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27 pages, 3498 KiB

Open AccessArticle

Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters

by Ivana Ban, Aleksandra Deluka-Tibljaš and Igor Ružić

Lubricants 2024, 12(1), 23; https://doi.org/10.3390/lubricants12010023 - 12 Jan 2024

Viewed by 1155

The importance of skid resistance performance assessment in pavement engineering and management is crucial due to its direct influence on road safety features. This paper provides a new approach to skid resistance predictive model definition based on experimentally obtained texture roughness parameters. The [...] Read more.

The importance of skid resistance performance assessment in pavement engineering and management is crucial due to its direct influence on road safety features. This paper provides a new approach to skid resistance predictive model definition based on experimentally obtained texture roughness parameters. The originally developed methodology is based on a photogrammetry technique for pavement surface data acquisition and analysis, named the Close-Range Orthogonal Photogrammetry (CROP) method. Texture roughness features were analyzed on pavement surface profiles extracted from surface 3D models, obtained by the CROP method. Selected non-standard roughness parameters were used as predictors in the skid resistance model. The predictive model was developed by the partial least squares (PLS) method as a feature engineering procedure in the regression analysis framework. The proposed model was compared to the simple linear regression model with a traditional texture parameter Mean Profile Depth as the predictor, showing better predictive strength when multiple non-standard texture parameters were used. Full article

(This article belongs to the Special Issue Friction Assessment in Pavement Engineering)

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18 pages, 5158 KiB

Open AccessArticle

Incep-FrictionNet-Based Pavement Texture Friction Level Classification Prediction Method

by Guomin Xu, Xiuquan Lin, Shifa Wang, You Zhan, Jing Liu and He Huang

Lubricants 2024, 12(1), 8; https://doi.org/10.3390/lubricants12010008 - 28 Dec 2023

Cited by 1 | Viewed by 1258

Abstract

Pavement skid resistance is crucial for driving safety, and pavement texture significantly impacts skid resistance performance. To realize the application of pavement texture data in assessing pavement skid resistance performance, this paper proposes a convolutional neural network model based on the InceptionV4 module [...] Read more.

Pavement skid resistance is crucial for driving safety, and pavement texture significantly impacts skid resistance performance. To realize the application of pavement texture data in assessing pavement skid resistance performance, this paper proposes a convolutional neural network model based on the InceptionV4 module to predict the pavement friction level from the pavement texture dataset. The surface texture data of indoor test-rutted slabs were collected using a portable laser scanner. The surface friction coefficient of rutted slabs was measured using a pendulum tribometer. After data pre-processing, a total of nine types of texture data that are in the range of 0.4 to 0.8 skid resistance levels are selected at an interval of 0.05 for training, validation, and testing of the network model. The same dataset and training parameters were also used to train a conventional convolutional network model for comparison. The results showed that the proposed network model achieved 97.89% classification accuracy on the test set, which was 11.94 percentage points higher than the comparison model. This demonstrates that the proposed model in this paper can evaluate pavement friction levels by non-contact scanning of textures and has higher evaluation accuracy. Full article

(This article belongs to the Special Issue Friction Assessment in Pavement Engineering)

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16 pages, 6091 KiB

Open AccessArticle

Estimation of Road Wetness from a Passenger Car

by Wiyao Edjeou, Ebrahim Riahi, Manuela Gennesseaux, Veronique Cerezo and Minh-Tan Do

Lubricants 2024, 12(1), 2; https://doi.org/10.3390/lubricants12010002 - 20 Dec 2023

Viewed by 1242

Abstract

This paper presents an evaluation of a system aiming at estimating water depths on a road surface. Using accelerometers, the system records the vibrations of a wheel arch liner due to impacts of water droplets. The system setup, including the location of the [...] Read more.

