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.
2,5-Furandicarboxylic acid (FDCA) is a value added chemical that can be used as a polymer building block for the synthesis of biobased polymers.
Nanomaterials have a great potential for enhancing the performance of base asphalt binder. This study aims to promote the application of nano-TiO2/CaCO3 in bitumen and presents a study on rheological properties for TiO2/CaCO3 nanoparticle-bitumen. In this study, a series of laboratory experiments have been performed for bitumen with different nano-TiO2/CaCO3 dosages. Nano-TiO2/CaCO3-modified bitumen with optimum dosage was prepared for viscosity, dynamic shear rheometer (DSR), and beam bending rheometer (BBR) for assessing temperature sensitivity of bitumen, and the low-medium-high-temperature performances were analyzed for TiO2/CaCO3 nanoparticle-bitumen as well. Results show that bituminous mechanical properties were enhanced by TiO2/CaCO3, and based on the overall desirability analysis of various conventional tests, the reasonable dosage of nano-TiO2/CaCO3 was recommended as 5% by weight of base bitumen. Adding nano-TiO2/CaCO3 was beneficial to improve the viscosity and reduce the temperature sensitivity of bitumen. The capacities of bituminous rutting resistance as well as medium-temperature fatigue resistance were enhanced by the addition of nano-TiO2/CaCO3. However, BBR test shows that bituminous anticracking is reduced slightly. On this basis, the Burgers model is selected for clarifying the decrease in anticracking performance; that is, nano-TiO2/CaCO3 increased the stiffness modulus while increasing the viscosity of bitumen.
Automobile exhaust pollution is a serious problem that restricts urban development, and it poses a serious threat to people’s lives and health and even the climate. At present, the treatment of automobile exhaust has attracted people’s attention, and numerous works have been focused on it thereafter. The purpose of the present study is to drive TiO2 nanoparticles application into pavement, and the study present an experimental investigation of performances and automobile exhaust purification of asphalt and its mixture modified by nano-TiO2. In this work, a series of rheometer properties and pavement performances were studied, including penetration, softening point, ductility, DSR and BBR for asphalt binder, conventional pavement performances, and creep test for asphalt mixture. Moreover, the photocatalytic degradation test of automobile exhaust was conducted to assess degradation of TiO2 nanoparticles in the asphalt mixture on automobile exhaust. Results indicate that the TiO2 nanoparticle was beneficial to increase the viscosity and reduce the temperature sensitivity, which would enhance its high-temperature stabilization capability of asphalt. Meanwhile, nano-TiO2 can significantly enhance the rheometer properties of asphalt and its capacity of high-temperature antirutting, and its low-temperature performance could also comply with the specification. Besides, the incorporation of nano-TiO2 in mixtures could effectively enhance the antirutting and anticracking as well as water stabilization. Moreover, the nano-TiO2-modified asphalt mixture possesses a positive impact on photocatalytic degradation of CH and NOx, which could provide a reference for the treatment of automobile exhaust. The photocatalytic degradation effect of asphalt mixtures modified by nano-TiO2 on NOx is significantly better than that of CH.
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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