surface texture characteristics from three perspectives. The state-of-the-art 3D laser imaging technology, high speed texture profiler, and the continuous friction measurement equipment (CFME) - Grip Tester, are used in parallel in the field to collect 1-mm 3D pavement surface data, macro-texture profiles and pavement friction data respectively at highway speed for selected testing locations, while the newly developed portable ultra-high resolution 3D texture scanner (LS-40) is utilized in the laboratory to acquire both macro- and micro-texture characteristics of pavement surfaces, and the Aggregate Image Measurement System (AIMS) to analyze surface characteristics of aggregates before and after the Micro-Deval polishing process.Firstly, this study predicts pavement friction as a function of pavement surface and aggregate texture properties. Secondly, panel data analysis (PDA), which is able to investigate the differences of cross-sectional information, but also the time-series changes over time, is conducted to evaluate pavement skid resistance performance and identify the most influencing factors. Finally, inspired by the big success of deep learning in the field of image recognition and computer vision, a novel Deep Residual Network (ResNets) tailored for pavement friction prediction, named Friction-ResNets, is developed using pavement surface texture profiles as the inputs.This dissertation developed several novel friction prediction models that could assist in selecting the most effective PM treatments, and proper aggregates with desired texture characteristics for optimized skid resistance. This study also demonstrates the feasibility of replacing the contact based method for pavement friction evaluation with non-contact texture measurements

Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

With the pressing need to improve the poorly rated transportation infrastructure, asset managers leverage predictive maintenance strategies to lower the life cycle costs while maximizing or maintaining the performance of highways. Hence, the limitations of prediction models can highly impact prioritizing maintenance tasks and allocating budget. This study aims to investigate the potential of different predictive models in reaching an effective and efficient maintenance plan. This paper reviews the literature on predictive analytics for a set of highway assets. It also highlights the gaps and limitations of the current methodologies, such as subjective assumptions and simplifications applied in deterministic and probabilistic approaches. This article additionally discusses how these shortcomings impact the application and accuracy of the methods, and how advanced predictive analytics can mitigate the challenges. In this review, we discuss how advancements in technologies coupled with ever-increasing computing power are creating opportunities for a paradigm shift in predictive analytics. We also propose new research directions including the application of advanced machine learning to develop extensible and scalable prediction models and leveraging emerging sensing technologies for collecting, storing and analyzing the data. Finally, we addressed future directions of predictive analysis associated with the data-rich era that will potentially help transportation agencies to become information-rich

Cyclist route assessment using machine learning

Increasing the number of bike commutes can provide numerous benefits for individuals and communities. However, several factors including the availability of cycle paths, traffic characteristics, and pavement quality, can either encourage or discourage the use of bicycles. To promote cycling and understand how cyclists interact with the urban environment, it is crucial to assess the quality of cyclist routes. This thesis proposes an automatic assessment tool that uses machine learning to detect features of the route segment and calculates a score representing the level of safety and comfort for cyclists. The models are trained on YOLOv5 to classify pavement types, detect pavement defects and detect the presence of cycle paths. Two datasets were built and annotated for the pavement type classification and cycle infrastructure detection tasks. A questionnaire was applied to cyclists to compare the real perceptions with the automatic assessment. The results showed a good alignment with the real perceptions, validating the approach, but also demonstrated the need of adding new features and improving the models’ performance before being adequate for real use.Aumentar o n´umero de deslocamentos de bicicleta pode trazer in´umeros benef´ıcios para indiv´ıduos e comunidades. No entanto, v´arios fatores, incluindo a disponibilidade de ciclovias, caracter´ısticas do tr´afego e qualidade do pavimento, podem encorajar ou desencorajar o uso de bicicletas. Para promover o ciclismo e entender como os ciclistas interagem com o ambiente urbano, ´e crucial avaliar a qualidade das rotas dos ciclistas. Esta tese prop˜oe uma ferramenta de avalia¸c˜ao autom´atica que usa aprendizado de m´aquina para detectar caracter´ısticas do segmento de rota e calcula uma pontua¸c˜ao que representa o n´ıvel de seguran¸ca e conforto para os ciclistas. Os modelos s˜ao treinados no YOLOv5 para classificar os tipos de pavimento, detectar defeitos no pavimento e detectar a presen¸ca de ciclovias. Dois datasets foram constru´ıdos e anotados para as tarefas de classifica¸c˜ao do tipo de pavimento e detec¸c˜ao de infraestrutura cicl´avel. Foi aplicado um question´ario aos ciclistas para comparar as percep¸c˜oes reais com a avalia¸c˜ao autom´atica. Os resultados mostraram um bom alinhamento com as percep¸c˜oes reais, validando a abordagem, mas tamb´em demonstraram a necessidade de adicionar novas caracter´ısticas e melhorar a performance dos modelos antes de ser adequado para uso real