This paper presents an evaluation of a system aiming at estimating water depths on a road surface. Using accelerometers, the system records the vibrations of a wheel arch liner due to impacts of water droplets. The system setup, including the location of the accelerometers on a wheel arch and the data acquisition, is described. Tests were performed with a passenger car on various road surfaces and at different vehicle speeds and water depths. Signals recorded by the accelerometers are filtered and processed. The link between the acceleration amplitude, the water depth, and the vehicle speed is consistent with results from previous studies. The effect of the surface texture is less obvious and needs further investigations. A mathematical model has been developed to relate the acceleration amplitude to the water depth. The potential application of the developed system to on-board evaluation of pavement wetness, and consequently the pavement skid resistance, is discussed. Perspectives for driver assistance, or more generally, for autonomous driving to improve traffic safety, are also highlighted. Full article

(This article belongs to the Special Issue Friction Assessment in Pavement Engineering)

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16 pages, 4715 KiB

Open AccessArticle

Reconstruction and Intelligent Evaluation of Three-Dimensional Texture of Stone Matrix Asphalt-13 Pavement for Skid Resistance

by Gang Dai, Zhiwei Luo, Mingkai Chen, You Zhan and Changfa Ai

Lubricants 2023, 11(12), 535; https://doi.org/10.3390/lubricants11120535 - 18 Dec 2023

Cited by 1 | Viewed by 1325

Abstract

To examine the three-dimensional texture structure of SMA-13 asphalt pavement and assess its anti-skid performance, a light gradient-boosting machine evaluation model was developed using non-contact three-dimensional laser-scanning technology. The study focused on collecting three-dimensional texture data from newly laid SMA-13 asphalt pavement. Subsequently, [...] Read more.

To examine the three-dimensional texture structure of SMA-13 asphalt pavement and assess its anti-skid performance, a light gradient-boosting machine evaluation model was developed using non-contact three-dimensional laser-scanning technology. The study focused on collecting three-dimensional texture data from newly laid SMA-13 asphalt pavement. Subsequently, wavelet transform was employed to reconstruct the pavement’s three-dimensional texture, and discrete Fourier transform was utilized to separate macro- and microtextures, enabling the calculation of their characteristics. The macro- and micro-characteristics of the three-dimensional texture and friction coefficient were input into the model. A comparative analysis with linear regression and a random forest model revealed superior accuracy and efficiency in the model. The training set R² is 0.948, and the testing set R² is 0.842, effectively enabling the evaluation of pavement anti-skid performance. An analysis of parameter importance indicated that R_ku and MPD are still effective indicators for evaluating skid resistance. Furthermore, diverse texture indexes exhibited varying effects on the anti-skid performance. The established asphalt pavement anti-skid evaluation model serves as a theoretical foundation for understanding the actual influence on pavement anti-skid performance. Full article

(This article belongs to the Special Issue Friction Assessment in Pavement Engineering)

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22 pages, 4356 KiB

Open AccessArticle

Enhancing Quality Control of Chip Seal Construction through Machine Learning-Based Analysis of Surface Macrotexture Metrics

by Jieyi Bao, Joseph Adcock, Shuo Li and Yi Jiang

Lubricants 2023, 11(9), 409; https://doi.org/10.3390/lubricants11090409 - 18 Sep 2023

Viewed by 1183

Abstract

Efforts to enhance quality control (QC) practices in chip seal construction have predominantly relied on single surface friction metrics such as mean profile depth (MPD) or friction number. These metrics assess chip seal quality by targeting issues such as aggregate loss or excessive [...] Read more.

Efforts to enhance quality control (QC) practices in chip seal construction have predominantly relied on single surface friction metrics such as mean profile depth (MPD) or friction number. These metrics assess chip seal quality by targeting issues such as aggregate loss or excessive bleeding, which may yield low friction numbers or texture depths. However, aggregate loss, particularly due to snowplow operations, does not always result in slippery conditions and may lead to uneven surfaces. The correlation between higher MPD or friction number and superior chip seal quality is not straightforward. This research introduces an innovative machine learning-based approach to enhance chip seal QC. Using a hybrid DBSCAN-Isolation Forest model, anomaly detection was conducted on a dataset comprising 183,794 20 m MPD measurements from actual chip seal projects across six districts in Indiana. This resulted in typical 20 m segment MPD ranges of [0.9 mm, 1.9 mm], [0.6 mm, 2.1 mm], [0.3 mm, 1.3 mm], [1.0 mm, 1.7 mm], [0.6 mm, 1.9 mm], and [1.0 mm, 2.3 mm] for the respective six districts in Indiana. A two-step QC procedure tailored for chip seal evaluation was proposed. The first step calculated outlier percentages across 1-mile segments, with an established limit of 25% outlier segments per wheel track. The second step assessed unqualified rates across projects, setting a threshold of 50% for unqualified 1-mile wheel track segments. Through validation analysis of four chip seal projects, both field inspection and friction measurements closely aligned with the proposed methodology’s results. The methodology presented establishes a foundational QC standard for chip seal projects, enhancing both acceptance efficiency and safety by using a quantitative method and minimizing the extended presence of practitioners on roadways. Full article

