In this paper, uniaxial compression tests were carried out for recycled aggregate concrete with water cement ratios of 0.38, 0.49, and 0.66 and replacement ratios of 0%, 25%, 50%, 75%, and 100%, respectively. The influence of the replacement ratio of recycled aggregate and water cement ratio on the strength, elastic modulus, and deformation characteristics of concrete was discussed. The results show that the replacement rate of recycled aggregate has a significant effect on the macro stress–strain behavior of concrete. In the case of a constant water cement ratio, the peak nominal stress first decreases and then increases with the increase of the replacement rate; while the water cement ratios equal 0.38, 0.49, and 0.66, the corresponding transition states are 25%, 50%, and 50% of the replacement rate, respectively. The deformation and failure is characterized by two stages: distributed damage and local failure. Combined with the statistical damage mechanics, the influence of the aggregate replacement rate on the damage evolution mechanism of recycled concrete on a mesoscopic scale was explored. Two mesoscopic damage modes, fracture and yield, are considered. Their cumulative evolutions are assumed to follow triangular probability distributions, which could be characterized by four parameters. The peak nominal stress state and the critical state are distinguished, and the latter is defined as a precursor to local failure. With the increase of the replacement rate of recycled aggregate, the inhomogeneous evolution of mesoscopic damage shows obvious regular change, which is consistent with the internal chemical and physical mechanism and macro nonlinear stress–strain behavior.

In order to investigate the effect of a baseline control system (BCS) on dynamic and fatigue characteristics of modern wind turbines, the simulation results of a 5-MW wind turbine subjected to wind loading without and with considering BCS (No. 1 and No. 2, respectively) were compared. The displacement and moment of tower and blades were compared, and the rainflow counting algorithm was used to analyze the fatigue load expressed in damage equivalent load (DEL). The results indicate that the mean and maximum values of structural dynamic responses in No. 2 have a great reduction with wind speed exceeding the rated speed comparing to No. 1; the 3P (3-times the frequency of rotor speed) of the wind turbine rotor speed will be close to the tower fore-aft (FA) natural frequency with the wind velocity around the cut-in speed in No. 2, which caused larger dynamic responses, number of load cycles, and DELs for the tower compared with No. 1; when wind velocity exceeds the rated wind speed, the BCS caused higher fluctuation of loads, increasing the related DELs. It is concluded that when analyzing the dynamic responses and designing wind turbine tower and blades, taking the BCS into account during analysis is significant.

Earth-rock dams are critical components of hydraulic engineering, undertaking core functions such as flood control and disaster mitigation. However, the potential occurrence of dam breach poses a severe threat to regional socioeconomic stability and ecological security. To address the limitations of traditional Bayesian network (BN) in capturing the complex nonlinear coupling and dynamic mutual interactions among risk factors, they are integrated with machine learning techniques, based on a collected dataset of earth-rock dam breach case samples, the PC structure learning algorithm was employed to preliminarily uncover risk associations. The dataset was compiled from public databases, including the U.S. Army Corps of Engineers (USACE) and Dam Safety Management Center of the Ministry of Water Resources of China, as well as engineering reports from provincial water conservancy departments in China and Europe. Expert knowledge was integrated to optimize the network topology, thereby correcting causal relationships inconsistent with engineering mechanisms. The results indicate that the established hybrid model achieved AUC, accuracy, and F1-Score values of 0.887, 0.895, and 0.899, respectively, significantly outperforming the data-driven model G1. Forward inference identified the key drivers elevating breach risk. Conversely, backward inference revealed that overtopping was the direct failure mode with the highest probability of occurrence and the greatest contribution. The integration of data-driven approaches and domain knowledge provides theoretical and technical support for the probabilistic quantification of earth-rock dam breach and risk prevention and control decision-making.

Probabilistic analysis of overtopping is an important aspect of dam construction, and the uncertainty in the construction progress complicates the calculation of overtopping probabilities. Construction progress is significantly influenced by human factors, making it difficult to assign an accurate probability distribution. Although the interval non-probabilistic reliability (INPR) method can estimate the likelihood of events with unknown probability distributions, its calculation results for the overtopping probability have significant errors. To improve the rationality of the results, we developed a method that adopted flood frequency of the upstream flood level to correct the traditional INPR calculations, developing an improved model. Taking the Qianping Reservoir as an example, the overtopping probabilities for three construction schedules were calculated, and the results were compared with a flood routing calculation. The results indicated that the improved approach significantly improves the rationality of the calculations, and the results effectively reflected the impact of construction progress uncertainty on overtopping probabilities.