Comparing physically-based with data-driven models for landslide susceptibility: a case study in the Catalan Pyrenees

En este proyecto de investigación, se utilizaron un modelo físico (FSLAM) y cuatro modelos basados en datos (regresión logística, SVC, árbol de clasificación y bosque aleatorio) para mapear la susceptibilidad a los deslizamientos para un área de estudio ubicada en los Pirineos Catalanes. Seguidamente, se compararon los resultados de todos los modelos para determinar cuál funcionó mejor y en qué condiciones. También se discutieron las ventajas y desventajas de cada modelo, así como las limitaciones de sus productos finales.In this research project, a physically-based (FSLAM) and four data-driven models (logistic regression, SVC, classification tree and random forest) were used to map landslide susceptibility for a case study area located in the Catalan Pyrenees. The results for all models were then compared in order to determine which performed best and under which conditions. The advantages and disadvantages of each model were also discussed as well as the limitations of their end products

Prediction of friction degradation in highways with linear mixed models

The development of a linear mixed model to describe the degradation of friction on flexible road pavements to be included in pavement management systems is the aim of this study. It also aims at showing that, at the network level, factors such as temperature, rainfall, hypsometry, type of layer, and geometric alignment features may influence the degradation of friction throughout time. A dataset from six districts of Portugal with 7204 sections was made available by the Ascendi Concession highway network. Linear mixed models with random effects in the intercept were developed for the two-level and three-level datasets involving time, section and district. While the three-level models are region-specific, the two-level models offer the possibility to be adopted to other areas. For both levels, two approaches were made: One integrating into the model only the variables inherent to traffic and climate conditions and the other including also the factors intrinsic to the highway characteristics. The prediction accuracy of the model was improved when the variables hypsometry, geometrical features, and type of layer were considered. Therefore, accurate predictions for friction evolution throughout time are available to assist the network manager to optimize the overall level of road safety.This research was funded by FCT—Fundação para a Ciência e Tecnologia (Foundation for Science and Technology), Grants No. UIDB/04029/2020 and UIDB/00319/2020

Developing a Data-Driven Safety Assessment Framework for RITI Communities in Washington State

The roadway safety of the Rural, Isolated, Tribal, or Indigenous (RITI) communities has become an important social issue in the United States. Official data from the Federal Highway Administration (FHWA) shows that, in 2012, 54 percent of all fatalities occurred on rural roads while only 19 percent of the US population lived in rural communities. Under the serious circumstances, this research aims to help the RITI communities to improve their roadway safety through the development of a roadway safety management system. Generally, a roadway safety management system includes two critical components, the baseline data platform and safety assessment framework. In our Year 1 and Year 2 CSET projects, a baseline data platform was developed by integrating the safety related data collected from the RITI communities in Washington State. This platform is capable of visualizing the accident records on the map. The Year 3 project further developed the safety data platform by developing crash data analysis and visualization functions. In addition, various roadway safety assessment methods had been developed to provide safety performance estimation, including historical accident data averages, predictions based on statistical and machine learning (ML) models, etc. Beside roadway safety assessment methods, this project investigated the safety countermeasures selection and recommendation methods for RITI communities. Specifically, the research team has reached out to RITI communities and established a formal research partnership with the Yakama Nation. The research team has conducted research on safety countermeasures analysis and recommendation for RITI communities

