Development of Machine Learning Based Analytical Tools for Pavement Performance Assessment and Crack Detection

Pavement Management System (PMS) analytical tools mainly consist of pavement condition investigation and evaluation tools, pavement condition rating and assessment tools, pavement performance prediction tools, treatment prioritizations and implementation tools. The effectiveness of a PMS highly depends on the efficiency and reliability of its pavement condition evaluation tools. Traditionally, pavement condition investigation and evaluation practices are based on manual distress surveys and performance level assessments, which have been blamed for low efficiency low reliability. Those kinds of manually surveys are labor intensive and unsafe due to proximity to live traffic conditions. Meanwhile, the accuracy can be lower due to the subjective nature of the evaluators. Considering these factors, semiautomated and automated pavement condition evaluation tools had been developed for several years. In current years, it is undoubtable that highly advanced computerized technologies have resulted successful applications in diverse engineering fields. Therefore, these techniques can be successfully incorporated into pavement condition evaluation distress detection, the analytical tools can improve the performance of existing PMSs. Hence, this research aims to bridge the gaps between highly advanced Machine Learning Techniques (MLTs) and the existing analytical tools of current PMSs. The research outputs intend to provide pavement condition evaluation tools that meet the requirement of high efficiency, accuracy, and reliability. To achieve the objectives of this research, six pavement damage condition and performance evaluation methodologies are developed. The roughness condition of pavement surface directly influences the riding quality of the users. International Roughness Index (IRI) is used worldwide by research institutions, pavement condition evaluation and management agencies to evaluate the roughness condition of the pavement. IRI is a time-dependent variable which generally tends to increase with the increase of the pavement service life. In this consideration, a multi-granularity fuzzy time series analysis based IRI prediction model is developed. Meanwhile, Particle Swarm Optimization (PSO) method is used for model optimization to obtain satisfactory IRI prediction results. Historical IRI data extracted from the InfoPave website have been used for training and testing the model. Experiment results proved the effectiveness of this method. Automated pavement condition evaluation tools can provide overall performance indices, which can then be used for treatment planning. The calculations of those performance indices are required for surface distress level and roughness condition evaluations. However, pavement surface roughness conditions are hard to obtain from surface image indicators. With this consideration, an image indicators-based pavement roughness and the overall performance prediction tools are developed. The state-of-the-art machine learning technique, XGBoost, is utilized as the main method in model training, validating and testing. In order to find the dominant image indicators that influence the pavement roughness condition and the overall performance conditions, the comprehensive pavement performance evaluation data collected by ARAN 900 are analyzed. Back Propagation Neural Network (BPNN) is used to develop the performance prediction models. On this basis, the mean important values (MIVs) for each input factor are calculated to evaluate the contributions of the input indicators. It has been observed that indicators of the wheel path cracking have the highest MIVs, which emphasizes the importance of cracking-focused maintenance treatments. The same issue is also found that current automated pavement condition evaluation systems only include the analysis of pavement surface distresses, without considering the structural capacity of the actual pavement. Hence, the structural performance analysis-based pavement performance prediction tools are developed using the Support Vector Machines (SVMs). To guarantee the overall performance of the proposed methodologies, heuristic methods including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are selected to optimize the model. The experiments results show a promising future of machine learning based pavement structural performance prediction. Automated pavement condition analyzers usually detect pavement surface distress through the collected pavement surface images. Then, distress types, severities, quantities, and other parameters are calculated for the overall performance index calculation. Cracks are one of the most important pavement surface distresses that should be quantified. Traditional approaches are less accurate and efficient in locating, counting and quantifying various types of cracks initialed on the pavement surface. An integrated Crack Deep Net (CrackDN) is developed based on deep learning technologies. Through model training, validation and testing, it has proved that CrackDN can detect pavement surface cracks on complex background with high accuracy. Moreover, the combination of box-level pavement crack locating, and pixel-level crack calculation can achieve comprehensive crack analysis. Thereby, more effective maintenance treatments can be assigned. Hence, a methodology regarding pixel-level crack detection which is called CrackU-net, is proposed. CrackU-net is composed of several convolutional, maxpooling, and up-convolutional layers. The model is developed based on the innovations of deep learning-based segmentation. Pavement crack data are collected by multiple devices, including automated pavement condition survey vehicles, smartphones, and action cameras. The proposed CrackU-net is tested on a separate crack image set which has not been used for training the model. The results demonstrate a promising future of use in the PMSs. Finally, the proposed toolboxes are validated through comparative experiments in terms of accuracy (precision, recall, and F-measure) and error levels. The accuracies of all those models are higher than 0.9 and the errors are lower than 0.05. Meanwhile, the findings of this research suggest that the wheel path cracking should be a priority when conducting maintenance activity planning. Benefiting from the highly advanced machine learning technologies, pavement roughness condition and the overall performance levels have a promising future of being predicted by extraction of the image indicators. Moreover, deep learning methods can be utilized to achieve both box-level and pixel-level pavement crack detection with satisfactory performance. Therefore, it is suggested that those state-of-the-art toolboxes be integrated into current PMSs to upgrade their service levels

