Yunxuan Wang

Millimeter-wave radar is a vital component of advanced driver assistance systems and intelligent transportation systems, thanks to its affordability, continuous operational capability, and resilience in adverse weather conditions. Point cloud imaging is one of the most crucial techniques that enables a thorough perception of the surroundings and enhances target detection and recognition in practical scenarios. However, conventional radar point cloud imaging methods encounter performance limitations due to inadequate target reflectivity under complex electromagnetic environments. This study investigates coherent and non-coherent accumulation methods for point cloud imaging and introduces a novel neural network method, namely, 3DCFAR-Net, to overcome these limitations. The robustness and effectiveness of 3DCFAR-Net are rigorously evaluated through both numerical simulations and real-world experiments under complex electromagnetic scenarios.

Machine learning methods have demonstrated significant advantages in predicting the shear mechanical performance of concrete connectors. However, the application to nailed connections in wood structures remains limited. Accurate prediction of the shear capacity of nailed connections is essential for assessing the seismic performance and safety of wood structures. In this study, a series of push-out tests were conducted on panel-sheathing nailed connections, and the obtained load–displacement curves were analyzed to characterize their mechanical behavior. Six ML models were trained and tested using a dataset comprising 101 push-out tests. Eight key features, including both mechanical properties (E, G, f_es, f_em) and geometric parameters (d, l, s, D), were selected as input parameters, and three characteristic values including ultimate load, initial shear stiffness, and ultimate displacement were chosen as output parameters. The results indicated that the support vector regression (SVR) model exhibited the best performance in predicting the three output parameters of nailed connections, with corresponding R² values reaching of 0.9950, 0.9976, and 0.9994, respectively. The study employed the Shapley additive explanations (SHAP) method to investigate the importance of features. The findings revealed that the elastic modulus of the side plate significantly influenced ultimate load and initial shear stiffness. Additionally, the initial shear stiffness was primarily affected by the nail spacing, whereas the shear modulus and pin-bearing strength substantially affected ultimate displacement. The prediction results of the machine learning model were compared with existing empirical, confirming that the machine learning model achieved high accuracy and strong applicability in predicting the shear bearing capacity of nailed connections in wood structures.