Triboinformatic Approaches for Surface Characterization: Tribological and Wetting Properties

Tribology is the study of surface roughness, adhesion, friction, wear, and lubrication of interacting solid surfaces in relative motion. In addition, wetting properties are very important for surface characterization. The combination of Tribology with Machine Learning (ML) and other data-centric methods is often called Triboinformatics. In this dissertation, triboinformatic methods are applied to the study of Aluminum (Al) composites, antimicrobial, and water-repellent metallic surfaces, and organic coatings.Al and its alloys are often preferred materials for aerospace and automotive applications due to their lightweight, high strength, corrosion resistance, and other desired material properties. However, Al exhibits high friction and wear rates along with a tendency to seize under dry sliding or poor lubricating conditions. Graphite and graphene particle-reinforced Al metal matrix composites (MMCs) exhibit self-lubricating properties and they can be potential alternatives for Al alloys in dry or starved lubrication conditions. In this dissertation, artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and hybrid ensemble algorithm-based ML models have been developed to correlate the dry friction and wear of aluminum alloys, Al-graphite, and Al-graphene MMCs with material properties, the composition of alloys and MMCs, and tribological parameters. ML analysis reveals that the hardness, sliding distance, and tensile strength of the alloys influences the COF most significantly. On the other hand, the normal load, sliding speed, and hardness were the most influential parameters in predicting wear rate. The graphite content is the most significant parameter for friction and wear prediction in Al-graphite MMCs. For Al-graphene MMCs, the normal load, graphene content, and hardness are identified as the most influential parameters for COF prediction, while the graphene content, load, and hardness have the greatest influence on the wear rate. The ANN, KNN, SVM, RF, and GBM, as well as hybrid regression models (RF-GBM), with the principal component analysis (PCA) descriptors for COF and wear rate were also developed for Al-graphite MMCs in liquid-lubricated conditions. The hybrid RF-GBM models have exhibited the best predictive performance for COF and wear rate. Lubrication condition, lubricant viscosity, and applied load are identified as the most important variables for predicting wear rate and COF, and the transition from dry to lubricated friction and wear is studied. The micro- and nanoscale roughness of zinc (Zn) oxide-coated stainless steel and sonochemically treated brass (Cu Zn alloy) samples are studied using the atomic force microscopy (AFM) images to obtain the roughness parameters (standard deviation of the profile height, correlation length, the extreme point location, persistence diagrams, and barcodes). A new method of the calculation of roughness parameters involving correlation lengths, extremum point distribution, persistence diagrams, and barcodes are developed for studying the roughness patterns and anisotropic distributions inherent in coated surfaces. The analysis of the 3×3, 4×4, and 5×5 sub-matrices or patches has revealed the anisotropic nature of the roughness profile at the nanoscale. The scale dependency of the roughness features is explained by the persistence diagrams and barcodes. Solid surfaces with water-repellent, antimicrobial, and anticorrosive properties are desired for many practical applications. TiO2/ZnO phosphate and Polymethyl Hydrogen Siloxane (PMHS) based 2-layer antimicrobial and anticorrosive coatings are synthesized and applied to steel, ceramic, and concrete substrates. Surfaces with these coatings possess complex topographies and roughness patterns, which cannot be characterized completely by the traditional analysis. Correlations between surface roughness, coefficient of friction (COF), and water contact angle for these surfaces are obtained. The hydrophobic modification in anticorrosive coatings does not make the coated surfaces slippery and retained adequate friction for transportation application. The dissertation demonstrates that Triboinformatic approaches can be successfully implemented in surface science, and tribology and they can generate novel insights into structure-property relationships in various classes of materials