Accurate shear load capacity predictions are crucial to achieving the load-bearing requirements of concrete deep beams in a variety of construction structures. Conventional BP neural networks have the drawbacks of being prone to local optimums and having a sluggish rate of convergence for predicting the shear load capacity of reinforced concrete deep beams. To overcome this problem, this study incorporated the black widow optimization algorithm (BWO) and principal component analysis (PCA) into a BP neural network to create a unique Hybrid Intelligent Optimization Algorithm (PCA-BWO-BP). Firstly, PCA was used to reduce the dimensionality of the input variables of the shear load capacity prediction model of reinforced concrete deep beams. Secondly, BWO was introduced to optimize the weights and thresholds of the BP neural network. Finally, the four algorithms were compared and validated through the use of five model evaluators. The results showed that the PCA-BWO-BP model can explore the intrinsic relationship between member size, bottom longitudinal reinforcement, hoop reinforcement, concrete strength and the shear load capacity of reinforced concrete deep beams and generate reasonable prediction values, and the complexity of the prediction model can be effectively reduced by introducing the PCA algorithm, whereas the BWO algorithm can optimize the weights and thresholds of the BP neural network to improve the convergence and global search ability of the model. The mean absolute percentage error (MAPE) of the PCA-BWO-BP algorithm is 5.126, and the Nash efficiency coefficient (NS) is 0.989. The generalization ability and prediction accuracy are significantly better than those of the BP neural network, which can solve the problem relating to the fact that BP neural networks are prone to falling into the local optimum. The PCA-BWO-BP model has strong prediction ability, stability, generalization ability and robustness, which can predict the shear load capacity of reinforced concrete deep beams more accurately. It provides a new method and case support for further research on the shear bearing capacity of reinforced concrete deep beams.

As the stress amplitude in the anchorage zone of cable-stayed bridges vary considerably, it is essential to study the fatigue-load-bearing capacity of the anchorage structures of cable-stayed-bridge girders. In this study, the mechanical properties and force-transmission mechanism of a built-in double-fixed steel anchor box, as well as the influence of the geometric design parameters of the main pressure plates and support plates on its stress performance, were studied. A PSO-BP built-in double-fixed steel anchor box mechanical-performance-prediction-and-evaluation system was established, with the geometric design parameters of the important plates of the main body of the anchor box as the input variables and the mechanical properties of the main stress-transmitting welds of the steel anchor box as the output-evaluation variables. The results were as follows: the cable force was mainly transmitted in the form of shear force through the welds between the support plate and the web of the main beam; the transmission ratio of the four main welds was generally maintained at about 23%, and the stress distribution of each plate was relatively uniform, with most of the stresses in the range of 10~50 MPa. The shear-stress-inhomogeneity coefficient of the transmission weld was sensitive to the changes in the thickness of the support plate and the pressure plate, and the transmission ratio of the main weld was sensitive to the changes in the thickness and length of the support plate. The PSO-BP-based mechanical-performance-prediction-and-evaluation system can be used to explore the intrinsic relationship between the designed cable force, important plate-geometry parameters, and the force performance of steel anchor boxes during the iterative process, and to generate more accurate prediction and evaluation values.