It is important to determine the soil–water characteristic curve (SWCC) for analyzing landslide seepage under varying hydrodynamic conditions. However, the SWCC exhibits high uncertainty due to the variability inherent in soil. To this end, a Bayesian updating framework based on the experimental data was developed to investigate the uncertainty of the SWCC parameters in this study. The objectives of this research were to quantify the uncertainty embedded within the SWCC and determine the critical factors affecting an unsaturated soil landslide under hydrodynamic conditions. For this purpose, a large-scale landslide experiment was conducted, and the monitored water content data were collected. Steady-state seepage analysis was carried out using the finite element method (FEM) to simulate the slope behavior during water level change. In the proposed framework, the parameters of the SWCC model were treated as random variables and parameter uncertainties were evaluated using the Bayesian approach based on the Markov chain Monte Carlo (MCMC) method. Observed data from large-scale landslide experiments were used to calculate the posterior information of SWCC parameters. Then, 95% confidence intervals for the model parameters of the SWCC were derived. The results show that the Bayesian updating method is feasible for the monitoring of data of large-scale landslide model experiments. The establishment of an artificial neural network (ANN) surrogate model in the Bayesian updating process can greatly improve the efficiency of Bayesian model updating.

Pervious concrete (PC) is a multifunctional material with good water permeability, noise reduction, and heat absorption properties. The most critical performance indicators of the PC are permeability and strength, which are mainly affected by the cement paste coating thickness (PCT) on the aggregate surface. The experiment was carried out to study the influence of the water–cement ratio, superplasticizer dosage, aggregate roughness, and aggregate size on PCT, and a new normalization method was proposed for grey correlation analysis to determine the influence degree of the above factors on PCT. Finally, fitting models for predicting the PCT were established based on experimental data. The experimental results indicate that the influence of the water–cement ratio on PCT can be divided into two stages, whereby the PCT shows a slow decline and then a rapid decline with the increase in water–cement ratio; with the increase in superplasticizer dosage, the PCT represents an exponentially decreasing trend; the PCT increases with the aggregate size and aggregate roughness. Based on the grey correlation analysis, the superplasticizer dosage exerts the greatest influence on PCT, followed by the water–cement ratio, aggregate size, and aggregate roughness. The results of this study revealed the change law of PCT under the action of the above factors, which established the premise of controlling the strength and permeability of PC from the perspective of the PCT and provided a reference for the mixture proportion design.

The water flow within the turbine passage of a hydropower station exhibits high-speed closed-pressure flow. The flow field characteristics will directly affect the turbine’s operational efficiency and safety. To ensure the safe operation of the turbine and accurately monitor its flow state, the relationship between the flow characteristics in the turbine passage and its discharge noise must be established. In this study, the relationship between the flow field and the noise field of the turbine is explored using a combination of a model turbine passage discharge noise test and numerical simulation of flow field characteristics. Results show that the operating parameters are closely related to the discharge noise’s characteristics, in which the operating head and discharge of the unit’s operating parameters greatly influence the discharge noise in the flow passage. Hydrodynamic factors, such as fluctuation pressure and pressure in the flow field, show a strong correlation with the discharge noise characteristics. As the pressure and fluctuation pressure in the inlet area of the spiral case intensify, the sound pressure level (SPL) of the discharge noise increases and the main frequency decreases. A large-scale vortex easily forms in the spiral case and draft tube area, thereby causing low-frequency fluctuation and forming high-decibel noise. Also, the runner area is the main sound source region of the turbine passage. The research results will provide technical and theoretical support for the safe operation and accurate fault diagnosis of hydropower stations.