Rice husk ash concrete (RHAC) shows promise as a beneficial supplementary material in concrete. However, determining mechanical properties such as compressive strength (CS) and splitting tensile strength (STS) of RHAC through conventional lab-scale methods is laborious and time-consuming. In this research, seven important variables were selected as inputs to predict CS and STS using machine learning (ML) models, including Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) with grid search optimization. The result presented revealed that selected machine learning models provide well accuracy for CS and STS estimates. Among these, the DTR model demonstrated superior performance, with CS prediction R², RMSE, MAE, and MAPE values of 0.964, 3.314, 2.225, and 5.068, at the testing stage respectively. For STS at the testing stage, DTR achieved R² of 0.969, RMSE of 0.177, MAE of 0.1322, and MAPE of 3.413. GPR and RFR models also performed well, with R² values of 0.9434 and 0.9530 for CS prediction. The partial dependence plot (PDP) analysis revealed the optimal mix design parameters for achieving the desired strength. These results offer valuable insights for sustainable construction, allowing engineers to efficiently predict and optimize material performance, reducing the reliance on time-consuming lab methods.

This paper proposes an improved method for car motion planning aimed at addressing the limitations of traditional path planning and obstacle avoidance algorithms in complex environments. The study utilizes Bi-RRT* and polynomial fitting for path planning, incorporating an environment-adaptive polynomial fitting technique based on obstacle density to enhance path precision in areas with high obstacle density. In the local planning phase, intelligent switching of the car’s obstacle avoidance strategies is implemented, allowing the car to use reverse motion or lateral avoidance in high-density regions to prevent stalling. Furthermore, problem decomposition and approximation methods are applied to large-scale quadratic programming (QP) problems in path tracking, improving the efficiency of the MPC algorithm. Experimental results demonstrate that the proposed method significantly enhances the car’s motion performance and stability in complex environments.