Comprehensive Decision Support System for Managing Asphalt Pavements

Hadjidemetriou, Georgios M.; Masino, Johannes; Christodoulou, Symeon E.; Gauterin, Frank; Brilakis, Ioannis

doi:10.1061/jpeodx.0000189

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…e network of asphalt pavement roads is a crucial element of infrastructure in modern societies [1][2][3][4]. As pointed out by [5], asphalt pavement roads significantly support social interaction as well as economic development.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.

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Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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“…The network of asphalt pavement road is definitely the largest component of infrastructure and its essentiality is reflected by the influence on daily activities of people. Therefore, the pavement condition strongly affects the safety of vehicle passengers and any degradations of road surface can bring about multiple negative influences on vehicle passengers as well as economic development of local societies (Gao et al 2020;Hadjidemetriou et al 2020;Hadjidemetriou et al 2018;Liu et al 2017). Many countries around the globe are currently focusing on maintaining the acceptable road condition.…”

Section: Research Background and Motivationmentioning

confidence: 99%

Image processing-based automatic detection of asphalt pavement rutting using a novel metaheuristic optimized machine learning approach

et al. 2021

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This study presents a novel computer vision based approach to automatically identify rutting appeared on asphalt pavement of road. The developed model is established base on a hybridization of image processing techniques and an advanced machine learning model with support of a metaheuristic optimization engine. Gabor filter and discrete cosine transform are employed to implement context computation for image data, accordingly generate initially extracted features of rutting and non-rutting. Least Squares Support Vector Classification (LSSVC) is then used to learn categorization of rutting and non-rutting based on the extracted features. The final LSSVC prediction model is constructed via a loop of optimization process which is controlled by a novel metaheuristic optimization algorithm, called forensic-based investigation (FBI), to attain optimal model's configuration with ultimate prediction accuracy. This study further utilized a dynamic feature selection (FS) method to integrate in the searching loop to appropriately remove redundant features that provide inconsistent information leading to the compromising of model performance. A dataset of 2000 image samples has been collected during field trip of pavement survey in Da Nang city to form and verify the newly developed model. The statistical results of 20 run times using k-fold cross validation method have demonstrated the hybrid model of FBI-LSSVC-FS to achieve the most desired rutting detection performance with classification accuracy rate, precision, recall, and F1 score of 98.9%, 0.994, 0.984 and 0.989, respectively. Hence, this paper contributes to the core body of knowledge a novel AI-based prediction model to assist transportation agencies in the task of periodic asphalt pavement survey.

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“…Management of existing infrastructure includes asset monitoring, condition prediction and maintenance prioratisation. In practice, management and planning of transport infrastructure assets is conducted in different ways by decision-makers, who follow their organisations' guidelines and attempt to effectively use their available budget (Hadjidemetriou et al, 2020a). Assets are assessed either manually by inspectors or automatically with the aid of sensors and novel monitoring technologies, which can be based on computer vision and artificial intelligence (Christodoulou et al, 2018;Hadjidemetriou et al, 2015;Zhu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Road Network Layout Planning based on Evaluation of Connectivity and Asset Criticality

Hadjidemetriou¹,

Herrera²,

Parlikad³

2021

Proceedings of the 2021 European Conference on Computing in Construction

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State of the art research in ensuring transport infrastructure resilience focuses on adopting a network perspective. However, there is no comprehensive, widespread method for evaluating connectivity and proposing alternative routes to improve it. Presented herein is a framework that: assesses road network connectivity, using a "closeness" measure and focusing on routes passing by the vulnerable asset of bridges; and proposes the development of optimal alternative routes, using a genetic algorithm. The results showed a significant improvement of network connectivity and the potential of the method to serve as the basis for updated transport infrastructure planning practices.

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Comprehensive Decision Support System for Managing Asphalt Pavements

Cited by 9 publications

References 8 publications

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

Image processing-based automatic detection of asphalt pavement rutting using a novel metaheuristic optimized machine learning approach

Road Network Layout Planning based on Evaluation of Connectivity and Asset Criticality

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