Assessing The Prevalence Of Suspicious Activities In Asphalt Pavement Construction Using Algorithmic Logics And Machine Learning
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Assessing the Prevalence of Suspicious Activities in Asphalt Pavement Construction Using Algorithmic Logics and Machine Learning
"Quality Control (QC) and Quality Assurance (QA) is a planned systematic approach to secure the satisfactory performance of Hot mix asphalt (HMA) construction projects. Millions of dollars are invested by government and state highway agencies to construct large-scale HMA construction projects. QC/QA is statistical approach for checking the desired construction properties through independent testing. The practice of QC/QA has been encouraged by the Federal Highway Administration (FHWA) since the mid 60’s. However, the standard QC/QA practice is often criticized on how effective such statistical tests and how representative the reported material tests are. Material testing data alteration in the HMA construction sector can render the QC/QA practice ineffective and shadow the performance of asphalt pavements. The American Society of Civil Engineers estimates that $340 billion is lost globally each year due to corruption in the construction industry. Asphalt pavement construction consists of several sectors, including construction and transportation, which are prone to potential suspicious activities. There is approximately 18 billion tons of asphalt pavement on American roads, which makes the costs of potential suspicious activities unacceptably large. The Idaho Transportation Department (ITD) relies on contractor-produced QC test results for the payment of the HMA pavement projects. In 2017, a case study by FHWA found some unnatural trends where 74% of the ITD test results didn’t match with the contractor results. ITD’s approach to track down the accuracy of mix design and volumetric test data set the off-stage of this research to mark out instances of suspicious activities in asphalt pavement projects. The first objective of this research was to develop algorithmic logics to recognize the patterns of discrepancies in agency- and contractor-produced QC/QA test results. This was possible with a unique dataset that ITD collected from several dozen HMA projects, in which all instances of data entry into the material testing report file was recorded in the background, without the operators’ knowledge. My solution was bifurcated into development of an algorithm combining the logics to automatically detect and categorize suspicious instances when multiple data entries were observed. Modern data mining approaches were also used to explore the latent insights and screen out suspicious incidences to identify the chances of suboptimal materials used for paving and extra payment in HMA pavement projects. I have also successfully prompted supervised machine learning techniques to detect suspicious cases of data alterations. The second step of this research was to calculate the monetary losses due to data alteration. I replicated ITD’s procedure for HMA payment calculation, and quantified payment-related parameters and associated payment for each project for two cases: 1. when the first parameter value categorized as Suspicious Alteration (S.A.) was used for payment calculation, and 2. when the last S.A. parameter value was used for payment. It was evident from my findings that there has been overpayment on construction projects across Idaho due to material testing data alterations. Overall, based on the available audit data, I found that overpayments have ranged from $14,000 to $360,000. Further analysis showed that alteration of each major material testing parameter’s value can cause roughly $1,000 to $5,000 overpayment. I also note that data alteration did not always cause monetary gains. Other possible motives may include passing Percent Within Limit (PWL) criteria and precision criteria. Throughout the research, I strive to automate a suspicious activity detection system and calculate the associated excessive payment."--Boise State University ScholarWorks.
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.
Unpaved Roads' Surface Evaluation Using Unmanned Aerial Vehicle and Deep Learning Segmentation
Unpaved roads have a significant role in Canada's transportation and service activities, accounting for close to 60% of Canada's total public road networks. Furthermore, they connect agricultural, mining, recreational areas, and small communities to the nearby towns and businesses. An effective maintenance program for a network of unpaved roads requires a detailed assessment of the road surface's condition, and such assessment is usually made by visual inspections which can be time-consuming and error prone. The main part of these evaluations aims to identify distresses on the road surface, such as washboarding (corrugation), potholes, and rutting. Many research studies have developed methods to automate condition assessment of asphalt roads by combining machine learning algorithms and low-cost unmanned aerial vehicles (UAV), but the research on the automated assessment of unpaved roads is very limited. A system has been developed in this study to automate the assessment of unpaved roads by coupling computer vision methods, namely deep convolutional neural networks, and UAV-based imaging. This automated system could be used as an alternative method to reduce the need for human effort and possible manual errors, and therefore improve road maintenance programs in remote areas. The performance of the proposed method was evaluated using different test settings, and despite having some challenges, such as false positives, it showed promising outcomes that can contribute to the proposed purpose of this research. This proposed method has a potential for further improvement and the findings can be used as a basis for similar studies.