Development of Machine Learning Based Analytical Tools for Pavement Performance Assessment and Crack Detection

Pavement Management System (PMS) analytical tools mainly consist of pavement condition investigation and evaluation tools, pavement condition rating and assessment tools, pavement performance prediction tools, treatment prioritizations and implementation tools. The effectiveness of a PMS highly depends on the efficiency and reliability of its pavement condition evaluation tools. Traditionally, pavement condition investigation and evaluation practices are based on manual distress surveys and performance level assessments, which have been blamed for low efficiency low reliability. Those kinds of manually surveys are labor intensive and unsafe due to proximity to live traffic conditions. Meanwhile, the accuracy can be lower due to the subjective nature of the evaluators. Considering these factors, semiautomated and automated pavement condition evaluation tools had been developed for several years. In current years, it is undoubtable that highly advanced computerized technologies have resulted successful applications in diverse engineering fields. Therefore, these techniques can be successfully incorporated into pavement condition evaluation distress detection, the analytical tools can improve the performance of existing PMSs. Hence, this research aims to bridge the gaps between highly advanced Machine Learning Techniques (MLTs) and the existing analytical tools of current PMSs. The research outputs intend to provide pavement condition evaluation tools that meet the requirement of high efficiency, accuracy, and reliability. To achieve the objectives of this research, six pavement damage condition and performance evaluation methodologies are developed. The roughness condition of pavement surface directly influences the riding quality of the users. International Roughness Index (IRI) is used worldwide by research institutions, pavement condition evaluation and management agencies to evaluate the roughness condition of the pavement. IRI is a time-dependent variable which generally tends to increase with the increase of the pavement service life. In this consideration, a multi-granularity fuzzy time series analysis based IRI prediction model is developed. Meanwhile, Particle Swarm Optimization (PSO) method is used for model optimization to obtain satisfactory IRI prediction results. Historical IRI data extracted from the InfoPave website have been used for training and testing the model. Experiment results proved the effectiveness of this method. Automated pavement condition evaluation tools can provide overall performance indices, which can then be used for treatment planning. The calculations of those performance indices are required for surface distress level and roughness condition evaluations. However, pavement surface roughness conditions are hard to obtain from surface image indicators. With this consideration, an image indicators-based pavement roughness and the overall performance prediction tools are developed. The state-of-the-art machine learning technique, XGBoost, is utilized as the main method in model training, validating and testing. In order to find the dominant image indicators that influence the pavement roughness condition and the overall performance conditions, the comprehensive pavement performance evaluation data collected by ARAN 900 are analyzed. Back Propagation Neural Network (BPNN) is used to develop the performance prediction models. On this basis, the mean important values (MIVs) for each input factor are calculated to evaluate the contributions of the input indicators. It has been observed that indicators of the wheel path cracking have the highest MIVs, which emphasizes the importance of cracking-focused maintenance treatments. The same issue is also found that current automated pavement condition evaluation systems only include the analysis of pavement surface distresses, without considering the structural capacity of the actual pavement. Hence, the structural performance analysis-based pavement performance prediction tools are developed using the Support Vector Machines (SVMs). To guarantee the overall performance of the proposed methodologies, heuristic methods including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are selected to optimize the model. The experiments results show a promising future of machine learning based pavement structural performance prediction. Automated pavement condition analyzers usually detect pavement surface distress through the collected pavement surface images. Then, distress types, severities, quantities, and other parameters are calculated for the overall performance index calculation. Cracks are one of the most important pavement surface distresses that should be quantified. Traditional approaches are less accurate and efficient in locating, counting and quantifying various types of cracks initialed on the pavement surface. An integrated Crack Deep Net (CrackDN) is developed based on deep learning technologies. Through model training, validation and testing, it has proved that CrackDN can detect pavement surface cracks on complex background with high accuracy. Moreover, the combination of box-level pavement crack locating, and pixel-level crack calculation can achieve comprehensive crack analysis. Thereby, more effective maintenance treatments can be assigned. Hence, a methodology regarding pixel-level crack detection which is called CrackU-net, is proposed. CrackU-net is composed of several convolutional, maxpooling, and up-convolutional layers. The model is developed based on the innovations of deep learning-based segmentation. Pavement crack data are collected by multiple devices, including automated pavement condition survey vehicles, smartphones, and action cameras. The proposed CrackU-net is tested on a separate crack image set which has not been used for training the model. The results demonstrate a promising future of use in the PMSs. Finally, the proposed toolboxes are validated through comparative experiments in terms of accuracy (precision, recall, and F-measure) and error levels. The accuracies of all those models are higher than 0.9 and the errors are lower than 0.05. Meanwhile, the findings of this research suggest that the wheel path cracking should be a priority when conducting maintenance activity planning. Benefiting from the highly advanced machine learning technologies, pavement roughness condition and the overall performance levels have a promising future of being predicted by extraction of the image indicators. Moreover, deep learning methods can be utilized to achieve both box-level and pixel-level pavement crack detection with satisfactory performance. Therefore, it is suggested that those state-of-the-art toolboxes be integrated into current PMSs to upgrade their service levels

