In high humidity areas, it is necessary to improve the impermeability of concrete to water and other erosion solutions. The internal defect and pore channel of concrete are the main factors affecting the impermeability and durability. In this paper, a novel hydrophobic agent named Yellow River Engineering Consulting (YREC) was prepared. The relative internal defect degree of concrete with different curing ages and YREC contents was evaluated by ultrasonic non-destructive testing as qualitative characterization method, and the effect of YREC on hydration reaction was investigates using X-ray powder diffraction (XRD). Water permeability and contact angle tests were used to analyze the internal and external hydrophobicity induced by YREC addition, respectively. The pore structure changes of concrete mortar matrix induced by YREC were further discussed applying low-temperature liquid nitrogen adsorption (LT-NA) and mercury intrusion/extrusion porosimetry (MIP). The results indicated that YREC not only improves the impermeability of water, but also greatly enhances the mechanical strength. In the case of mixing YREC, the porosity of concrete mortar matrix decreases accompanied with the more advantage pores (micropores and transition pores) developed. Based on the relative internal defect degree and the changes of multi-scale pore structure, the functionality and durability of concrete with 4% YREC addition are the most desirable.

The water diversion surge shaft is vital for a hydropower station. However, the complex geological properties of the surrounding rock make it challenging to obtain its mechanical parameters. A method combining particle swarm optimization (PSO) and support vector machine (SVM) algorithms is proposed for estimating these parameters. According to the engineering geological background and support scheme, a three-dimensional model of the water diversion surge shaft is established by FLAC3D. An orthogonal test is designed to verify the accuracy of the numerical model. Then, the surrounding rock mechanical parameter database is established. The PSO-SVM intelligent inversion algorithm is used to invert the optimal values of the mechanical parameters of the surrounding rock. The support for excavating the next layer depends on the mechanical parameters of the current rock layer. An optimized design scheme is then compared and analyzed with the original support scheme by considering deformation and plastic characteristics. The research results demonstrate that the PSO-SVM intelligent inversion algorithm can effectively improve the accuracy and efficiency of the inversion of rock mechanical parameters. Under the influence of excavation, the surrounding rock in the plastic zone mainly fails in shear, with maximum deformation occurring in the middle and lower parts of the excavation area. The maximum deformation of the surrounding rock under support with long anchor cables is 0.6 cm less than that of support without long anchor cables and 4.07 cm less than that of support without an anchor. In the direction of the maximum and minimum principal stress, the maximum depth of the plastic zone under the support with long anchor cables is 1.3 m to 2.6 m less than that of the support without long anchor cables and the support without an anchor. Compared with the support without long anchor cables and support without an anchor, the support with long anchor cables can effectively control the deformation of the surrounding rock and limit the development of the plastic zone.

The first impoundment of a super-high dam is a crucial period from dam construction to operation, in which the prediction of the dam deformation is vital for the continued safety of the dam. Therefore, a multi-head attention model based on Pearson K-means clustering is proposed, which is shortened to PKMA. The inputs of the PKMA include measurements of the displacements of plumb lines, water levels, air temperatures, dam body temperatures, water temperatures, and foundation temperatures. Among these inputs, variables related to displacements are regarded as the dominant explanatory factors. Hence, the K-means clustering based on the Pearson index is utilised to increase the weights of displacements in the PKMA. To involve the interactions between inputs, the MA mechanism of neural networks is used to simulate the relationship between inputs and deformation targets. The PKMA model had a maximum MSE of 1.2518 and a maximum MAE of 0.9017 for the model performance metrics at the study measurement points. Compared to the comparison models MA, HST, and LSTM, the performance metrics of the PKMA model are an improvement of an average of 87.02%, 72.42%, and 69.24%.