Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks

Osman, Sami A.; Almoshaogeh, Meshal; Jamal, Arshad; Alharbi, Fawaz; Mojil, Abdulhamid Al; Dalhat, M.A.

doi:10.3390/su15010561

Cited by 3 publications

(1 citation statement)

References 58 publications

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“…Biking comfort levels on various cycling infrastructures can significantly impact cyclists' perceptions of comfort and mode choice [2,[18][19][20][21][22]. Additionally, the pavement surface quality frequently influences riders' selection of the ideal route [23,24]. Therefore, bicycle paths and roadways dedicated to bicycles should be smooth, requiring minimum effort [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

et al. 2023

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Vibration from bicycle infrastructure affects the cyclists’ comfort and the choice of this transportation mode. This study uses smart portable bicycle lights to measure the vibration and quantify the level of cycling comfort on cycling infrastructure. A total of 28 bicycle streets and paths were selected in the city of Hasselt, Belgium, as the case study area. Six volunteer cyclists were recruited for the vibration sensitivity test of the device before the actual data collection. The results showed no considerable difference in the vibration recorded separately on each tested bicycle surface. The average vibration values vary from 1 to 17.78, indicating that riding comfort varies significantly across different surfaces. Asphalt and concrete roads had the lowest vibration and were the most comfortable in the study area. In contrast, cobblestone-paved bike paths were the least comfortable because of higher vibration. A comfort level map was developed based on the relationship between cycle vibration and subjective perception of comfort level. Twenty cyclists participated in the perception of vibration test. The comfort level is inversely correlated with the vibration. This methodology is adaptable to any other setting. Additionally, practitioners can use it to check and track the quality of the surface of the bicycle infrastructure over time.

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Section: Introductionmentioning

confidence: 99%

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

et al. 2023

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Predicting pavement surface conditions through artificial neural networks

Deneko,

Filaj,

Gheibi

et al. 2024

Proceedings of the Institution of Civil Engineers - Municipal E

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In this study, we evaluate six distinct types of pavement distress using the Pavement Condition Index (PCI) rating: raveling, rutting, cracking (including alligator, transverse, and longitudinal cracks), shoving, patching, and potholes. The severity levels of these distress types are utilized for classification, and these levels are subsequently converted into PCI values. Our methodology incorporates a thorough analysis, considering various variables such as average daily traffic (ADT), land-use classification, number of lanes, road width and length, pavement age, road alignment, vehicle type, and weather conditions in Berzhite, Tirana, Albania. To predict PCI values, we employ an artificial neural network (ANN) model due to its versatility in handling diverse inputs. Key performance metrics, including R-squared, mean squared error, and mean absolute error, are used for model assessment. Notably, Model 2, featuring two hidden layers, exhibits superior performance over Model 1. However, due to size constraints in our testing and validation datasets, the models’ accuracy is somewhat limited. Looking ahead, future directions for our research involve expanding the dataset to enhance model accuracy and incorporating advanced features to capture a more complex comprehension of pavement conditions.

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A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions

Al-Samahi,

Zeiada,

Al-Khateeb

et al. 2024

Infrastructures

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Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide.

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Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks

Cited by 3 publications

References 58 publications

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces

Predicting pavement surface conditions through artificial neural networks

A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions

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