Deep Learning Framework For Intelligent Pavement Condition Rating: A direct classification approach for regional and local roads

Transport authorities rely on pavement characteristics to determine a pavement condition rating index. However, manually computing ratings can be a tedious, subjective, time-consuming, and training-intensive process. This paper presents a deep-learning framework for automatically rating the condition of rural road pavements using digital images captured from a dashboard-mounted camera. The framework includes pavement segmentation, data cleaning, image cropping and resizing, and pavement condition rating classification. A dataset of images, captured from diverse roads in Ireland and rated by two expert raters using the pavement surface condition index (PSCI) scale, was created. Deep-learning models were developed to perform pavement segmentation and condition rating classification. The automated PSCI rating achieved an average Cohen Kappa score and F1-score of 0.9 and 0.85, respectively, across 1–10 rating classes on an independent test set. The incorporation of unique image augmentation during training enabled the models to exhibit increased robustness against variations in background and clutter

Learning pavement surface condition ratings through visual cues using a deep learning classification approach.

Pavement surface condition rating is an essential part of road infrastructure maintenance and asset management, and it is performed manually by the data analyst. The manual rating requires cognitive skills built through training and experience, which is quantitatively challenging and timeconsuming. This paper first analyses the complexity of the current manual visual rating system. This paper then investigates the suitability and robustness of a state-of-the-art convolutional neural network (CNN) classifier to automate the pavement surface condition index (PSCI) system used to rate pavement surfaces in Ireland. The dataset contains 3735 images of flexible asphalt pavements from Irish urban and rural environments taken from a video camera mounted in front of a van. The PSCI ratings were applied by experts using a scale of 1-10 to indicate surface conditions. The classification models are evaluated for different input pre-processing variations, image size, learning techniques, and the number of classes. Using 10 PSCI classes, the best classifier achieved a precision of 57% and a recall of 58%. Adjacent combination of classes (e.g., ratings 1 and 2 combined into a single class) to form a 5-class problem produced a classifier with a precision of 70% and recall of 77%. Given the complexity of the problem, classification using CNN holds promise as a first step towards an automated ranking system

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

Network-level maintenance decisions for flexible pavement using a soft computing-based framework

An effective pavement management system (PMS) is one that is guided by a software program that ensures that all pavement sections are maintained at adequately high serviceability levels and structural conditions with a low budget and resource usage, without causing any significant negative effect on environment, safe traffic operations and social activities. PMS comprises of section classification; performance prediction; and optimisation for decision-making. For section classification, this research presents a fuzzy inference system (FIS), with appropriate membership functions for section classifications and for calculating the pavement condition index (PCI). The severity and extent of seven distress types (alligator cracking, block cracking, longitudinal and transverse cracking, patching, potholes, bleeding and ravelling) were used as fuzzy inputs. The result showed a good correlation for fuzzy model. A sensitivity analysis showed a pavement crack has the greatest influence on section classification compared to the other distress types. A novel network level deterministic deterioration model was developed for flexible pavement on arterial and collector roads in four climatic zones considering the impact of maintenance, age, area and length of cracks, and traffic loading. The prediction models showed good accuracy with high determination coefficient (R2). The cross-validation study showed that the models for arterial roads yield better accuracy than the models for collector roads. A sensitivity analysis showed that the area and length of cracks have the most significant impact on the model performance. A novel discrete barebones multi-objective particle swarm algorithm was applied for a discrete multi-objective problem. Conventional particle swarm optimisation (PSO) techniques require a manual selection of various control parameters for the velocity term. In contrast, the bare-bones PSO has the advantage of being velocity-free, hence, does not involve any parameter selection. The discrete barebones multi-objective PSO algorithm was applied to find optimal rehabilitation scheduling considering the two objectives of the minimisation of the total pavement rehabilitation cost and the minimisation of the sum of all residual PCI values. The results showed that the optimal maintenance plan found by the novel algorithm is the better than found by conventional algorithm. Although the results of performance metrics showed that the both algorithms perform on a par, the novel algorithm is clearly advantageous as it does not need parameter selection

