Modelling asphalt pavement analyzer rut depth using different statistical techniques

Hussan, Sabahat; Kamal, Mumtaz Ahmed; Hafeez, Imran; Ahmad, Naveed; Khanzada, Shahab; Ahmed, Shafeeq

doi:10.1080/14680629.2018.1481880

Cited by 38 publications

(16 citation statements)

References 41 publications

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“…ANN has already been extensively used in pavement material properties prediction and pavement performance modeling. Hussan utilized nonlinear regression and ANN to predict rutting test results of asphalt mixture based on temperature, aggregate source, aggregate gradation, bitumen penetration values, and number of loading cycles [32]. Yao et al used an DNN with 2 hidden layers and 64 nodes to predict pavement performance with 37 inputs [26].…”

Section: 5mentioning

confidence: 99%

Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

Dong¹,

Chen

Dong

et al. 2021

Preprint

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This study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snow fall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.

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Section: 5mentioning

confidence: 99%

Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

Dong¹,

Chen

Dong

et al. 2021

Preprint

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“…It is a local quarry of limestone. Table 2 shows the qualitative properties of the aggregates obtained from the Margalla Hills [12]. The National Highway Authority Class B (for wearing course), which is specified as a finer gradation, was used for the preparation of the asphalt mixtures.…”

Section: Aggregatesmentioning

confidence: 99%

Experimental Investigation into the Structural and Functional Performance of Graphene Nano-Platelet (GNP)-Doped Asphalt

et al. 2019

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With the increase in the demand for bitumen, it has become essential for pavement engineers to ensure that construction of sustainable pavements occurs. For a complete analysis of the pavement, both its structural and functional performances are considered. In this study, a novel material (i.e., Graphene Nano-Platelets (GNPs)) has been used to enhance both of the types of pavements’ performances. Two percentages of GNPs (i.e., 2% and 4% by the weight of the binder) were used for the modification of asphalt binder in order to achieve the desired Performance Grade. GNPs were homogeneously dispersed in the asphalt binder, which was validated by Scanning Electron Microscope (SEM) images and a Hot Storage Stability Test. To analyze the structural performance of the GNPs-doped asphalt, its rheology, resistance to permanent deformation, resistance to moisture damage, and bitumen-aggregate adhesive bond strength were studied. For the analysis of the functional performance, the skid resistance and polishing effect were studied using a British Pendulum Skid Resistance Tester. The results showed that GNPs improved not only the rutting resistance of the pavement but also its durability. The high surface area of GNPs increases the pavement’s bonding strength and makes the asphalt binder stiffer. GNPs also provide nano-texture to the pavement, which enhances its skid resistance. Thus, we can recommend GNPs as an all-around modifier that could improve not only the structural performance but also the functional performance of asphalt pavements.

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“…With the development of the global economy and the improvement of people’s living standards, the traffic load is increasing. Under the combined influence of natural environment, traffic load, and other factors, the rutting, potholes, and cracks frequently appear, which causes the pavement’s actual service life to be far shorter than the design life [ 2 , 3 , 4 ]. In order to meet the function requirements of petroleum asphalt in road engineering, modified asphalt can be prepared by adding certain modified materials to the asphalt [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Study on Properties of Bone Glue/Polyurethane Composite Modified Asphalt and Its Mixture

Yan

Xie

et al. 2021

Materials

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Composite modification technology is widely used in the materials field. To enhance the property of polyurethane modified asphalt and realize its application in road engineering, the bone glue/polyurethane composite modified asphalt (CMA) was prepared using bone glue, polyurethane, and neat asphalt in this research. The bone glue content ranges 5–10%, that of the polyurethane is 1–5%. The relationship between the modifier’s content and the conventional properties and rheological properties of CMA was revealed by response surface methodology (RSM). The CMA performance was further verified under the optimal content of the bone glue and polyurethane. The differences of properties of styrene–butadienestyrene (SBS) modified asphalt mixture, neat asphalt mixture, and bone glue/polyurethane CMA mixture were compared and analyzed by using the pavement performance test. The results showed that the CMA’s conventional properties and rheological properties are improved. The optimal bone glue content and polyurethane content determined by RSM are 6.848% and 2.759%, respectively. The low-temperature crack resistance and water stability of the CMA mixture are enhanced, better than neat asphalt mixture and SBS modified asphalt mixture. The CMA mixture’s dynamic stability is 85% of SBS modified asphalt mixture, but it is 2.4 times of neat asphalt mixture. The result indicated that the bone glue/polyurethane CMA mixture still has certain advantages of high-temperature stability. In this research, the composite modification of bone glue and polyurethane can significantly enhance the characteristic of asphalt and asphalt mixture and provide a new method for applying and promoting polyurethane modified asphalt in road engineering.

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Modelling asphalt pavement analyzer rut depth using different statistical techniques

Cited by 38 publications

References 41 publications

Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

Classification of Pavement Climatic Regions through Unsupervised and Supervised Machine Learnings

Experimental Investigation into the Structural and Functional Performance of Graphene Nano-Platelet (GNP)-Doped Asphalt

Study on Properties of Bone Glue/Polyurethane Composite Modified Asphalt and Its Mixture

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