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This work aims to explore optimization methods for the design of earthen buildings in rural Fujian to achieve low-carbon emissions and improve the structural stability of earthen buildings. First, parametric modeling and optimization algorithms are employed through the Grasshopper platform. An intelligent earthen building design is created by combining the optimization of factors such as the structure of earthen buildings, building materials, and orientation. Then, a comparison is made with the unoptimized, energy-efficient, and carbon emission reduction designs. Finally, the work concludes that the proposed design significantly optimizes the total carbon emissions, energy consumption, structural stability, and economic aspects. The proposed design scheme achieves the highest carbon emission reduction effect, with a reduction rate of 34.64%. The proposed design exhibits lower maximum stress and higher minimum safety factor in terms of structural stability compared to other scenarios, along with smaller structural displacement. It also performs well in terms of initial investment, annual operating costs, and construction period. The significance of this work lies in providing scientific guidance for the design and construction of rural earthen buildings, promoting the organic integration of rural development with low-carbon initiatives. This indicates that the use of intelligent optimization methods for earthen building design is feasible and can yield positive results in practice.

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Weld line defects, commonly occurring during the plastic product manufacturing process, are caused by the merging of two opposing streams of molten plastic. The presence of weld lines harms the product's aesthetic appeal and durability. This study uses artificial neural networks to forecast the ultimate tensile strength of a PA6 composite incorporating 30% glass fibers (GFs). Data were collected from tensile strength tests and the technical parameters of injection molding. The packing pressure factor is the one that significantly affects the tensile strength value. The melt temperature has a significant impact on the product's strength as well. In contrast, the filling time factor has less impact than other factors. According to the scanning electron microscope result, the smooth fracture surface indicates the weld line area's high brittleness. Fiber bridging across the weld line area is evident in numerous fractured GF pieces on the fracture surface, which enhances this area. Tensile strength values vary based on the injection parameters, from 65.51 MPa to 73.19 MPa. In addition, the experimental data comprise the outcomes of the artificial neural networks (ANNs), with the maximum relative variation being only 4.63%. The results could improve the PA6 reinforced with 30% GF injection molding procedure with weld lines. In further research, mold temperature improvement should be considered an exemplary method for enhancing the weld line strength.

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Nowadays, what captures consumers' primary attention is how to purchase electric vehicles with long range and desirable price. Lightweight construction stands as one of the most effective approaches for prolonging range and lowering costs. As a consequence, it is particularly imperative to undertake lightweight design optimization for the battery bracket of new energy vehicles by applying 3D printing technology. To actualize this goal, Rhino software was initially employed for 3D modeling to design the battery bracket system for a pure electric vehicle in China. Subsequently, topology optimization design of the battery bracket was carried out by adopting Altair Inspire software. Last but not least, manufacturing and assembly inspection were completed using a 3D printer. The results show that the maximum displacement of the battery lower tray bracket after topology optimization is 3.20 mm, which is slightly higher than before, but still relatively small. The maximum Mises equivalent stress rose to 240.7 MPa post-optimization, but brought about a uniform stress distribution at the bottom of the bracket. In comparison, the minimum factor of safety met design requirements at 1. The mass was lessened to 0.348 kg, representing a 49.2% decrease in comparison with pre-optimization levels. The 3D-printed bracket was fabricated by employing a 3D printer, thereby achieving the aforementioned mass abatement. The battery pack parts exhibited a bright surface with low roughness and no discernible warping or deformation defects. As revealed by the assembly results, the components of the battery pack bracket are tightly coordinated with each other, with no evident assembly conflicts, revealing that the dimensional accuracy and fit of the completed parts meet production requirements. These findings lay solid groundwork for the mass production of high-performance battery pack brackets.

