Study on the Performances of Waste Crumb Rubber Modified Asphalt Mixture with Eco-Friendly Diatomite and Basalt Fiber

Wang, Wensheng; Cheng, Yongchun; Chen, Heping; Tan, Guojin; Lv, Zehua; Bai, Yunshuo

doi:10.3390/su11195282

Cited by 29 publications

(16 citation statements)

References 36 publications

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“…In this paper, the reasonable dosages of AEA and multimineral admixtures were studied through the orthogonal experimental design [54][55][56]. Considering that the influence of AEA on the frost resistance of concrete is higher than that of multimineral admixtures [31], the AEA content was regarded as the priority parameter and divided into three feasible range levels of air content, i.e., (2.5 ± 0.5)%, (4.5 ± 0.5)%, and (6.5 ± 0.5)%.…”

Section: Mixture Proportion and Specimen Preparationmentioning

confidence: 99%

“…en, the orthogonal experimental design for the dosages of multimineral admixtures at each range level of air content was carried out, which is presented in Table 3. In the orthogonal experimental design, based on the existing literature [54][55][56][57], the dosage of FA or BFS should be controlled within 30% by the total weight of cementitious materials, respectively. Meanwhile, the total dosage of FA and BFS was limited to less than 40% by the total weight of cementitious materials.…”

Section: Mixture Proportion and Specimen Preparationmentioning

confidence: 99%

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Investigation on Durability Behaviour and Optimization of Concrete with Triple-Admixtures Subjected to Freeze-Thaw Cycles in Salt Solution

Wang

Zhu

et al. 2021

Advances in Materials Science and Engineering

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In the seasonal frozen area of northeast China, cement concrete is usually in a working environment of cold climate and chlorine erosion coupling effect. In general, with a reasonable addition of air entraining agent (AEA) and multimineral admixtures such as fly ash, blast furnace slag, and silica fume, the durability of cement concrete under the effects of freeze-thaw and salt solution can be significantly improved in cold regions. However, due to several more compositions of cement concrete with multiple mineral admixtures, it would take excessive trial mixtures to select the desired mixture proportion based on the conventional method. This means a great deal of costs of raw materials and laboratory experimental time. In this paper, the experimental scheme of mixture proportion for air-entrained concrete with multimineral admixtures was designed based on the orthogonal experiment design method. Based on the compressive strength, rapid chloride permeability, and weight loss and relative dynamic elastic modulus after salt freeze-thaw cycles, the influence of different mineral admixtures and their dosages on the durability of concrete subjected to freeze-thaw in salt solution was analyzed. After that, based on genetic algorithm, an optimization of mixture proportion was proposed, which only requires less trial mixes and accessible optimization process. The test results indicated the superiority of air-entrained concrete with multimineral admixtures when serving in salt freeze-thaw environment. Eventually, it was also verified that the optimized concrete in this paper could achieve pleasurable durability performances under salt freeze-thaw cycles.

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Section: Mixture Proportion and Specimen Preparationmentioning

confidence: 99%

Section: Mixture Proportion and Specimen Preparationmentioning

confidence: 99%

Investigation on Durability Behaviour and Optimization of Concrete with Triple-Admixtures Subjected to Freeze-Thaw Cycles in Salt Solution

Wang

Zhu

et al. 2021

Advances in Materials Science and Engineering

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“…Based on Cantabro and draindown tests, the optimal bitumen-aggregate ratios (OBRs) of each group were 3.8%, 3.7%, 3.6%, 3.6%, and 3.5%, respectively [32]. Compared with DGAM, PAM reduces bitumen consumption and engineering cost, owing to its less fine aggregate content [33,34]. Another finding is that the OBR decreases slightly with the increase of the replacement level, which is inconsistent with the results obtained by Kavussi et al [35].…”

Section: Mixture Designmentioning

confidence: 99%

Laboratory Evaluation on the Performance of Porous Asphalt Mixture with Steel Slag for Seasonal Frozen Regions

et al. 2019

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Porous asphalt mixtures with steel slag (PAM-SS), as an eco-friendly and low-cost pavement material, are conducive to addressing the issue of urban floods and natural resource shortages. The primary objective of this paper was to explore the feasibility of the application of PAM-SS for seasonal frozen regions, and ascertain the optimal replacement percentage of natural aggregate. Steel slag coarse aggregate (SSCA) was used to replace basalt coarse aggregate (BCA) at four levels (25%, 50%, 75%, 100%) by equal volume. The volume characteristics, mechanical properties, low-temperature cracking resistance, water stability, and freeze-thaw (F-T) durability of the mixture were assessed. The results indicated that the low-temperature cracking resistance of the mixture was significantly enhanced and acoustic emission (AE) energy was uniformly released by the incorporation of steel slag. Furthermore, the porosity, permeability, Marshall stability (MS), and the resistance against water damage and F-T cycles were also significantly improved. Based on the experimental results, the complete replacement of natural aggregate is advisable to obtain an optimal overall performance.

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“…Diatomite has been proved to dramatically improve the high-temperature performance of asphalt mixture [49][50][51][52]. Hence, the incorporation of diatomite and basalt fibres would comprehensively enhance the resistance to high-temperature rutting, low-temperature cracking as well as moisture stability of asphalt mixture.…”

Section: Case Study Ii: Tri-objective Optimizationmentioning

confidence: 99%

Machine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion

Liang

Chen

et al. 2021

Applied Sciences

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Asphalt mixture proportion design is one of the most important steps in asphalt pavement design and application. This study proposes a novel multi-objective particle swarm optimization (MOPSO) algorithm employing the Gaussian process regression (GPR)-based machine learning (ML) method for multi-variable, multi-level optimization problems with multiple constraints. First, the GPR-based ML method is proposed to model the objective and constraint functions without the explicit relationships between variables and objectives. In the optimization step, the metaheuristic algorithm based on adaptive weight multi-objective particle swarm optimization (AWMOPSO) is used to achieve the global optimal solution, which is very efficient for the objectives and constraints without mathematical relationships. The results showed that the optimal GPR model could describe the relationship between variables and objectives well in terms of root-mean-square error (RMSE) and R2. After the optimization by the proposed GPR-AWMOPSO algorithm, the comprehensive pavement performances were enhanced in terms of the permanent deformation resistance at high temperature, crack resistance at low temperature as well as moisture stability. Therefore, the proposed GPR-AWMOPSO algorithm is the best option and efficient for maximizing the performances of composite modified asphalt mixture. The GPR-AWMOPSO algorithm has advantages of less computational time and fewer samples, higher accuracy, etc. over traditional laboratory-based experimental methods, which can serve as guidance for the proportion optimization design of asphalt pavement.

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Study on the Performances of Waste Crumb Rubber Modified Asphalt Mixture with Eco-Friendly Diatomite and Basalt Fiber

Cited by 29 publications

References 36 publications

Investigation on Durability Behaviour and Optimization of Concrete with Triple-Admixtures Subjected to Freeze-Thaw Cycles in Salt Solution

Investigation on Durability Behaviour and Optimization of Concrete with Triple-Admixtures Subjected to Freeze-Thaw Cycles in Salt Solution

Laboratory Evaluation on the Performance of Porous Asphalt Mixture with Steel Slag for Seasonal Frozen Regions

Machine Learning Approach to Develop a Novel Multi-Objective Optimization Method for Pavement Material Proportion

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