Search Results (337)

For arch dam seismic safety evaluation, the finite element equivalent stress method has been widely used, and existing studies have realized mature equivalent stress calculation along the foundation surface path. However, from the scientific research perspective, there is a lack of a full dam surface equivalent stress characterization method for arch dams under seismic action; from the engineering practice perspective, the traditional path method cannot fully reflect the overall stress distribution of the dam, leading to insufficient comprehensive safety evaluation. To accurately assess the impact of seismic action on the overall structural safety of arch dams and address the above limitations, this study develops a methodology for calculating equivalent stress across the entire dam surface of arch dams under seismic action. Taking a concrete arch dam as the research object, a seismic wave input method based on viscoelastic artificial boundaries is employed. Three-dimensional finite element analysis of the arch dam is performed using ABAQUS, integrated with Python-based secondary development to extract stress along the integration path of each arch ring layer and calculate sectional internal forces. The equivalent stress of each arch ring layer integration path is then processed using the material mechanics method to obtain the equivalent stress distribution across the entire dam surface. A comparative analysis is conducted between the equivalent stress on the entire dam surface and that along paths on the foundation surface regarding the seismic dynamic response and behavioral patterns of the dam. The results demonstrate that the full dam surface equivalent stress approach not only accurately captures the extreme tensile and compressive stress values in the downstream foundation area but also identifies stress extrema in the upstream dam crest region, thereby achieving comprehensive characterization of the dam stress field under seismic action and enhancing both the efficiency and accuracy of equivalent stress calculations for arch dams. This method provides more comprehensive and reliable data support for seismic design optimization and reinforcement of arch dams. Compared with the traditional foundation surface path method, the proposed method achieves 100% identification of the whole dam surface stress extremum areas, with a maximum relative error of only 1.62% in the overlapping calculation area. Full article

The rapid development of digital technologies presents both a challenge and an opportunity for strengthening sustainability in construction project management. Within the broader digitalization agenda, Building Information Modelling (BIM) and Artificial Intelligence (AI) have emerged as key tools for improving environmental and economic performance through resource optimization. While traditional methods for optimizing resources, costs, and time remain relevant, the integration of BIM and AI introduces innovative capabilities that support decision-making, process automation, and data-driven sustainability strategies. The aim of this research is to analyze the extent to which BIM and AI are used for sustainable resource optimization in construction and to quantify their potential impact on the optimization of costs, resources, and time in the sector. A cross-sectional survey was conducted among construction companies operating in three European markets, Slovakia, Slovenia, and Croatia. The collected data were analyzed using descriptive statistics, correlation and regression analysis, and statistical hypothesis testing to assess the significance of relationships between technology adoption and sustainability outcomes. The results confirm that BIM adoption is positively correlated with improved sustainability management and optimization practices, with usage levels varying by company size and project scale. In contrast, AI adoption remains at a low level, indicating untapped potential for broader application. These findings contribute to understanding the role of digital tools in driving sustainable transformation in the construction sector and highlight areas for further research and practical deployment. BIM demonstrates particularly strong correlations with cost planning (r = 0.983), resource planning (r = 0.964), and schedule planning (r = 0.867), while AI shows robust associations with cost planning (r = 0.925), schedule planning (r = 0.865), and resource planning (r = 0.809). The findings indicate that maximum effectiveness is achieved when BIM and AI are deployed in a complementary manner under skilled human oversight. Full article

Biochar activation is critical for producing high-performance adsorbents; however, conventional activation methods are energy-intensive and difficult to control, particularly when air is used as an activating agent. This study investigates CO₂–air co-activation in a laboratory-scale fluidized bed reactor as an energy-efficient alternative. Experiments were conducted at 750–850 °C under varying gas flow rates with a constant CO₂/O₂ ratio. Optimal properties were achieved at 800 °C and 0.2–0.3 L/min CO₂, yielding a maximum BET surface area of 479 m²/g, a micropore contribution of 42%, and controlled carbon conversion (~25–35% yield). Aspen Plus equilibrium simulations also confirm that CO₂-only activation remains endothermic (heat duty up to +0.07 kW), air-only activation becomes strongly exothermic (down to −0.13 kW), while the CO₂–air mixture exhibits near-thermoneutral to mildly exothermic behavior (+0.13 to −0.10 kW), thereby reducing external energy demand potentially by approximately 60–70% compared with CO₂-only activation and significantly improving process stability. These results demonstrate that CO₂–air co-activation offers a practical route to produce high-quality activated biochar with controlled porosity and improved energy efficiency. Full article

