Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence

Hassan, Md. Rafiul; Mamun, A. A.; Hossain, Muhammad Imtiaz; Arifuzzaman, Md

doi:10.1155/2018/7525789

Cited by 19 publications

(10 citation statements)

References 39 publications

(37 reference statements)

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“…e durability of the pavement dictates the ability to withstand the effects of different environmental conditions without further deterioration over a long period of time under traffic loads. While asphalt pavement passes its service life in different environmental conditions, moisture plays a significant role in asphalt pavement failure by altering the adhesion between asphalt binder and aggregate [30][31][32].…”

Section: Durabilitymentioning

confidence: 99%

Evaluation of Waste Engine Oil‐Rejuvenated Asphalt Concrete Mixtures with High RAP Content

Mamun

Wahhab

2018

Advances in Materials Science and Engineering

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The use of large proportions of reclaimed asphalt pavement is necessary to meet the increasing demand for road construction materials in a sustainable way. One of the challenges of using a greater percentage of reclaimed asphalt pavement (>30%) is the greater stiffness of mixes incorporating it. While this stiffness problem is usually resolved by using different commercial rejuvenators, there are circumstances in which commercial rejuvenators are not available. Therefore, this study evaluates the potential of using waste engine oil as a substitute for commercial rejuvenators for the higher percentage of reclaimed asphalt pavement that could meet the increasing demand in a more sustainable way. To assess the possibility of using a higher percentage of reclaimed asphalt pavement in road construction, different percentages of reclaimed asphalt pavement (30%, 40%, and 50%) are used. Following the property of the aged binder, three different percentages (7%, 13%, and 20%) of waste engine oil are considered. Each percent of waste engine oil is incorporated with one of the three mixes. The mixes (with the minimum required Marshall criteria) are evaluated for different properties, namely, their indirect tensile strength, resilient modulus, and durability. Finally, those properties are compared to those of the mixes rejuvenated by commercial rejuvenators. It is observed that, for the aforementioned properties, 7% to 13% of waste engine oil is identical to the commercial rejuvenator for the mixes with 30% to 40% of reclaimed asphalt pavement.

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Section: Durabilitymentioning

confidence: 99%

Evaluation of Waste Engine Oil‐Rejuvenated Asphalt Concrete Mixtures with High RAP Content

Mamun

Wahhab

2018

Advances in Materials Science and Engineering

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“…It can be observed that ensemble learning resulted in a small improvement in the accuracy, compared to the respective best ANN model, in all the cases. The test accuracies of this study were compared with another similar study carried out by Hassan et al [39]. In doing so, it was found that the accuracies of this study's models on test datasets for aged and moisture-damaged samples were slightly better.…”

Section: Resultsmentioning

confidence: 66%

Behavioral Investigation of Single Wall and Double Wall CNT Mixed Asphalt Adhesion Force Using Chemical Force Microscopy and Artificial Neural Networks

et al. 2022

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Flexible pavement deterioration due to moisture intrusion and aging is the key concern worldwide for highway engineers. However, this damage has not been properly investigated in detail due to lack of appropriate experimental and modeling techniques. Such lacking hinders the design of long-lasting pavements, as the effects of environmental damages are unknown, especially for modified asphalt. Therefore, the current study aims at determining a better approach for modeling asphalt adhesion damage using Artificial Neural Networks (ANNs). The Atomic Force Microscopy (AFM) test was deployed to determine the adhesion and cohesion forces of asphalt samples with varying contents of polymer and Antistripping Agents (ASAs). Two types of ANN models, namely multilayer perceptions (MLPs) and radial basis function neural network (RBFNN), were used in this effort. Two popular modifications, namely ensemble learning and hierarchical modeling, were also engaged to achieve convenient and accurate damage models. The analysis found that RBFNN was better suited for hierarchical models than MLP. RBFNN is preferred for aged and moisture-damaged samples which have less variation in their datasets. Hierarchical models are convenient to apply as they can be applied to any type of asphalt sample. However, they produced a small reduction in accuracy (less than 10%) as compared to other models. The accuracy of the hierarchical model was found to be satisfactory. The ensemble learning approach showed slight improvement in accuracy for all models ranging between 1–3%, i.e., 6–8 nN. This study recommends the use of hierarchical models, developed with ensemble learning, for prediction of asphalt damage. The results of the study will be helpful for researchers and practitioners working on pavement materials for developing prediction models to prepare a better mix design of polymer modified asphalt.

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“…One of the significant environmental factors that jeopardize the performance of the flexible pavement is moisture [34]. Of the numerous numbers of distress that an asphalt pavement experiences during its service life, moisture is regarded as one of the significant factors that subsequently results in the failure of asphalt pavement [35,36]. The safe performance of a structure for the specified life expectancy depends on increasing durability against water-induced damage.…”

Section: Durabilitymentioning

confidence: 99%

Comparative Evaluation of Waste Cooking Oil and Waste Engine Oil Rejuvenated Asphalt Concrete Mixtures

2020

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In road construction, different types of waste oil have been recommended to overcome the stiffening effect of reclaimed asphalt pavement content. However, the selection of an effective rejuvenator based on a comparative study can lead to using the resources more efficiently. In this study, waste cooking oil and waste engine oil are used to rejuvenate three different percentages (30%, 40%, and 50%) of reclaimed asphalt pavement following the current maximum industrial adaptability. The waste oil rejuvenated mixtures are compared to the fresh mixture, and mixtures rejuvenated with commercial rejuvenator. The moisture sensitivity, indirect tensile strength, and resilient modulus of the various asphalt mixtures are analyzed. Based on the statistical analyses and overall ranking, it is concluded that 7% of waste engine oil performs better till 40% of reclaimed asphalt pavement, whereas 13% of waste cooking oil can be used till 50% of reclaimed asphalt pavement.

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Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence

Cited by 19 publications

References 39 publications

Evaluation of Waste Engine Oil‐Rejuvenated Asphalt Concrete Mixtures with High RAP Content

Evaluation of Waste Engine Oil‐Rejuvenated Asphalt Concrete Mixtures with High RAP Content

Behavioral Investigation of Single Wall and Double Wall CNT Mixed Asphalt Adhesion Force Using Chemical Force Microscopy and Artificial Neural Networks

Comparative Evaluation of Waste Cooking Oil and Waste Engine Oil Rejuvenated Asphalt Concrete Mixtures

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