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This study aims to recognize fluorescent excitation images of microcracks in concrete components through the adoption of an Attention Mechanism-based Deep Recurrent Neural Network (RNN) model, thereby enhancing the accuracy and efficiency of crack detection. Considering the significance of concrete crack detection and the limitations in efficiency and accuracy of existing methods, this paper proposes an innovative image processing technique that combines fluorescent excitation methods with deep learning models to achieve earlier and more accurate identification of concrete microcracks. C30 concrete specimens were prepared experimentally and treated with fluorescent solution spray. Fluorescent images of cracks under UV light excitation were collected and processed using a segmented attention mechanism deep RNN model. Various performance evaluation metrics, including mean Intersection over Union (mIoU), mean Image Intersection over Union (miIoU), mean Image Dice Coefficient (miDice), and F1 score, were employed to comprehensively assess the model's performance. The results demonstrate that the proposed model achieved significant effectiveness in concrete crack image recognition, showing higher mIoU, miIoU, miDice, and F1 scores compared to other representative deep learning models, thus proving its advantages in recognition accuracy and efficiency. Particularly, by introducing the segmented attention mechanism, the model could capture microcrack features more effectively, significantly improving the accuracy of crack identification. This method not only provides a new technical approach for the early detection of concrete cracks but also lays the foundation for further development of efficient and accurate crack detection technologies to accommodate more complex engineering application scenarios.

High concrete-face rockfill dams (CFRDs) with heights of over 100 m have been quickly developed in recent years. The self-weight of rockfill materials causes creep deformation of the dam body. However, the creep analysis method of high CFRDs in finite element software is few, and sometimes, it can also not reflect the long-term performance of high CFRDs well. Therefore, it is necessary to carry out the secondary development in finite element software. This study developed a subroutine that can run in Finite Element Method (FEM) platform ABAQUS to simulate long-term creep deformation behavior of the rockfill materials more accurately. Then, a displacement back-analysis for parameters, based on the Xujixia high CFRD project, is performed by the neural network response surface method (BP-MPGA/MPGA). Remarkable agreements are observed between simulation and field monitoring results. The calibrated FEM model is used to predict stress and deformation behavior of the Xujixia high CFRD after three years of operation period. The result indicates that rockfill creep deformation has a significant impact on stress and deformation of the high CFRD during the operation. This research may predict long-term performance using FEM in the design stage for high CFRDs.

This study presents a stability analysis of a high-steep rock slope with two faults during excavations and evaluates the effectiveness of a proposed reinforcement method using prestressed anchor cables. A 3D finite difference model was established based on the strength reduction method using FLAC3D software. The influence of various fault conditions and the effectiveness of the reinforcement on the slope stability during the excavation process were analyzed and compared to field monitoring data. The numerical analysis and field monitoring results showed that the fault close to the slope surface (f20) was prone to the local instability under external forces caused by the excavation, but a fault further away from the slope surface (f14) had insignificant influence on the stability of the slope. Based on the numerical analysis results, the proposed reinforcement measure can increase the factor of safety (FOS) of the slope by 19.2%. The field monitoring data also showed that the displacement of the monitoring point gradually decreased after the reinforcement, and the deformation of the slope was effectively controlled.

This paper mainly investigates the fracture parameters of Basalt Fiber Reinforced Concrete (BFRC) with various fiber lengths and dosages using Double-K fracture model. The model was developed by fracture criterion using ABAQUS Virtual Crack Closure Technique (VCCT), and the results of the model and experiments were compared. The basalt fiber with length of 6 mm and 12 mm was added into concrete in the dosage of 0.0%, 0.1%, 0.2%, 0.3%, 0.4%, and 0.5% by volume of concrete, respectively. Concrete specimens were cast into three dimensions, i.e., 60 mm × 180 mm × 480 mm, 80 mm × 240 mm × 640 mm, and 100 mm × 300 mm × 800 mm. Then, three-point bending test was conducted on precast-notched beams. The load versus cracking mouth opening displacement (P-CMOD curve) was developed in order to evaluate cracking and breaking load. The initial fracture toughness and unstable fracture toughness were derived from the Double-K fracture model aimed to optimize the fiber length and dosage. The results showed that the initial fracture toughness and unstable fracture toughness increased first and then decreased with the increase in fiber dosage, and basalt fiber with length of 6 mm and dosage of 0.2% performed the best toughening effect on concrete. The comparison results showed that numerical simulation can better simulate the initiation and propagation of BFRC fractures and achieve the dynamic propagation process of fractures.

Abstract Drought risk assessment plays a crucial role in effective drought management. However, it is often challenging due to the intricate relationships among various indicators and the lack of practical guidance. This study presents a drought risk assessment model developed using the Semi‐partial Quadratic Subtraction Set Pair Potential (SQSSPP) method, which is derived from the theory of set pair analysis. The indicator system comprises 21 indicators divided into four subsystems. The SQSSPP method utilizes uncertainty information in the overall development trend of regional drought risk states by extracting connection numbers from the Subtraction Set Pair Potential (SSPP), improving the reliability of evaluation results. The SQSSPP method is validated through a case study of Suzhou City, China, from 2007 to 2017. Three grades are used to evaluate comprehensive drought risk. The result shows an overall decreasing trend over time, with a level III risk in 2010 and consistently at level II from 2011 to 2017. Indicators in the hazard and resilience subsystems are the primary factors influencing drought risk in the Suzhou City. Specific indicators requiring emphasis for improvement are identified, including arable land rate, agricultural population ratio, reservoir regulation rate, current water supply capacity, and irrigation index. The SQSSPP method not only provides targeted drought risk assessment but also provides valuable guidance for future water resource management. While the study focuses on Suzhou City, the proposed approach is applicable to broader‐scale risk management evaluations and practices.