As hydrogen mobility gains increasing importance, the number of hydrogen refueling stations (HRSs) worldwide is expanding rapidly. Hydrogen compression is a critical component of every HRS, exerting a direct and decisive influence on operability, performance, economic viability, downtime, safety, and public acceptance. Given this central role, this work presents a comprehensive overview of the hydrogen compression landscape, critically examining both conventional mechanical systems—such as piston and diaphragm compressors—and emerging non-mechanical technologies, including electrochemical and metal hydride compressors. The analysis also addresses novel hybrid approaches that combine methods to exploit their respective strengths. Each technology is assessed against a consistent set of practical criteria, encompassing not only fundamental performance metrics such as maximum discharge pressure and flow capacity but also key considerations relevant to real-world deployment. This review provides a detailed comparison of all hydrogen compression technologies with respect to energy efficiency, maintenance needs and intervals, capital expenditures (CAPEX), operating expenditures (OPEX), and Technology Readiness Level (TRL). Additional factors—including physical size, noise levels, and effects on hydrogen purity—are also evaluated, as they strongly influence the suitability for applications in urban or remote areas. By synthesizing recent scientific literature, industry data, and applicable technical standards, this work develops a structured multi-criteria framework that translates technical insights into practical guidance and a clear technology selection roadmap. The overarching objective is to equip engineers, station developers, operators, and policymakers with the knowledge needed to make informed and optimized decisions about hydrogen compression during HRS planning and design. Full article

Water scarcity in arid/semi-arid regions restricts agricultural sustainability systems and hinders the achievement of regional sustainable development goals, especially in northwest China’s extremely arid areas, where acute water supply–demand conflicts and inefficient traditional practices intensify competition for water between agricultural and ecological sectors. This study aims to verify the effectiveness of an intelligent automatic irrigation system in mitigating water scarcity pressures and enhancing agricultural sustainability in the Shule River Basin of northwestern China, a region where traditional irrigation methods not only yield suboptimal crop outputs but also undermine long-term water resource sustainability. A smart irrigation module, integrating “sensing–decision–execution” processes, was embedded within a digital twin platform to enable precise, resource-efficient water management that aligns with sustainable development principles. Sunflower (Helianthus annuus L.), the most popular cash crop in the area, was used as the test crop, with three soil moisture-based irrigation levels compared against traditional farmer practices. Key indicators including leaf area index (LAI), dry biomass, grain yield, and irrigation water use efficiency (IWUE) were systematically evaluated. The results showed that (1) LAI increased from the seedling to flowering stage, with smart irrigation treatments significantly outperforming farmer practices in both crop growth and water-saving effects, laying a foundation for sustainable yield improvement; (2) total dry biomass at maturity was positively correlated with irrigation amount but smart irrigation optimized the allocation of water resources to avoid waste, balancing productivity and sustainability; (3) grain yield peaked within 70–89% field capacity (fc), with further increases leading to diminishing returns and unnecessary water consumption that impairs sustainable water use; (4) IWUE followed a parabolic trend, reaching its maximum under the same optimal irrigation range, indicating that smart irrigation can maximize water productivity while preserving water resources for ecological and future agricultural needs. The digital twin-driven smart irrigation system enhances both crop yield and water productivity in arid regions, providing a scalable model for precision water management in water-stressed agricultural zones. The results provide a key empirical basis and technical approach for sustainably using irrigation water, optimizing water–energy–food–ecology synergy, and advancing sustainable agriculture in arid regions of Northwest China, which is crucial for achieving regional sustainable development objectives amid worsening water scarcity. Full article