Predicting Pervious Concrete Pavement Performance for Usage in Cold Climates

Pervious Concrete Pavement (PCP) has the potential to provide significant benefits. To better understand the technical, economical, and environmental impacts of PCP, the performance must be comprehensively evaluated and quantified. Because PCP is a new material, there is no mechanism for properly quantifying its performance. In addition, the application of this technology in cold climates is limited and therefore limited in-service performance data is available. A comprehensive engineering based performance model quantifies the deterioration rate and predicts future performance. Pavement performance models are developed using a pavement condition index and extensive pavement condition databases. A pavement condition index is a value which expresses the overall condition of pavement by considering various factors such as surface distresses, structural defects, and ride quality. This research will assist pavement engineers and managers in the design, construction, and management of PCP. The review of published literature reveals that there is currently a large gap in the performance evaluation of PCP in cold climates. Neither extensive condition indices nor comprehensive performance models have been developed for PCP. This research involves development of comprehensive performance models for PCP in cold climates using laboratory and field experiments and existing available data in order to predict functionality (permeability rate) and surface distresses of PCP. This study is, furthermore, aimed at developing an extensive condition index for better management of PCP by predicting and quantifying the various types of distresses and the associated functionality of PCP with particular emphasis on cold climate usage and performance. The scope of this research is to design a comprehensive tool which is simple and cost-effective. The tool involves first defining the typical types of distresses that are occurring on PCP. This is facilitated through laboratory and field design, construction, and evaluation of two test sites located in Ontario. It also involves continuous evaluation of these sites and evaluation of several other sites in the United States. The main sources of data in this research are panel rating data and field investigations data. A panel rates the condition of PCP in terms of surface distresses and permeability rates. In addition to this, field measurements of distresses and permeability rates are obtained manually. As a result, the Pervious Concrete Condition Index (PCCI) is developed through incorporation of field measurements and panel ratings. By using regression analysis, performance models are developed between PCCI and pavement age. The performance models are validated using the data splitting technique. Ultimately, the performance models are calibrated using field data by applying the Markov Chain process (acquiring expert knowledge by distributing questionnaires) and the Bayesian technique

Effect of recycled concrete aggregate features on adhesion properties of asphalt mortar-aggregate interface

Asphalt-aggregate interface’s adhesion properties commonly affect the damage initiation and evolution within asphalt concrete materials, related to pavement durability and quality. The scope of this research was to investigate the influence of Recycled Concrete Aggregate (RCA) features on asphalt mortar-aggregate interface adhesion. Firstly, a three-dimensional reconstruction model of RCA was carried out using X-ray CT tomography and digital image processing. In this regard, five feature indicators, namely cement mortar content, sphericity, flat and elongated ratio, angularity, and surface texture, were proposed. Based on a bilinear cohesive zone model, the interface damage behavior of asphalt mortar-RCA was investigated by using a uniaxial compression simu- lation. Finally, a GA-BP artificial neural network was conducted to predict and quantify the effect of each feature indicator of RCA on interface adhesion. The results showed that when RCA had lower cement mortar content, higher sphericity value, and smoother surface, the asphalt mortar-RCA system was less prone to interface adhesion failure. The 5-14-1 GA-BP artificial neural network proposed in this study showed very good perfor- mance in predicting the interfacial dissipation damage energy with a mean-squared error value of 3.52 × 10^-4 for testing dataset. The cement mortar content parameter exhibited a remarkable influence on the interface adhesion property, and its global contribution to the interfacial dissipation damage energy (0.3486) was more than twice that of the surface texture parameter (0.1316). In future studies, the performance characteristics of cement mortar can be further investigated, thereby proposing RCA’s performance optimization technology

Machine Learning Approaches to Road Surface Anomaly Assessment Using Smartphone Sensors