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Predicting and optimizing the mechanical performance of the helically wound nylon-reinforced rubber fertilizer hose (HWNR hose) is crucial for enhancing the performance of hose pumps. This study aims to enhance the service life of HWNR hoses and the efficiency of liquid fertilizer transport. First, a finite element simulation model and a mathematical model were established to analyze the influence of fiber layer arrangement on the maximum shear strain on the coaxial surface (MSS) and the reaction force on the extrusion roller (RF). For the first time, the Crested Porcupine Optimizer algorithm was used to improve the Generalized Regression Neural Network (CPO-GRNN) method to establish a surrogate model for predicting the mechanical properties of HWNR hoses, and it was compared with Response Surface Methodology (RSM). Results showed CPO-GRNN's superiority in handling complex nonlinear problems. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed for optimization design. Compared to the original HWNR hose with an MSS of 0.906 and an RF of 30,376N, the optimized design reduced the MSS by 7.99% and increased the RF by 2.46%, significantly enhancing their service life and liquid fertilizer transport capacity. However, further research on fatigue damage is needed.

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An air spring (AS) for ships must have the structural strength of its bellows enhanced considerably to ensure its reliability under high internal pressure and strong impact. In this case, the stiffness of the bellows gradually dominates the overall stiffness of the AS. Nevertheless, the parameterization calculation of stiffness for an AS mainly focuses on its pneumatic stiffness. The bellows stiffness is normally analyzed by virtue of equivalent simplification or numeric simulation. There is not an effective parameterization calculation model for the stiffness of the bellows, making it difficult to achieve the structural optimization design of the bellows. In this paper, the shell theory was borrowed to build a mechanical model for the bellows. Subsequently, the state vector of the bellows was solved by precision integration and boundary condition. Iteration was conducted to identify the complex coupling relationship between the vector of the bellows and other parameters. On this basis, the parameterization calculation method was introduced for the stiffness of the bellows to obtain the vertical and horizontal stiffness of the AS. After that, a dual-membrane low-stiffness structure was designed to analyze the dominating factors affecting the strength and stiffness of the AS, which highlighted the way to the low-stiffness optimization design of high-strength ASs. In the end, three prototypes and one optimized prototype were tested to verify the correctness of the parameterization design model for stiffness as well as the effectiveness of the structural optimization design.

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BACKGROUND: Blood clots are composed of aggregated fibrin and platelets, and thrombosis is the body's natural response to repairing injured blood vessels or stopping bleeding. However, when this process is activated abnormally, such as in a mechanical blood pump, it can lead to excessive thrombus formation. Therefore, how to avoid or reduce the probability of thrombus formation is an important indicator of the stable operation of a blood pump. METHODS: In this paper, Lagrangian particle tracking trajectories are simulated to study platelet transport in a blood pump. The design of the thrombus blood pump was optimized using an orthogonal design method based on three factors: inlet angle, outlet angle, and blade number. The effect of blood pump pressure, rotational speed, impeller outlet angle, inlet angle, and number of blades on thrombus formation was analysed using Fluent software. The thrombogenic potential was derived by analyzing the trajectory and flow parameters of platelet particles in the blood pump, as well as the statistical parameters of residence time and stress accumulation thrombus in the platelet pump. RESULTS: When the impeller inlet angle is 30°, the outlet angle is 20°, and the number of blades is 6, the probability of thrombus formation is minimized in the orthogonal design method, aligning with the requirements for blood pump performance. CONCLUSIONS: These design parameters serve as a numerical guideline for optimizing the geometry of the semi-open impeller in blood pumps and provide a theoretical foundation for subsequent in vitro experiments.

