The noise suppression method based on dictionary learning has shown great potential in magnetotelluric (MT) data processing. However, the constraints used in the existing algorithm’s method need to set manually, which significantly limits its application. To solve this problem, we propose a deep learning optimized dictionary learning denoising method. We use a deep convolutional network to learn the characteristic parameters of high-quality MT data independently and then use them as the constraints for dictionary learning so as to achieve fully adaptive sparse decomposition. The method uses unified parameters for all data and completely eliminates subjective bias, which makes it possible to batch-process MT data using sparse decomposition. The processing results of simulated and field data examples show that the new method has good adaptability and can achieve recognition with high accuracy. After processing with our method, the apparent resistivity and phase curves became smoother and more continuous, and the results were validated by the remote reference method. Our method can be an effective alternative method when no remote reference station is set up or the remote reference processing is not effective.

Calcium sulfate hemihydrate whiskers (CSHW), a multi-functional and high value-added building material, were prepared with flue gas desulfurization (FGD) gypsum by hydrothermal method, which could be a reasonable disposal of FGD gypsum. In order to obtain CSHW of a high aspect ratio, a series of manufacturing parameters such as reaction temperature, stirring speed, material–water ratio, and reaction time were investigated. The effect of stabilizing treatment and glycerol concentration on CSHW morphology were also studied by environmental scanning electron microscopy (ESEM) and statistical analysis. The results showed that the optimum preparing conditions of reaction temperature, stirring speed, water–material ratio, and reaction time were 160 °C, 200~300 rpm, 11:1 and 1 h, respectively. Furthermore, stabilizing treatment with octodecyl betaine was necessary for the preparation of CSHW. The final prepared whiskers had smooth surface, uniform morphology, a diameter of 260 nm, and a corresponding aspect ratio of 208.2. Moreover, the addition of glycerol reduced the activity of water, contributing to a lower reaction temperature and much smaller diameter.

Rockbursts pose a significant threat to the safety of construction workers during construction periods. Many factors control its occurrence. To evaluate the intensity level of rockburst in hydraulic tunnels accurately, the intuitionistic fuzzy sets-TOPSIS model is first introduced. And, the decisive matrix is constructed. Then, the weighed decisive matrix is calculated. Finally, the degree of membership at different levels regarding the rockburst intensity is obtained. According to the ranking sequence of the membership degree, the risk level that corresponds to the maximum membership degree is the final assessment level. The conclusions are drawn that assessing the level of rockburst intensity using the proposed method is feasible. Compared with the traditional method, the proposed model has sufficient usage of original data, minor information loss, and broader application. And, their judgments under inherent uncertainty in the proposed model can be conveyed. More significantly, the degree of indeterminacy can be handled adequately in the evaluation. The proposed method not only can deal with vague information but also ease our workload. The efficiency and accuracy can be improved. In total, it can provide a new route for the intensity prediction of rockburst in the future.

The occurrence of debris flow hazards has great destructive effects on the life and property safety of the people. Many factors control its occurrence. The intuitionistic fuzzy sets-TOPSIS model is introduced to assess the risk level of debris flow in the Duba river watershed at first; secondly, the decisive matrix of the intuitionistic fuzzy sets is established, and the index weight coefficients are determined by using the entropy weight method, and then the weighed decisive matrix is obtained. Finally, the degree of membership at different levels is calculated. The risk level corresponding to the maximum degree of membership is regarded as the final assessment level. The conclusions are drawn, estimating that the risk level of debris flow hazards in the Duba river watershed based on the intuitionistic fuzzy sets-TOPSIS model is feasible when its results are compared with the actual investigation and Liu X.L model, and the model not only can reflect the risk level of debris flow hazards accurately, but also further determines the risk ranking of debris flow in four different gullies, so that it can provide a new method and thoughts to estimate the risk level of debris flow hazards in the future.

The seismic fragility of reinforced concrete (RC) bridge piers is critical for urban rail transport systems, as severe pier damage may interrupt post-earthquake operation and threaten network safety. Compared with conventional highway bridge piers, urban rail transport RC solid piers usually have lower axial load ratios, larger cross-sections, and stricter serviceability requirements. However, the combined effects of geometric parameters, reinforcement detailing, and material strength on their cyclic behavior, dynamic response, and seismic fragility remain insufficiently understood. To address this issue, seven 1/4-scale RC solid pier specimens were tested under quasi-static cyclic loading to examine the effects of pier height, transverse reinforcement ratio, and longitudinal reinforcement ratio on damage evolution, hysteretic response, skeleton curves, and energy dissipation. A fiber-based OpenSees model considering bond-slip effects was then established, validated against the tests, and extended to a full-scale prototype pier for parametric analysis. The effects of aspect ratio, axial load ratio, longitudinal reinforcement ratio, stirrup ratio, steel yield strength, and concrete strength were evaluated under cyclic loading and nonlinear dynamic time-history excitations. An incremental dynamic analysis-based probabilistic seismic demand model was further developed using 30 near-fault ground motions, with peak ground acceleration as the intensity measure and displacement ductility as the engineering demand parameter. The results showed that increasing the aspect ratio changed the failure mode from flexure-shear-dominated to flexure-dominated behavior, increasing the ultimate displacement from 122 mm to 155 mm while reducing the peak lateral strength from 263 kN to 248 kN. Increasing the longitudinal reinforcement ratio improved both peak strength and ultimate displacement, from 226 kN to 262 kN and from 120 mm to 160 mm, respectively. The numerical results indicated that aspect ratio, axial load ratio, and longitudinal reinforcement ratio had more pronounced effects on seismic demand and fragility than stirrup ratio. Increasing steel yield strength generally reduced seismic fragility, whereas increasing concrete strength enhanced lateral resistance but did not necessarily improve fragility performance. These findings suggest that the seismic performance of urban rail transport RC solid piers should be evaluated by combining cyclic response, dynamic demand, and fragility-based performance, rather than by maximizing any single design parameter.