The inflammatory response induced by fine particulate matter (PM2.5), a common class of air pollutants, is an important trigger for the development of pulmonary fibrosis. However, the specific mechanisms responsible for this phenomenon are yet to be fully understood. To investigate the mechanisms behind the onset and progression of lung fibrosis owing to PM2.5 exposure, both rats and human bronchial epithelial cells were subjected to varying concentrations of PM2.5. The involvement of the PPARG/HMGB1/NLRP3 signaling pathway in developing lung fibrosis caused by PM2.5 was validated through the utilization of a PPARG agonist (rosiglitazone), a PPARG inhibitor (GW9662), and an HMGB1 inhibitor (glycyrrhizin). These outcomes highlighted the downregulation of PPARG expression and activation of the HMGB1/NLRP3 signaling pathway triggered by PM2.5, thereby eliciting inflammatory responses and promoting pulmonary fibrosis. Additionally, PM2.5 exposure-induced DNA hypermethylation of PPARG-encoding gene promoter downregulated PPARG expression. Moreover, the DNA methyltransferase inhibitor 5-azacytidine mitigated the hypermethylation of the PPARG-encoding gene promoter triggered by PM2.5. In conclusion, the HMGB1/NLRP3 signaling pathway was activated in pulmonary fibrosis triggered by PM2.5 through the hypermethylation of the PPARG-encoding gene promoter.

In contrast to most existing works on robust unit commitment (UC), this study proposes a novel big-M-based mixed-integer linear programming (MILP) method to solve security-constrained UC problems considering the allowable wind power output interval and its adjustable conservativeness. The wind power accommodation capability is usually limited by spinning reserve requirements and transmission line capacity in power systems with large-scale wind power integration. Therefore, by employing the big-M method and adding auxiliary 0-1 binary variables to describe the allowable wind power output interval, a bilinear programming problem meeting the security constraints of system operation is presented. Furthermore, an adjustable confidence level was introduced into the proposed robust optimization model to decrease the level of conservatism of the robust solutions. This can establish a trade-off between economy and security. To develop an MILP problem that can be solved by commercial solvers such as CPLEX, the big-M method is utilized again to represent the bilinear formulation as a series of linear inequality constraints and approximately address the nonlinear formulation caused by the adjustable conservativeness. Simulation studies on a modified IEEE 26-generator reliability test system connected to wind farms were performed to confirm the effectiveness and advantages of the proposed method.

Glaciers experience periodic variations in flow velocity called surges, each of which influences the glacier’s characteristics and the occurrence of downstream disasters (e.g., ice-dammed lake outburst floods). The Karakoram region contains many surging glaciers, yet there are few comprehensive studies of multiple surge cycles. In this work, Landsat, topographic map, Shuttle Radar Topography Mission (SRTM), TerraSAR-X/TanDEM-X, ITS_LIVE, and Sentinel-1 glacier velocity data were used to systematically analyze the characteristics of Kyagar Glacier since the 1970s. Three surging events were identified, with active phases in 1975–1978, 1995–1997, and 2014–2016. The timing of these surges was similar, with a cycle of 19–20 years, an active phase of 3–4 years, and a quiescent phase of 16–17 years. During the quiescent phase, a large amount of ice accumulates in the lower part of the accumulation zone, and the terminal of the tongue thins significantly. According to the most recent surge event (2014–2016), glacier flow accelerated suddenly in the active phase and reached a maximum velocity of 2 ± 0.08 m d−1. Then, the glacier terminal thickened sharply, the reservoir zone thinned by 12 ± 0.2 m, and the terminal receiving zone thickened by 28 ± 0.2 m. The glacier may have entered a quiescent phase after July 2016. The glacier surge causes a large amount of material to transfer from upstream to downstream, forming an ice dam and creating conditions for a glacial lake outburst flood (GLOF). At the termination of the active phase, the subglacial drainage channel became effective, triggering the GLOF. For a period of the quiescent phase, the glacier ablation intensifies and the GLOF repeats constantly. One surge caused 7–8 GLOFs, and then a continuous reduction in the ice dam elevation. Eventually, the ice dam disappeared, and the GLOF no longer continued before the next glacier-surging event.

Online estimation of electromechanical oscillation parameters provides essential information to prevent system instability and blackout and helps to identify event categories and locations. We formulate the problem as a state space model and employ the extended Kalman filter to estimate oscillation frequencies and damping factors directly based on data from phasor measurement units. Due to considerations of communication burdens and privacy concerns, a fully distributed algorithm is proposed using diffusion extended Kalman filter. The effectiveness of proposed algorithms is confirmed by both simulated and real data collected during events in State Grid Jiangsu Electric Power Company.

The Variable Series Reactors (VSRs) can efficiently control the power flow through the adjustment of the line reactance. When they are appropriately allocated in the power network, the transmission congestion and generation cost can be reduced. This paper proposes a planning model to optimally allocate VSRs considering AC constraints and multi-scenarios including base case and contingencies. The planning model is originally a non-convex large scale mixed integer nonlinear program (MINLP), which is generally intractable. The proposed Benders approach decomposes the MINLP model into a mixed integer linear program (MILP) master problem and a number of nonlinear subproblems. Numerical case studies based on IEEE 118-bus demonstrate the high performance of the proposed approach.