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A new magnetic nano gel (MNG) was prepared from choline chloride/phenol deep eutectic solvent and magnetic amberlite XAD-7 nanocomposite. The dispersive solid phase micro extraction (dSPME) method was developed for seperation and preconcentration of Brilliant Blue FCF (BB) by the prepared MNG. In this study, firstly, the optimum DES type and mole ratio of DES were investigated before response surface methodology optimization. Then, the effect of the MNG-dSPME experimental parameters were optimized by response surface methodology using central composite design. Under the optimum microextraction conditions, limit of detection (LOD), limit of quantification (LOQ), preconcentration factor (PF), enhencament factor (EF) were found to be 1.15 µg L-1,3.80 µg L-1, 70, and 88, respectively. It was seen that the recovery of real samples were obtained from 95.5 to 103.6%. The pesent method was succesfully for extraction of BB in some food, personal care samples, to the best of our knowledge, this is the first study that is presented method on determination of BB by preconcentration with magnetic nano gel. The obtained results showed that the present procedure is effective, sensitive, and has high accuracy for the quantitative detection of BB.

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The optimal design of groundwater circulation wells (GCWs) is challenging. The key to purifying groundwater using this technique is its proficiency and productivity. However, traditional numerical simulation methods are limited by long modeling times, random optimization schemes, and optimization results that are not comprehensive. To address these issues, this study introduced an innovative approach for the optimal design of a GCW using machine learning methods. The FloPy package was used to create and implement the MODFLOW and MODPATH models. Subsequently, the formulated models were employed to calculate the characteristic indicators of the effectiveness of the GCW operation, including the radius of influence (R) and the ratio of particle recovery (Pr). A detailed collection of 3000 datasets, including measures of operational efficiency and key elements in machine learning, was meticulously compiled into documents through model execution. The optimization models were trained and evaluated using multiple linear regression (MLR), artificial neural networks (ANN), and support vector machines (SVM). The models produced by the three approaches exhibited notable correlations between anticipated outcomes and datasets. For the optimal design of circulating well parameters, machine learning methods not only improve the optimization speed, but also expand the scope of parameter optimization. Consequently, these models were applied to optimize the configuration of the GCW at a site in Xi'an. The optimal scheme for R (Q = 293.17 m3/d, a = 6.09 m, L = 7.28 m) and optimal scheme for Pr (Q = 300 m3/d, a = 3.64 m, L = 1 m) were obtained. The combination of numerical simulations and machine learning is an effective tool for optimizing and predicting the GCW remediation effect.

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The catalytic conversion of carbon dioxide is one of the important ways to achieve the goal of carbon neutralization, which can be further divided into electrocatalysis, thermal catalysis, and photocatalysis. Although photocatalysis and electrocatalysis have the advantages of mild reaction conditions and low energy consumption, the thermal catalytic conversion of CO2 has larger processing capacity, better reduction effect, and more complete industrial foundation, which is a promising technology in the future. During the development of new technology from laboratory to industrial application, ensuring the safety of production process is essential. In this work, safety optimization design of equipment, safety performance of catalysts, accident types, and their countermeasures in the industrial applications of CO2 to methanol are reviewed and discussed in depth. Based on that, future research demands for industrial process safety of CO2 to methanol were proposed, which provide guidance for the large-scale application of CO2 thermal catalytic conversion technology.

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PURPOSE: The primary objective of this study was to optimize formulation variables and investigate the in vitro characteristics of fluticasone propionate (FP)-loaded mixed polymeric micelles, which were composed of depolymerized chitosan-stearic acid copolymer (DC-SA) in combination with either tocopheryl polyethylene glycol succinate or dipalmitoylphosphatidylcholine for pulmonary drug delivery. METHODS: A D-optimal design was employed for the optimization procedure, considering lipid/ polymer ratio, polymer concentration, drug/ polymer ratio, and lipid type as independent variables. Dependent variables included particle size, polydispersion index, zeta potential, drug encapsulation efficiency, and loading efficiency of the polymeric micelles. Additionally, the nebulization efficacy and cell viability of the optimal FP-loaded DC-SA micellar formulations were evaluated. RESULTS: The mixed polymeric micelles were successfully prepared with properties falling within the desired ranges, resulting in four optimized formulations. The release of FP from the optimal systems exhibited a sustained release profile over 72 hours, with 70% of the drug still retained within the core of the micelles. The nebulization efficiency of these optimal formulations reached up to 63%, and the fine particle fraction (FPF) ranged from 41% to 48%. Cellular viability assays demonstrated that FP-loaded DC-SA polymeric micelles exhibited lower cytotoxicity than the free drug but were slightly more cytotoxic than empty mixed micelles. CONCLUSION: In conclusion, this study suggests that DC-SA/ lipid mixed micelles have the potential to serve as effective carriers for nebulizing poorly soluble FP.