Road Deterioration detection A Machine Learning-Based System for Automated Pavement Crack Identification and Analysis

Road surfaces may deteriorate over time because of a number of external factors such as heavy traffic, unfavourable weather, and poor design. These flaws, which may include potholes, fissures, and uneven surfaces, can pose significant safety threats to both vehicles and pedestrians. This research aims to develop and evaluate an automated system for detecting and analyzing cracks in pavements based on machine learning. The research explores the utilisation of object detection techniques to identify and categorize different types of pavement cracks. Additionally, the proposed work investigates several approaches to integrate the outcome system with existing pavement management systems to enhance road maintenance and sustainability. The research focuses on identifying reliable data sources, creating accurate and effective object detection algorithms for pavement crack detection, classifying various types of cracks, and assessing their severity and extent. The research objectives include gathering reliable datasets, developing a precise and effective object detection algorithm, classifying different types of pavement cracks, and determining the severity and extent of the cracks. The study collected pavement crack images from various sources, including publicly available databases and images captured using mobile devices. Multiple object detection models, such as YOLOv5, YOLOv8, and CenterNet were trained and tested using the collected dataset. The proposed approaches were evaluated using different performance metrics, The achieved results indicated that the YOLOv5 model outperformed CenterNet by a significant margin

Airport Runway Defect Detection Device: A Project-Based Learning Media

In the Aviation industry, runways play a significant role in flight safety. Damage to the runway can cause aircraft accidents that can harm several parties. The use of labor in inspection activities tends to frequent human error. Therefore, the author makes a neural network technology tool to detect runway damage at the airport. This research aims to design a runway defect detection device used as a project-based learning media for Transportation Cadets, especially Palembang Aviation Polytechnic Cadets, so they can absorb learning material quickly and find a new learning atmosphere. This research uses the research and development method by analyzing the needs qualitatively. The results of this study indicate that the design scenario and how this tool works can help in learning, especially project-based learning. Hence, the design of this tool can be utilized as a reference in the development and use of the tool

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

Identification of Top-down, Bottom-up, and Cement-Treated Reflective Cracks Using Convolutional Neural Network and Artificial Neural Network

The objective of this study was to formulate a Convolutional Neural Networks (CNN) model and to develop a decision-making tool using Artificial Neural Networks (ANN) to identify top-down, bottom-up, and cement treated (CT) reflective cracking in in-service flexible pavements. The CNN’s architecture consisted of five convolutional layers with three max-pooling layers and three fully connected layers. Input variables for the ANN model were pavement age, asphalt concrete (AC) thickness, annual average daily traffic (AADT), type of base, crack orientation, and crack location. The ANN network architecture consisted of an input layer of six neurons, a hidden layer of ten neurons, and a target layer of three neurons. The developed CNN model was found to achieve an accuracy of 93.8% and 91.0% in the testing and validation phases, respectively. The ANN based decision-making tool achieved an overall accuracy of 92% indicating its effectiveness in crack identification and classification. In the second phase of the study, the flexible pavement responses under a dual tire assembly were analyzed to identify the critical stress mechanisms for bottom-up and top-down cracking. Higher tensile strains were observed to occur underneath the tire ribs than away from them supporting the argument that both surface initiated and bottom-up fatigue cracking develop in or near the wheel paths. The incorporation of surface transverse tangential stresses increased the surface tensile strains near the tire ribs by approximately 68%, 63%, and 53% respectively for low, medium, and high volume flexible pavements indicating an increased potential for the initiation and development of top-down cracking when tangential stresses are considered. In contrast, this effect was observed to be minimal for the tensile strains at the bottom of the asphalt layer, which are the main pavement responses used in the prediction of fatigue cracking. Shrinkage cracking in cement treated base (CTB) was also modeled in finite element using displacement boundary conditions. The tensile stresses due to shrinkage strains in the cement treated base were observed to be comparable to the tensile strength of CTB at 7 days and higher at 56 days indicating the potential development of shrinkage cracks

Damage identification in bridge structures : review of available methods and case studies

Bridges are integral parts of the infrastructure and play a major role in civil engineering. Bridge health monitoring is necessary to extend the life of a bridge and retain safety. Periodic monitoring contributes significantly in keeping these structures operational and extends structural integrity. Different researchers have proposed different methods for identifying bridge damages based on different theories and laboratory tests. Several review papers have been published in the literature on the identification of damage and crack in bridge structures in the last few decades. In this paper, a review of literature on damage identification in bridge structures based on different methods and theories is carried out. The aim of this paper is to critically evaluate different methods that have been proposed to detect damages in different bridges. Different papers have been carefully reviewed, and the gaps, limitations, and superiority of the methods used are identified. Furthermore, in most of the reviews, future applications and several sustainable methods which are necessary for bridge monitoring are covered. This study significantly contributes to the literature by critically examining different methods, giving guidelines on the methods that identify the damages in bridge structures more accurately, and serving as a good reference for other researchers and future works