Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (5)

Search Parameters:
Keywords = square potholes

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
18 pages, 3305 KB  
Article
Defects in Flexible Pavements: A Relationship Assessment of the Defects of a Low-Cost Pavement Management System
by Shabir Hussain Khahro
Sustainability 2022, 14(24), 16475; https://doi.org/10.3390/su142416475 - 9 Dec 2022
Cited by 8 | Viewed by 2990
Abstract
Pavement maintenance is a key concern for pavement management authority. Countries (especially developing countries) are facing severe funding challenges regarding maintenance schemes. The existing pavement maintenance methods are goal-specific and lack integration of various indicators that are significant for low-cost PMSs. Thus, this [...] Read more.
Pavement maintenance is a key concern for pavement management authority. Countries (especially developing countries) are facing severe funding challenges regarding maintenance schemes. The existing pavement maintenance methods are goal-specific and lack integration of various indicators that are significant for low-cost PMSs. Thus, this paper investigates the possible defects that may occur in flexible pavements as well as the relationships between different defects. A detailed literature review was conducted to identify all possible defects in flexible pavements and key features considered PMSs. A questionnaire was designed to seek expert opinions on the defects and their possible relationships for a low-cost PMS. The data were collected from 283 experts currently working in pavement management authorities and pavement maintenance schemes. Aggregated mean score, box plotting, and the chi-square test were used to analyze the data. It is concluded that bumps/sags (3.17) are major defects reported by pavement experts in Pakistan, followed by fatigue cracks (3.07). Rutting (2.98) and rut depth (2.98) are the third-ranked key defects reported in this study. Depression (2.96), potholes (2.76), longitudinal crack (2.69), edge crack (2.55), roughness (2.51), and deflection (2.50) are also regular defects in pavement maintenance activities in Pakistan. The results are in an acceptable range of the three-mentioned validation methods. The correlation test results show that most of the defects in structural, functional, safety, and serviceability indicators reject the null hypothesis; thus, there are close relationships between these defects observed in flexible pavements. In the last stage, a PMS model is suggested to assist road management authorities in developing countries to make low-cost decisions for effective pavement rehabilitation. Full article
Show Figures

Figure 1

15 pages, 2406 KB  
Article
Predicting Pavement Condition Index Using Fuzzy Logic Technique
by Abdualmtalab Ali, Usama Heneash, Amgad Hussein and Mohamed Eskebi
Infrastructures 2022, 7(7), 91; https://doi.org/10.3390/infrastructures7070091 - 2 Jul 2022
Cited by 25 | Viewed by 4488
Abstract
The fuzzy logic technique is one of the effective approaches for evaluating flexible and rigid pavement distress. The process of classifying pavement distress is usually performed by visual inspection of the pavement surface or using data collected by automated distress measurement equipment. Fuzzy [...] Read more.
The fuzzy logic technique is one of the effective approaches for evaluating flexible and rigid pavement distress. The process of classifying pavement distress is usually performed by visual inspection of the pavement surface or using data collected by automated distress measurement equipment. Fuzzy mathematics provides a convenient tool for incorporating subjective analysis, uncertainty in pavement condition index, and maintenance-needs assessment, and can greatly improve consistency and reduce subjectivity in this process. This paper aims to develop a fuzzy logic-based system of pavement condition index and maintenance-needs evaluation for a pavement road network by utilizing pavement distress data from the U.S. and Canada. Considering rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and raveling as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relationships between nine pavement distress parameters and PCI were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF–THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used as the performance indicator metrics to evaluate the performance of analytical models. Full article
(This article belongs to the Special Issue Modern Material and Methods for Infrastructures)
Show Figures

Figure 1

14 pages, 5607 KB  
Article
Experimental and Numerical Investigation of Load Failure at the Interface Joint of Repaired Potholes Using Hot Mix Asphalt with Steel Fiber Additive
by Mohamed S. Eisa, Fahmy S. Abdelhaleem and Vivian A. Khater
Coatings 2021, 11(10), 1160; https://doi.org/10.3390/coatings11101160 - 26 Sep 2021
Cited by 15 | Viewed by 2642
Abstract
The maintenance of potholes is a long-standing problem. Previous studies focused on pothole patching materials and methods but not on bonding at the interface joint. In this study, the influence of the patching shape and depth on the bonding at the interface joint [...] Read more.
The maintenance of potholes is a long-standing problem. Previous studies focused on pothole patching materials and methods but not on bonding at the interface joint. In this study, the influence of the patching shape and depth on the bonding at the interface joint using two patching materials: hot mix asphalt (HMA) and hot mix asphalt containing 5% (by volume) steel fiber (HMA+) was investigated. Slabs with circular and square potholes in the middle with different depths (35, 50 and 70 mm) were prepared. The two shapes of potholes were patched with two patching materials: HMA and HMA+, at different depths. The slabs were tested after patching using a rigid steel frame. The experimental results were compared with those obtained from finite element analysis using the ABAQUS software, applying the same model of slabs with the same dimensions and properties of the materials used. The results indicated that the bonding at joint interface for circular-patched potholes slightly improved using HMA+ and this was independent of patching depth. As for the square-patched potholes, the bonding at the interface joint was better than for the circular-patched ones; the bonding increased with increasing depth. Using HMA+ for patching the square-patched potholes, the bonding at the interface joint slightly increased, only for the 3.5 cm depth. Full article
(This article belongs to the Collection Pavement Surface Coatings)
Show Figures

Figure 1

19 pages, 5093 KB  
Article
Pothole Classification Model Using Edge Detection in Road Image
by Ji-Won Baek and Kyungyong Chung
Appl. Sci. 2020, 10(19), 6662; https://doi.org/10.3390/app10196662 - 23 Sep 2020
Cited by 59 | Viewed by 9164
Abstract
Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road [...] Read more.
Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9. Full article
(This article belongs to the Special Issue Computing and Artificial Intelligence for Visual Data Analysis)
Show Figures

Figure 1

22 pages, 6692 KB  
Article
Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery
by Ben DeVries, Chengquan Huang, Megan W. Lang, John W. Jones, Wenli Huang, Irena F. Creed and Mark L. Carroll
Remote Sens. 2017, 9(8), 807; https://doi.org/10.3390/rs9080807 - 7 Aug 2017
Cited by 97 | Viewed by 13223
Abstract
We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over [...] Read more.
We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications. Full article
(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)
Show Figures

Graphical abstract

Back to TopTop