(This article belongs to the Special Issue Friction Assessment in Pavement Engineering)

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20 pages, 4434 KiB

Open AccessArticle

Application of Machine Learning Models to the Analysis of Skid Resistance Data

by Aboubakar Koné, Ahmed Es-Sabar and Minh-Tan Do

Lubricants 2023, 11(8), 328; https://doi.org/10.3390/lubricants11080328 - 01 Aug 2023

Cited by 2 | Viewed by 921

Abstract

This paper evaluates the ability of some state-of-the-art Machine Learning models, namely SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to predict pavement skid resistance. The study encompasses both regression and classification tasks. In the regression task, the aim [...] Read more.

This paper evaluates the ability of some state-of-the-art Machine Learning models, namely SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to predict pavement skid resistance. The study encompasses both regression and classification tasks. In the regression task, the aim is to predict the coefficient of friction values, while the classification task seeks to identify three classes of skid resistance: good, intermediate and bad. The dataset used in this work was gathered through an extensive test campaign that involved a fifth-wheel device to measure the coefficient of friction at different slip ratios on different road surfaces, vehicle speeds, tire tread depths and water depths. It was found that the RBF-SVM model, due to its ability to capture non-linear relationships between the features and the target for a relatively small dataset, is the most adapted tool compared with, on one side, MLR, linear SVM and DT models for the regression task and, on the other side, linear SVM and DT models for the classification task. The paper also discusses the strengths and weaknesses of the investigated models based on the underlying physical phenomena related to skid resistance. Full article

(This article belongs to the Special Issue Friction Assessment in Pavement Engineering)

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22 pages, 8817 KiB

Open AccessArticle

Study on the Influence of Environmental Conditions on Road Friction Characteristics

by Atsushi Watanabe, Ichiro Kageyama, Yukiyo Kuriyagawa, Tetsunori Haraguchi, Tetsuya Kaneko and Minoru Nishio

Lubricants 2023, 11(7), 277; https://doi.org/10.3390/lubricants11070277 - 26 Jun 2023

Cited by 3 | Viewed by 997

Abstract

This study focuses on changes in the friction characteristics of paved roads under various conditions from the viewpoint of traffic safety. In general, the braking characteristics of road vehicles are examined using the μ–s characteristics of the tires. Therefore, in our research, we [...] Read more.

This study focuses on changes in the friction characteristics of paved roads under various conditions from the viewpoint of traffic safety. In general, the braking characteristics of road vehicles are examined using the μ–s characteristics of the tires. Therefore, in our research, we used three types of limited datasets and identified them using the Magic Formula proposed by Prof. Pacejka. Based on various experiments, it was shown that changes in the road surface environment, such as dry and wet conditions, significantly affect the μ–s characteristics, and this influence varies significantly depending on the pavement conditions. In this study, as the first stage of the analysis of these influences, it was clarified that the difference in the friction characteristics of wet and dry road surfaces varies significantly depending on the pavement surface, which is based on experimental results obtained using actual roads. As this variation is closely related to the safety of actual road traffic, we used a brush model, which is a dynamic model of the road surface and tires, to clarify the differences in this characteristic. Full article

(This article belongs to the Special Issue Friction Assessment in Pavement Engineering)

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22 pages, 4674 KiB

Open AccessArticle

Determination of Safety-Oriented Pavement-Friction Performance Ratings at Network Level Using a Hybrid Clustering Algorithm

by Jieyi Bao, Yi Jiang and Shuo Li

Lubricants 2023, 11(7), 275; https://doi.org/10.3390/lubricants11070275 - 24 Jun 2023

Cited by 1 | Viewed by 1111

Abstract

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a [...] Read more.

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement-friction performance ratings across a statewide road network. A large, safety-oriented dataset is first generated based on the attributes possibly contributing to friction-related crashes. One-, two-, and multi-dimensional clustering analyses are performed to rate pavement friction. After using the Chi-square test, six ratings were identified and validated. These ratings are categorized as (0, 20], (20, 25], (25, 35], (35, 50], (50, 70], and (70, ∞). By effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement-friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management. Full article

(This article belongs to the Special Issue Friction Assessment in Pavement Engineering)

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