Design Optimization of Rubber-Basalt Fiber- Modified Concrete Mix Ratios Based on a Response Surface Method

Optimized Design of Low-Carbon Mix Ratio for Non-Dominated Sorting Genetic Algorithm II Concrete Based on Genetic Algorithm-Improved Back Propagation

In order to achieve low-carbon optimization in the intelligent mix ratio design of concrete materials, this work first constructs a concrete mix ratio database and performs a statistical characteristics analysis. Secondly, it employs a standard back propagation (BP) and a genetic algorithm-improved BP (GA-BP) to predict the concrete mix ratio. The NSGA-II algorithm is then used to optimize the mix ratio. Finally, the method's accuracy is validated through experiments. The study's results indicate that the statistical characteristics of the concrete mix ratio data show a wide distribution range and good representativeness. Compared to the standard BP, the fitting accuracies of each GA-BP set are improved by 4.9%, 0.3%, 16.7%, and 4.6%, respectively. According to the Fast Non-Dominated Sorting Genetic Algorithm II (NSGA-II) optimization for meeting C50 concrete strength requirements, the optimal concrete mix ratio is as follows: cement 331.3 kg/m3, sand 639.4 kg/m3, stone 1039 kg/m3, fly ash 56 kg/m3, water 153 kg/m3, and water-reducing agent 0.632 kg/m3. The 28-day compressive strength, material cost, and carbon emissions show relative errors of 2.1%, 0.6%, and 2.9%, respectively. Compared with commercial concrete of the same strength grade, costs and carbon emissions are reduced by 7.2% and 15.9%, respectively. The methodology used in this study not only significantly improves the accuracy of concrete design but also considers the carbon emissions involved in the concrete preparation process, reflecting the strength, economic, and environmental impacts of material design. Practitioners are encouraged to explore integrated low-carbon research that spans from material selection to structural optimization.

Mechanical properties of coconut shell-based concrete: experimental and optimisation modelling

Excessive accumulation of waste materials has presented a serious environmental problem on a global scale. This has prompted many researchers to utilise agricultural, industrial, and by-product waste materials as the replacement of aggregate in the concrete matrix. In this present study, the prediction and optimisation of coconut shell (CA) content as the replacement of fine aggregate were evaluated based on the mechanical properties of the concrete (M30). Based on the suggested design array from the response surface methodology (RSM) model, experimental tests were carried out to achieve the goal of this study. The collected data was used to develop mathematical predictive equations using both GEP and RSM models. Analysis of variance (ANOVA) was also taken into account to appraise and verify the performance of the proposed models. Based on the results, the optimum content of replacing CA was 50%. In particular, the compressive, tensile, and flexural strength obtained after 28 days of curing were 46.2, 3.74, and 8.06 MPa, respectively, from the RSM model and 46.18, 3.85, and 7.99 MPa, respectively, from the GEP model. The obtained values were superior to those of the control concrete sample (43.12, 3.51 and 7.14 MPa, respectively). Beyond the optimum content, a loss in strength was observed. It was also found that both the GEP and RSM models exhibited high prediction accuracy with strong correlations (R2 = 0.97 and 0.95, respectively). In addition, minimum prediction error (RMSE < 0.945 (RSM), RMSE < 1.62 (GEP)) was achieved, indicating that both models were robust and reliable for further prediction. It was concluded that CA could serve as an excellent strategic material to address several serious environmental issues.

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

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.