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This study aims to enhance the productivity of high-voltage transmission line insulators and their operational safety by investigating their failure mechanisms under ultimate load conditions. Destructive tests were conducted on a specific type of insulator under ultimate load conditions. A high-speed camera was used to document the insulator's failure process and collect strain data from designated points. A simulation model of the insulator was established to predict the effects of ultimate loads. The simulation results identified a maximum first principal stress of 94.549 MPa in the porcelain shell, with stress distribution characteristics resembling a cantilever beam subjected to bending. This implied that the insulator failure occurred when the stress reached the bending strength of the porcelain shell. To validate the simulation's accuracy, bending and tensile strength tests were conducted on the ceramic materials constituting the insulator. The bending strength of the porcelain shell was 100.52 MPa, showing a 5.6% variation from the simulation results, which indicated the reliability of the simulation model. Finally, optimization designs on the design parameters P1 and P2 of the insulator were conducted. The results indicated that setting P1 to 8° and P2 to 90.062 mm decreased the first principal stress of the porcelain shell by 47.6% and Von Mises stress by 31.6% under ultimate load conditions, significantly enhancing the load-bearing capacity. This research contributed to improving the production yield and safety performance of insulators.

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The quality pioneer Dr. Joseph M. Juran first proposed the idea of quality by design. According to him, pharmaceutical quality by design is an organised approach to product development that starts with predetermined goals and places an emphasis on product, process understanding, control based on reliable science and quality risk management. The quality of a product or process can typically be affected by a number of input elements. Design of experiments has been employed widely recently to understand the impacts of multidimensional and interactions of input parameters on the output responses of analytical procedures and pharmaceutical goods. Depending on the design of experiments objectives, screening, characterization, or optimization of the process and formulation, a variety of designs, such as factorial or mixture, can be used. The most popular designs used in the stage of screening or factor selection are the 2-Level Factorial and Plackett-Burman designs, both of which have two levels for each factor (k), both economical and effective, and in optimization widely used designs in this step are full factorial at three levels, central composite, Box-Behnken design. The analysis of variance, regression significance, and lack of fit of the regression model were some of the key topics covered in the discussion of the main components of multiple regression model adjustment. Design of experiments is thus the primary element of the formulation and analytical quality by design. The details about design of experiments used for the analysis of pharmaceutical formulation using HPLC.

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BACKGROUND: There continues to be a need for COVID-19 testing that is pragmatic, community-centered, and sustainable. This study will refine and test implementation strategies prioritized by community partners: (1) walk-up no-cost testing, (2) community health worker (promotores)-facilitated testing and preventive care counseling, (3) vending machines that dispense no-cost, self-testing kits. METHODS: A co-designed Theory of Change from an earlier study phase and the Practical, Robust Implementation and Sustainment Model (PRISM) will guide the study design, measures selection, and evaluation. The first aim is to refine and operationalize a multi-component implementation strategy bundle and outcome measures for COVID-19 testing. A Community and Scientific Advisory Board (CSAB) will be established and include community members, clinical providers/staff from the partnering Federally Qualified Health Center (FQHC), public health researchers, policymakers, and a county health department ambassador. Engagement of CSAB members will be assessed through structured ethnography and a survey about the quality and quantity of engagement practices. The second aim is to implement and evaluate the impact of the implementation strategy bundle to optimize COVID-19 testing in communities using a roll-out implementation optimization (ROIO) design. Seven thousand and five hundred community members will be enrolled across four FQHC clinics over 18 months. Participants will be invited to complete an electronic survey about their demographics, health, and COVID-19 testing results and experiences. CSAB members and clinic partners will participate in PRISM fit and determinant assessments prior to each clinic rollout and post-trial. Interviews will be conducted with 60 community participants and 12 providers/staff following a 3-month rollout period at each clinic, inquiring about their experiences with the implementation strategies. Quantitative data will be analyzed using hierarchical multilevel models to determine the impact of implementation strategies. Qualitative data will be analyzed using rapid qualitative approaches to summarize implementation experiences and identify necessary changes prior to subsequent rollouts. A matrix approach will be used to triangulate data from quantitative and qualitative sources based on PRISM domains. DISCUSSION: This is one of the first pragmatic implementation trials to use a ROIO design and aims to co-create a sustainable and equitable COVID-19 testing program. Findings are likely to generalize to other public health prevention efforts. TRIAL REGISTRATION: NCT05894655 March 2, 2023.