Accurate evaluation of the standard normal cumulative distribution function is fundamental in many areas of mathematics, statistics, and applied computation, yet no closed-form expression in elementary functions exists. We present a simple analytic approximation based on a logistic function with a cubic argument, designed to preserve symmetry, monotonicity, and analytic invertibility. The parameters of the approximation are obtained through numerical optimization over a wide domain, targeting both maximum absolute error and root-mean-square error. The resulting function achieves uniformly low approximation error and significantly reduces error relative to the classical logistic approximation, while remaining competitive with commonly used high-accuracy numerical methods. Unlike rational or high-degree polynomial approximations, the proposed form admits an explicit inverse, making it convenient for applications requiring analytic quantile evaluation or inverse transform sampling. Numerical error analysis and illustrative examples demonstrate that the approximation provides a practical balance between accuracy, simplicity, and analytic tractability. Full article

Soil erosion is largely driven by climate change and land use dynamics. The objective of this study is to assess the dynamic variation in erosion under the combined effects of precipitation and land use change in the Tigrigra watershed, located in the mountainous region of the Middle Atlas. The RUSLE (Revised Universal Soil Loss Equation) model is used in the methodological approach to estimate soil loss based on various parameters such as precipitation, soil, topography, land cover, and conservation practices. Geographic Information Systems (GIS) and remote sensing tools are essential for applying this method. In addition, the CA-Markov model (cellular automata), which models and predicts land use changes over time, is used to project future land cover scenarios that influence soil erosion dynamics. The research focuses on four previous periods (1991–2000, 2001–2010, 2011–2015, and 2016–2023), as well as a future period (2024–2050), considering two climate scenarios, RCP 2.6 and RCP 4.5. Precipitation data from local weather stations and the CMIP5 climate model were used to calculate the R factor (precipitation erosivity). Land cover analysis was performed using Landsat satellite images (30 m resolution) integrated into the CA-Markov model to calculate the C factor (land cover management). The results show that erosion has gradually decreased over both past and future periods, mainly due to variations in precipitation and vegetation cover. It should be noted that the period from 1991–2000 to 2016–2023 shows higher erosion compared to the future periods, with a maximum value of 17.83 t/ha/year recorded between 1991 and 2000. For the future period 2024–2050, a continuous decrease in erosion is observed under both scenarios, with an average value of 15.30 t/ha/year for the RCP2.6 scenario and 15.86 t/ha/year for the RCP4.5 scenario, with erosion remaining slightly higher under RCP4.5. Overall, erosion decreases across both historical (1991–2023) and projected (2024–2050) periods due to reduced rainfall erosivity. The northern part of the basin is particularly prone to erosion due to the low vegetation cover. The results indicate that areas susceptible to erosion require conservation measures to reduce soil loss. Implementing sustainable agricultural practices is crucial for maintaining long-term soil health and preventing degradation. However, some limitations of the study, such as the lack of data on conservation practices and daily precipitation, might affect the overall robustness of the findings. Full article

With the rapid development of the Internet of Things (IOT), 5G, and modern power systems, demand-side loads are becoming increasingly observable and remotely controllable, which enables demand-side flexibility to participate more actively in grid dispatch and emergency support. Under extreme rainstorm conditions, however, component failure risk rises and the availability and dispatchability of demand-side flexibility can change rapidly. This paper proposes a risk-aware emergency regulation framework that translates rainstorm information into actionable multi-load aggregation decisions for urban power systems. First, demand-side resources are quantified using four response attributes, including response speed, response capacity, maximum response duration, and response reliability, to enable a consistent characterization of heterogeneous flexibility. Second, a backpropagation (BP) neural network is trained on long-term real-world meteorological observations and corresponding reliability outcomes to estimate regional- or line-level fault probabilities from four rainstorm drivers: wind speed, rainfall intensity, lightning warning level, and ambient temperature. The inferred probabilities are mapped onto the IEEE 30-bus benchmark to identify high-risk areas or lines and define spatial priorities for emergency response. Third, guided by these risk signals, a two-level coordination model is formulated for a load aggregator (LA) to schedule building air conditioning loads, distributed photovoltaics, and electric vehicles through incentive-based participation, and the resulting optimization problem is solved using an adaptive genetic algorithm. Case studies verify that the proposed strategy can coordinate heterogeneous resources to meet emergency regulation requirements and improve the aggregator–user economic trade-off compared with single-resource participation. The proposed method provides a practical pathway for risk-informed emergency regulation under rainstorm conditions. Full article

