Fast Segmentation of Industrial Quality Pavement Images using Laws Texture Energy Measures and k-Means Clustering

Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture non-uniformities making their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough and expedited health monitoring of roads. In the pavement monitoring area, well known texture descriptors such as gray-level co-occurrence matrices and local binary patterns are often used for surface segmentation and identification. These, despite being the established methods for texture discrimination, are inherently slow. This work evaluates Laws texture energy measures as a viable alternative for pavement images for the first time. k-means clustering is used to partition the feature space, limiting the human subjectivity in the process. Data classification, hence image segmentation, is performed by the k-nearest neighbor method. Laws texture energy masks are shown to perform well with resulting accuracy and precision values of more than 80%. The implementations of the algorithm, in both MATLAB and OpenCV/C++, are extensively compared against the state of the art for execution speed, clearly showing the advantages of the proposed method. Furthermore, the OpenCV based segmentation shows a 100% increase in processing speed when compared to the fastest algorithm available in literature

Sustainable approaches for stormwater quality improvements with experimental geothermal paving systems

This article has been made available through the Brunel Open Access Publishing Fund.This research assesses the next generation of permeable pavement systems (PPS) incorporating ground source heat pumps (geothermal paving systems). Twelve experimental pilot-scaled pavement systems were assessed for its stormwater treatability in Edinburgh, UK. The relatively high variability of temperatures during the heating and cooling cycle of a ground source heat pump system embedded into the pavement structure did not allow the ecological risk of pathogenic microbial expansion and survival. Carbon dioxide monitoring indicated relatively high microbial activity on a geotextile layer and within the pavement structure. Anaerobic degradation processes were concentrated around the geotextile zone, where carbon dioxide concentrations reached up to 2000 ppm. The overall water treatment potential was high with up to 99% biochemical oxygen demand removal. The pervious pavement systems reduced the ecological risk of stormwater discharges and provided a low risk of pathogen growth

Machine learning for homogeneous grouping of pavements

Abstract Machine learning for homogeneous grouping of pavements. Kanan Mukhtarli Rapid pavement deterioration is a major problem in areas with harsh weather conditions or high traffic loading. Despite many studies focused on the pavement management systems, there is not, to the date, a robust method explaining how to process large amounts of pavement data to create homogeneous groups for rehabilitation-related decision making. This thesis employs machine learning to develop an approach capable of partitioning pavement data with a close response to casual factors like traffic and weather conditions and considering its performance through international roughness index and deflections. Two different methods: K-means and Self Organizing Maps (SOM) clustering techniques were tested to understand the correlation between daily factors and pavements deterioration. The goodness of clustering was tested using extrinsic and intrinsic evaluation methods. It was concluded from the results that SOM clustering provided better results as it relies on a soft clustering method where one point can represent two clusters at the same time. Moreover, it became obvious from the methodology that including the previous year’s data has very little to no effect on homogeneous groups. Techniques discussed and developed in this study can help road asset managers with decision making for the maintenance and rehabilitation of pavement. Moreover, future researchers can use the results of this study to further develop the idea of building decision support systems for pavement rehabilitation

Statistical Clustering Performance in Pavement Condition Prediction as Decision Supporting System Tool

Mathematical methods and statistical patterns have always been considered by managers, designers and science and technology expert in order to develop technology and engineering objectives. During the development of data-gathering tools and increment of data-bases, data mining have made suitable tools in management and engineering. The assessment of roads' maintenance is highly important in order to prevent early deterioration of roads and performing maximum road capacity during the service-life. Pavement management of roads has also implemented this tool to make proper decisions and preferences of pavement repair methods, using decision tree. Through engineering management, cluster analysis is one of the basic tools of data mining and knowledge discovery and makes the decision making, easier in engineering. Data categorization is helpful for planning and is important in picking proper methods. This study was performed by using recorded data from other scientific sources considering data mining method and analyzing data with respect to statistical clustering. The results indicate that bitumen content in asphalt mix, pavement age, marshal strength and rate of passing vehicles have the most important effect on decrement of condition index of pavement, relatively. Also, the highest deterioration in asphalt happens in 5.5% and higher values of bitumen content and the progressive deteriorations take place when the pavement age exceeds 35 years

Data-driven algorithms for enhanced transportation infrastructure asset management

State highway agencies collect a considerable amount of digital data to document as well as support a variety of decision-making processes. This data is used to develop insights and extract information to enhance serval decision-making systems. However, digital data collected by highway agencies has been consistently underutilized especially in supporting data-driven or evidence-based decision-making systems. This underutilization is a result of a poor established connection between the data collected and its final possible usage. This study analyzes the digital data collected by highway agencies to enhance the reliability of decision-making systems by utilizing Geographic Information Systems (GIS) and data analytics. This study will a) develop an enhanced Life-Cycle Cost Analysis (LCCA) for pavement rehabilitation investment decisions by establishing a novel cost classification system , b) identifying the barriers and challenges faced by agencies to adopt a data-driven pavement performance evaluation process, and c) develop a dynamic pavement delineation algorithm that aggregates the pavement condition data at the distress level. In order to achieve these objectives, the study uses different digital dataset including a) pavement rehabilitation historical bid-data, b) pavement rehabilitation as-built drawings, c) pavement condition data, and d) pavement maintenance and rehabilitation geospatial data. The study developed an enhanced life-cycle cost analysis practice that would significantly improve the economic evaluation accuracy of investment decisions. Additionally, the study identified seven major barriers and challenges that hinder the adoption of a data-driven pavement performance evaluation. Finally, the study developed and automated a pavement delineation algorithm using Python programming language. This study is expected help highway agencies utilize their historical digital datasets to support a variety of decision-making systems. Furthermore, the study paves the way to adopting and implementing data-driven and evidence based decision-making processes

WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

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

Research on Intelligent Decision-Making of Asphalt Pavement Maintenance in Offshore Soft Soil Area

The performance of roads in offshore soft soil areas is different from ordinary pavement. In view of this feature, and based on the summary of the existing intelligent decision-making research on the maintenance of asphalt pavement, this study has selected Ningbo as the survey area. The changes in the performance characterization index of asphalt pavement in offshore soft soil areas were compared and analyzed. The influencing factors of decision-making in the maintenance were analyzed, and the pavement maintenance standards and intelligent decision-making process in offshore soft soil areas were determined, including the timing of preventive maintenance, road section under maintenance, maintenance plan, etc. The conclusions in this study can promote the scientific decision-making on asphalt pavement maintenance in offshore soft soil areas and promote the healthy and sustainable development of highway maintenance

INTELLIGENT ROAD MAINTENANCE: A MACHINE LEARNING APPROACH FOR SURFACE DEFECT DETECTION

The emergence of increased sources for Big Data through consumer recording devices gives rise to a new basis for the management and governance of public infrastructures and policy de-sign. Road maintenance and detection of road surface defects, such as cracks, have traditionally been a time consuming and manual process. Lately, increased automation using easily acquirable front-view digital natural scene images is seen to be an alternative for taking timely maintenance decisions; reducing accidents and operating cost and increasing public safety. In this paper, we propose a machine learning based approach to handle the challenge of crack and related defect detection on road surfaces using front-view images captured from driver’s viewpoint under diverse conditions. We use a superpixel based method to first process the road images into smaller coherent image regions. These superpixels are then classified into crack and non-crack regions. Various texture-based features are combined for the classification mod-el. Classifiers such as Gradient Boosting, Artificial Neural Network, Random Forest and Linear Support Vector Machines are evaluated for the task. Evaluations on real datasets show that the approach successfully handles different road surface conditions and crack-types, while locating the defective regions in the scene images