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This study aims to estimate the impact of sewing thread patterns on changes in the resistance of conductive yarns coated with silver paste. Firstly, the structure of the conductive yarns was examined, and various variations in the length and angle of individual sewing stitches were observed and analyzed through experiments. The results revealed that as the length of an individual stitch decreased, the width of the conductive yarn increased. Additionally, variations in the stitch angle resulted in different resistance values in the conductive yarn. These findings provide essential information for optimizing sewing patterns and designing components. Secondly, the comparison between models using multiple linear regression analysis and sewing neural networks was included to show optimized resistance prediction. The multiple linear regression analysis indicated that the stitch length and angle were significant variables affecting the resistance of the conductive thread. The artificial neural network model results can be valuable for optimizing sewing patterns and controlling resistance in various applications that utilize conductive thread. In addition, understanding the resistance variation in conductive thread according to sewing patterns and using optimized models to enhance component performance provides opportunities for innovation and progress. This research is necessary for the textile industry and materials engineering fields and holds high potential for practical applications in industrial settings.

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The main aim of this paper is to explore new approaches to structural design and to solve the problem of lightweight design of structures involving multivariable and multi-objectives. An integrated optimization design methodology is proposed by combining intelligent optimization algorithms with generative design. Firstly, the meta-model is established to explore the relationship between design variables, quality, strain energy, and inherent energy. Then, employing the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the optimal frameworks of the structure are sought within the entire design space. Immediately following, a structure is rebuilt based on the principle of cooperative equilibrium. Furthermore, the rebuilt structure is integrated into a generative design, enabling automatic iteration by controlling the initial parameter set. The quality and rigidity of the structure under different reconstructions are evaluated, resulting in solution generation for structural optimization. Finally, the optimal structure obtained is validated. Research outcomes indicate that the quality of structures generated through the comprehensive optimization method is reduced by 27%, and the inherent energy increases by 0.95 times. Moreover, the overall structural deformation is less than 0.003 mm, with a maximum stress of 3.2 MPa-significantly lower than the yield strength and meeting industrial usage standards. A qualitative study and analysis of the experimental results substantiate the superiority of the proposed methodology for optimized structural design.

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As calcium silicate hydrate (C-S-H) is the main binding phase in concrete, understanding the doping behavior of impurity elements in it is important for optimizing the structure of cementitious materials. However, most of the current studies focus on cement clinker, and the doping mechanism of impurity elements in hydrated calcium silicate is not yet fully understood. The hydrated calcium silicate component is complex, and its structure is very similar to that of the tobermorite mineral family. In this study, the effects of three different dopants (Mg, Sr and Ba) on a representing structure of C-S-H-tobermorite-was systematically explored using densify functional theory (DFT) calculations. The calculations show that Mg doping leads to a decrease in lattice volume and causes obvious structure and coordination changes of magnesium-oxygen polyhedra. This may be the reason why high formation energy is required for the Mg-doped tobermorite. Meanwhile, doping only increases the volume of the Sr- and Ba-centered oxygen polyhedra. Specifically, the Mg-doped structure exhibits higher chemical stability and shorter interatomic bonding. In addition, although Mg doping distorts the structure, the stronger chemical bonding between Mg-O atoms also improves the compressive (~1.99% on average) and shear resistance (~2.74% on average) of tobermorillonite according to the elastic modulus and has less effect on the anisotropy of the Young's modulus. Our results suggest that Mg doping is a promising strategy for the optimized structural design of C-S-H.