Road surface quality is an essential component of roadway infrastructure that leads to better driving standards and reduces risk of traffic accident. Traditional road condition monitoring systems fall short of current need for quick responses to maintain road quality. Several alternative systems have been proposed that utilize sensors mounted on vehicles and with the ubiquitous use of smartphone for personal use and navigation, smartphone based road condition assessment has gained prominence. We propose to analyze different multiclass supervised machine learning techniques to effectively classify road surface conditions using accelerometer, gyroscope and GPS data collected from smartphones. Our work focusses on classification of three main class labels- smooth road, pothole and deep transverse cracks. We investigate our conjecture that using features from all three axes of the sensors provide more accurate results as compared to using features from only one axis. We also investigate the performance of deep neural networks to classify road conditions with and without explicit manual feature extraction. Our results consistently show that models trained with features from all axes of the smartphone sensors perform better than models that use only one axis. This shows that there is information in the vibration signals along all three axis for road anomalies. We also observe that the use of neural networks provide significantly accurate data classification. The approaches discussed here can be implemented on a larger scale to monitor road for defects that present a safety risk to commuters as well as provide maintenance information to relevant authorities

Context-aware multi-head self-attentional neural network model for next location prediction

Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-attentional (MHSA) neural network that learns location transition patterns from historical location visits, their visit time and activity duration, as well as their surrounding land use functions, to infer an individual's next location. Specifically, we adopt point-of-interest data and latent Dirichlet allocation for representing locations' land use contexts at multiple spatial scales, generate embedding vectors of the spatio-temporal features, and learn to predict the next location with an MHSA network. Through experiments on two large-scale GNSS tracking datasets, we demonstrate that the proposed model outperforms other state-of-the-art prediction models, and reveal the contribution of various spatio-temporal contexts to the model's performance. Moreover, we find that the model trained on population data achieves higher prediction performance with fewer parameters than individual-level models due to learning from collective movement patterns. We also reveal mobility conducted in the recent past and one week before has the largest influence on the current prediction, showing that learning from a subset of the historical mobility is sufficient to obtain an accurate location prediction result. We believe that the proposed model is vital for context-aware mobility prediction. The gained insights will help to understand location prediction models and promote their implementation for mobility applications.Comment: updated Discussion section; accepted by Transportation Research Part

Municipal Road Infrastructure Assessment Using Street View Images

Road quality assessment is a crucial part in Municipalities' work to maintain their infrastructure, plan upgrades, and manage their budgets. Properly maintaining this infrastructure relies heavily on consistently monitoring its condition and deterioration over time. This can be a challenge, especially in larger towns and cities where there is a lot of city property to keep an eye on. Municipalities rely on surveyors to keep them up to date on the condition of their infrastructure to prevent this failure before it happens. This is both to prevent injuries and further damage from occurring as a result of infrastructure failure, and since it is can be more cost effective to maintain property rather than have to replace it. Surveying can either be done manually or automatically, but it is not done frequently as it is expensive and also time consuming. Manual surveying can be inaccurate, while a large portion of automatic surveying techniques rely on expensive equipment. To solve this problem, we propose an automated infrastructure assessment method that relies on Street View images for its input and uses various computer vision and pattern recognition methods to generate its assessments. First, we segment the image into 'road' and 'background' regions. We propose a road segmentation algorithm specifically aimed at segmenting roads from street view images. We use Fisher vectors calculated on SIFT descriptors to encode small windows extracted from the main image at multiple scales. Then we classify these patches using an SVM and utilize a Gaussian voting scheme to obtain a segmentation. We additionally utilize a spatial prior to improve this segmentation. Optionally, we improve the segmentation further by making use of a weighted contour map calculated on a shadow-free intrinsic image, and a find an optimal segmentation by utilizing a purity tree. Our algorithm performs well and outputs a good segmentation for further use in road evaluation. We test our method on the KITTI road dataset, and compare it to the state-of-the-art on this dataset, along with a manually annotated subset of Google Street View. After segmenting the road, we describe an algorithm aimed at identifying distressed road regions and pinpointing cracks within them. We predict distressed regions by re-using the computed Fisher vectors and classifying them with a different SVM trained to distinguish between road qualities. We follow this step with a comparison to the weighed contour map within these distressed regions to identify exact crack and defect locations, and use the contour weights to predict the crack severity. Promising results are obtained on our manually annotated dataset, which indicate the viability of using this cost-effective system to perform road quality assessment at a municipal level