In recent years, with the improvement of unmanned aerial vehicle (UAV) performance, various applications have been explored. In environments such as disaster areas, where existing infrastructure may be damaged, alternative uplink communication for transmitting observation data from UAVs to the ground station (GS) is critical. However, conventional mobile ad hoc network (MANET) routing protocols do not sufficiently account for GS-oriented traffic or the highly mobile UAV topology. This study proposed a flying ad hoc network (FANET) routing protocol that introduces a control option called GS flood, where the GS periodically disseminates routing information, enabling each UAV to efficiently acquire fresh source routes to the GS. Evaluation using NS-3 in a disaster scenario confirmed that the proposed method achieves a higher packet delivery ratio and practical latency compared to the representative MANET routing protocols, namely DSR, AODV, and OLSR, while operating with fewer control IP packets than existing methods. Furthermore, although the multihop throughput between UAVs and the GS in the proposed method plateaued at approximately 40% of the physical-layer maximum, it demonstrated performance exceeding realistic satellite uplink capacities ranging from several hundred kbps to several Mbps. Full article

In this paper, we take the longitudinal two-stage and two-yard EMU (Electric Multiple Unit) depot as an example and discusses the optimization challenges of the first-level maintenance shunting operation plan under the background of limited maintenance capacity. A multi-objective programming is constructed, which adopts the lexicographic ordering method and aims to minimize the occupancy time of key line areas and the number of train storage times. In order to enhance the flexibility and solution efficiency of the shunting operation plan, we design an efficient three-stage strategy algorithm. Specifically, in the first stage, the genetic and mutation rules are integrated, and the fast iterative advantage of the genetic algorithm is utilized to solve the time decision variables in the optimization problem. In the second stage, the allocation of track occupancy variables is further solved. The third stage focuses on the optimized allocation of maintenance team variables to ensure the scientific scheduling of maintenance resources. Finally, a validation experiment was conducted using the maintenance tasks of 19 EMU sets as the test scenario. The results indicate that when the number of maintenance teams is set to 4, an optimal balance between maintenance efficiency and operational cost is achieved, the occupancy duration of key line zones reaches 3034 min (the theoretical optimum), the number of maintenance teams is reduced by 33.33% compared to the initial 6 teams, and the number of storage operations is optimized to 27 times. Additionally, the algorithm’s solution time remains under 50 s, demonstrating significantly improved computational efficiency. Comparative experiments with baseline algorithms show that the proposed method reduces the occupancy duration of key line zones by up to 0.49%, decreases the number of storage operations by 14 times, and advances the maximum completion time by 20 min. In summary, the proposed method provides solid theoretical support for the formulation of maintenance plans and shunting schedules in EMU depots. Particularly in complex scenarios with limited maintenance capacity, it offers innovative and robust decision-making foundations, demonstrating significant practical guidance value. Full article