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The main challenge in a polymer coextrusion process is to have a good die design prior to the process, which can minimize the geometric errors that are caused by extrusion swell and interface motion. For this purpose, a coupling method of optimization and inverse design for a coextrusion die was studied for a medical striped catheter. In the study, the main material was thermoplastic polyurethane (TPU), and the auxiliary material was TPU filled with 30 wt% barium sulfate. An overall optimization design method was used to optimize the geometry of the extrusion die channel for the striped catheter, which had a complex geometry. In the global optimization process, the local inverse design method was used to design the inlet of the auxiliary material. The non-linear programming by quadratic Lagrangian (NLPQL) algorithm was used to obtain the optimal geometric solution of the coextrusion die runner. The experimental verification results showed that the coupling method for coextrusion die design improved the design efficiency of the coextrusion die remarkably. The value of the objective function, which was used to measure the geometric error of the product, was reduced by 72.3% compared with the initial die design.

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The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm's convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability.

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Textile-based Joule heaters in combination with multifunctional materials, fabrication tactics, and optimized designs have changed the paradigm of futuristic intelligent clothing systems, particularly in the automobile field. In the design of heating systems integrated into a car seat, conductive coatings via 3D printing are expected to have further benefits over conventional rigid electrical elements such as a tailored shape and increased comfort, feasibility, stretchability, and compactness. In this regard, we report on a novel heating technique for car seat fabrics based on the use of smart conductive coatings. For easier processes and integration, an extrusion 3D printer is employed to achieve multilayered thin films coated on the surface of the fabric substrate. The developed heater device consists of two principal copper electrodes (so-called power buses) and three identical heating resistors made of carbon composites. Connections between the copper power bus and the carbon resistors are made by means of sub-divide the electrodes, which is critical for electrical-thermal coupling. Finite element models (FEM) are developed to predict the heating behavior of the tested substrates under different designs. It is pointed out that the most optimized design solves important drawbacks of the initial design in terms of temperature regularity and overheating. Full characterizations of the electrical and thermal properties, together with morphological analyses via SEM images, are conducted on different coated samples, making it possible to identify the relevant physical parameters of the materials as well as confirm the printing quality. It is discovered through a combination of FEM and experimental evaluations that the printed coating patterns have a crucial impact on the energy conversion and heating performance. Our first prototype, thanks to many design optimizations, entirely meets the specifications required by the automobile industry. Accordingly, multifunctional materials together with printing technology could offer an efficient heating method for the smart textile industry with significantly improved comfort for both the designer and user.

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Concrete is the most consumed material in the construction industry. Using recycled aggregates (RA) and silica fume (SF) in concrete and mortar could preserve natural aggregates (NA) and reduce CO2 emissions and construction and demolition waste (C&DW) generation. Optimizing the mixture design based on both fresh and hardened properties of recycled self-consolidating mortar (RSCM) has not been performed. In this study, multi-objective optimization of mechanical properties and workability of RSCM containing SF was performed via Taguchi Design Method (TDM) with four main variables including cement content, W/C ratio, SF content and superplasticizer content at three different levels. SF was used to decrease the environmental pollution caused by cement production as well as compensating the negative effect of RA on the mechanical properties of RSCM. The results revealed that TDM could appropriately predict the workability and compressive strength of RSCM. Also, mixture design containing W/C = 0.39, SF = 6%, cement = 750 kg/m3 and SP = 0.33% was found as the optimum mixture having the highest compressive strength and acceptable workability as well as low cost and environmental concerns.