Gradation optimization and strength mechanism of aggregate structure considering macroscopic and mesoscopic aggregate mechanical behaviour in porous asphalt mixture

Ren, Jiaolong; Xu, Yan; Huang, Jiandong; Wang, Yi; Jia, Zhen

doi:10.1016/j.conbuildmat.2021.124262

Cited by 37 publications

(16 citation statements)

References 37 publications

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“…ITT was performed on universal testing machine (UTM, IPC Global, Melbourne, Victoria, Austrilia), and a diametral line load was applied on specimens with a displacement of 50 mm/min until the maximum failure strength was reached. The ITS was calculated based on Equation (1). ITT was performed under dry and wet conditions, and TSR was calculated with Equation (2).…”

Section: Ittmentioning

confidence: 99%

“…Porous asphalt (PA) mixtures are formed by an open graded asphalt mixture with a high air void (AV) content (i.e., 18~22%) and an open graded skeleton. PA mixtures have the benefits of improving drive safety, mitigation of spray and hydroplaning, and noise reduction [ 1 , 2 ]. Due to the frequent and direct exposure to water, PA mixtures are more sensitive to moisture damage, and the distresses of raveling and stripping caused by moisture damage are the main concerns during the application of PA mixtures [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Experimental Investigation of Moisture Sensitivity and Damage Evolution of Porous Asphalt Mixtures

Wang

Zheng

et al. 2021

Materials

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Porous asphalt (PA) mixtures are designed with a high air void (AV) (i.e., 18~22%) content allowing rainwater to infiltrate into their internal structures. Therefore, PA mixtures are more sensitive to moisture damage than traditional densely graded asphalt mixtures. However, the moisture damage evolution of PA mixtures is still unclear. The objective of this study was to investigate the moisture damage evolution and durability damage evolution of PA mixtures. The indirect tensile test (ITT), ITT fatigue test, and Cantabro loss test were used to evaluate the moisture sensitivity and durability of PA mixtures, and a staged ITT fatigue test was developed to investigate the damage evolutions under dry and wet conditions. Indirect tensile strength (ITS), fatigue life, indirect tensile resilience modulus (E), and durability decreased with the increment of moisture damage and loading cycles. The fatigue life is more sensitive to the moisture damage. The largest decrements in ITS and E were found in the first 3000 loading cycles, and PA mixtures tended to fail when the decrement exceeded 60%. Damage factors based on the ITS and E are proposed to predict the loading history of PA mixtures. The durability damage evolution and damage factors could fit an exponential model under dry conditions. Moisture had a significant influence and an acceleration function on the moisture damage evolution and durability damage evolution of PA mixtures.

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Section: Ittmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Moisture Sensitivity and Damage Evolution of Porous Asphalt Mixtures

Wang

Zheng

et al. 2021

Materials

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“…The relationship between the UCS of concrete and the proportion of the admixture is non-linear, so it cannot be simply calculated by a mathematical formula [ 36 , 37 ]. Many researchers have proposed the use of machine learning to achieve more efficient optimization of the UCS of concrete [ 10 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. Zhang et al proposed a new self-organizing fuzzy neural network (SOFNN) method based on clustering and extreme learning machine (ELM) optimization to overcome the fact that traditional machine learning models are difficult for engineers to understand when predicting the UCS of concrete.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Zhu

Zhou

et al. 2022

Materials

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Cement-slag concrete has become one of the most widely used building materials considering its economical advantage and satisfying uniaxial compressive strength (UCS). In this study, an AI-based method for cement-slag concrete design was developed based on the balance of economic and mechanical properties. Firstly, the hyperparameters of random forest (RF), decision tree (DT), and support vector machine (SVM) were tuned by the beetle antennae search algorithm (BAS). The results of the model evaluation showed the RF with the best prediction effect on the UCS of concrete was selected as the objective function of UCS optimization. Afterward, the objective function of concrete cost optimization was established according to the linear relationship between concrete cost and each mixture. The obtained results showed that the weighted method can be used to construct the multi-objective optimization function of UCS and cost for cement-slag concrete, which is solved by the multi-objective beetle antennae search (MOBAS) algorithm. An optimal concrete mixture ratio can be obtained by Technique for Order Preference by Similarity to Ideal Solution. Considering the current global environment trend of “Net Carbon Zero”, the multi-objective optimization design should be proposed based on the objectives of economy-carbon emission-mechanical properties for future studies.

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“…They are widely used in the construction industry because of their early strength, high strength, high mobility, strong durability, and other characteristics [ 3 , 4 , 5 , 6 , 7 ]. A large amount of CO 2 is generated during the cement configuration process, which brings a great burden to the environment [ 8 , 9 , 10 , 11 ]. To reduce resource consumption and ease the burden of carbon emissions on the environment, researchers are looking into replacing some cement with active materials such as fly ash and silica fume [ 5 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

et al. 2022

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Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio.

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Gradation optimization and strength mechanism of aggregate structure considering macroscopic and mesoscopic aggregate mechanical behaviour in porous asphalt mixture

Cited by 37 publications

References 37 publications

Experimental Investigation of Moisture Sensitivity and Damage Evolution of Porous Asphalt Mixtures

Experimental Investigation of Moisture Sensitivity and Damage Evolution of Porous Asphalt Mixtures

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

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