Maintaining the stability of the mine roadway is of paramount importance, as it is critical in ensuring the daily operational continuity, personnel safety, long-term economic viability, and sustainability of the entire mining operation. Significant instability can trigger serious disruptions—such as production stoppages, equipment damage, and severe safety incidents—which ultimately compromise the project’s financial returns and future prospects. Therefore, the proactive assessment and rigorous control of roadway stability constitute a foundational element of successful and sustainable resource extraction. In China, thick and extra-thick coal seams constitute over 44% of the total recoverable coal reserves. Consequently, their safe and efficient extraction is considered vital in guaranteeing energy security and enhancing the efficiency of resource utilization. The surrounding rock of gob-side roadways in typical coal seams is often fractured due to high ground stress, intensive mining disturbances, and overhanging goaf roofs. Consequently, asymmetric failure patterns such as bolt failure, steel belt tearing, anchor cable fracture, and shoulder corner convergence are common in these entries, which pose a serious threat to mine safety and sustainable mining operations. This deformation and failure process is associated with several parameters, including the coal seam thickness, mining technology, and surrounding rock properties, and can lead to engineering hazards such as roof subsidence, rib spalling, and floor heave. This study proposes countermeasures against asymmetric deformation affecting gob-side entries under intensive mining pressure during the fully mechanized caving of extra-thick coal seams. This research selects the 8110 working face of a representative coal mine as the case study. Through integrated field investigation and engineering analysis, the principal factors governing entry stability are identified, and effective control strategies are subsequently proposed. An elastic foundation beam model is developed, and the corresponding deflection differential equation is formulated. The deflection and stress distributions of the immediate roof beam are thereby determined. A systematic analysis of the asymmetric deformation mechanism and its principal influencing factors is conducted using the control variable method. A support approach employing a mechanical constant-resistance single prop (MCRSP) has been developed and validated through practical application. The findings demonstrate that the frequently observed asymmetric deformation in gob-side entries is primarily induced by the combined effect of the working face’s front abutment pressure and the lateral pressure originating from the neighboring goaf area. It is found that parameters including the immediate roof thickness, roadway span, and its peak stress have a significant influence on entry convergence. Under both primary and secondary mining conditions, the maximum subsidence shows an inverse relationship with the immediate roof thickness, while exhibiting a positive correlation with both the roadway span and the peak stress. Based on the theoretical analysis, an advanced support scheme, which centers on the application of an MCRSP, is designed. Field monitoring data confirm that the peak roof subsidence and two-side closure are successfully limited to 663 mm and 428 mm, respectively. This support method leads to a notable reduction in roof separation and surrounding rock deformation, thereby establishing a theoretical and technical foundation for the green and safe mining of deep extra-thick coal seams. Full article

Surface subsidence caused by high-intensity coal mining in the western mining area will have a negative impact on the environment. Mining subsidence has the characteristics of large scope, long duration, and strong destructiveness. In order to deeply understand the law of surface movement and deformation under the high-intensity mining of coal mines in western China, taking the Caojiatan 122,106 working face as an example, this study was conducted to obtain the surface movement characteristics and law by the method of surface rock movement measurement. The results showed that the surface subsidence in this study is mainly divided into three stages: start-up stage, active stage, and recession stage, with the active stage characterized by abrupt and intensive settlement. The maximum measured subsidence reached 4.173 m along the strike and 3.350 m along the dip. Numerical simulations further demonstrated strong vertical connectivity within the overburden, with surface subsidence area covering approximately 2/3 of the direct roof area. The predicted maximum subsidence values from simulation were 4.21 m (strike) and 3.36 m (dip), closely aligning with field data. A probability integral model was calibrated using observed data, yielding key parameters: subsidence coefficient = 0.537, main influence angle tangent = 4.435, horizontal movement coefficient = 0.20, inflection point offset = 76.90 m, and propagation angle = 86.2°. This study provides a validated methodology for predicting surface deformation in western mining areas and offers practical insights for subsidence mitigation and land restoration. Full article

Dewatering of sewage sludge is a key operational element of wastewater treatment plants and has major economic implications, as it entails the costs of thickening, transport, and disposal. The aim of this study was to determine the influence of selected polyelectrolytes and their dosages on dewatering efficiency and to present an innovative, multicriteria method of result evaluation using radar charts. In this research, 10 different polyelectrolytes were assessed in terms of sludge dewaterability, considering conditioning parameters including Specific Resistance to Filtration (SRF), Capillary Suction Time (CST), and centrifugation performance. The results were presented in the form of radar charts, enabling both an overall evaluation of the effectiveness of each product and an assessment of their suitability for specific dewatering technologies, such as belt filter presses and centrifuges. The analysis showed that polyelectrolytes with higher cationic charge provided better dewatering performance. The proposed visualization method allows us to analyze the effects across different conditioners and technologies. The best sludge conditioning effect (maximum radar chart area) was achieved with Praestol 665, a polyelectrolyte with a high cationic charge level. This method is a practical tool for selecting the optimal agent for sewage sludge dewatering. Full article

Different cultivation methods significantly affect wheat quality. However, the optimal cultivation pattern for strong-gluten wheat in Shandong province remains unclear. Through field experiments conducted over three consecutive wheat-growing seasons, wheat-quality-related traits under traditional cultivation practices (TC) and different cultivation patterns for Jimai44 (a strong-gluten wheat variety) were investigated. Plowing, delayed sowing date and increasing seeding rate could enhance grain protein content, SDS sedimentation value, wet and dry gluten content, and also had a clear positive effect on thousand-kernel weight and test weight. Employing a protocol of increased basal nitrogen (300 kg/ha) and topdressing water and fertilizer twice significantly increased wheat grain protein and nitrogen content, flour yield, gluten index, SDS sedimentation value, dough stability time, and extensibility. On the basis of the two wheat seasons experiments, we developed an optimized cultivation practice (Opt, that is, combined with plowing, delayed sowing date, seeding rate of 3.15 million or 3.60 million, basal nitrogen fertilizer application of 300 kg/ha, topdressing fertilizer twice, topdressing water twice or three times). Compared with TC treatment, the optimized cultivation demonstrated superior performance in grain protein content, flour yield, SDS sedimentation value, wet and dry gluten content, stability time, formation time, extension area, extension, and maximum retensibility with high grain yield. Meanwhile, we found that the expression of TaGlu1 was significantly increased under the optimized cultivation practice. In summary, the optimized cultivation practice might be a promising approach for improving strong-gluten wheat quality in the Huang-Huai-Hai Plain. Full article

In order to solve the problem of inadequate confinement provided by traditional rectangular stirrups in concrete square columns under stringent seismic fortification requirements, a spiral stirrup with a better constraint effect was used in the square columns in this study. Through a comprehensive analysis of test results, numerical simulations, and theoretical derivations, the seismic performance and shear capacity calculation methods of concrete square columns confined with five-spiral composite stirrups were investigated. This study provides pertinent technical data to facilitate the engineering application of such columns. The existing low-cycle repeated loading tests of 13 concrete square columns confined with five-spiral composite stirrups were collected and analyzed; some of these specimens were selected for finite element numerical simulation, and the simulation results were compared with the test results. The results indicate that the hysteresis curves and skeleton curves obtained from the numerical simulation agree well with the experimental curves, which verifies the rationality of the numerical simulation model proposed in this paper; post-peak load behavior reveals a pronounced compound confinement effect attributable to the five-spiral stirrups; during mid-to-late loading stages, the tensile stress in small spiral stirrups at intersections with larger spirals escalates rapidly, resulting in maximum transverse confinement within these areas. Based on the validated numerical simulation approach, a comprehensive analysis was performed to investigate the effects of axial compression ratio, shear-span ratio, spacing of small spiral stirrups, and diameter ratio of large-to-small spiral stirrups on the seismic performance of the specimens. The results demonstrate that when the spacing of large and small spiral stirrups is kept consistent, the specimens yield optimal strength and ductility. With the diameter of the central large-spiral stirrup fixed, either an increase or a decrease in the diameter of small spiral stirrups will induce varying degrees of reduction in both strength and ductility of the specimens. Furthermore, the five-spiral reinforced columns achieve the best overall seismic performance when the diameter of the central large spiral stirrup reaches the maximum allowable value for the cross-section, and the diameter of small spiral stirrups is set to one-third that of the large spiral stirrup. Finally, the shear mechanism and influencing factors of the shear capacity of the concrete square columns confined with five-spiral composite stirrups were discussed, and a practical formula for calculating the shear capacity of such columns was proposed. Full article