Search Results (702)

Progressive collapse is characterized by disproportionate structural failure triggered by localized damage, such as column loss under extreme loading conditions. The objective of this study is to develop a simplified analytical model that is applicable in engineering practice without the need for high-fidelity nonlinear finite element analysis. Although current design guidelines (GSA and DoD) provide analytical procedures and acceptance criteria, they do not explicitly address the tensile resistance of girders after the acceptance criteria are satisfied, particularly under large deformation and connection failure. To address this limitation, this study proposes a simplified theoretical load–displacement model for a fixed-end girder subjected to three concentrated loads, considering the effects of secondary beams and focusing on the local girder response under a column-removal scenario. The proposed model incorporates moment–axial force interactions at plastic sections in the large-deformation range. Based on one-dimensional finite element analysis results, an early-developed axial force of 0.15Fₚ at the onset of the transition stage and a residual bending moment of 0.3Mₚ during the catenary action stage are explicitly introduced to better represent actual structural behavior. The girder response is idealized using five characteristic points: yielding (Y), full plasticity (P), transition initiation (T), pure catenary action initiation (C), and collapse governed by connection failure (F_conn). Stress distributions at plastic sections are analyzed using three-dimensional finite element models to establish stress-based formulations and a rational procedure for estimating axial force at collapse. The validity of the proposed model is verified through comparisons with finite element analysis results for girders with different span-to-depth ratios. The results demonstrate reasonable agreement in terms of collapse load and displacement, particularly for slender girders, confirming the applicability of the proposed model for progressive collapse assessment. Full article

Reinforced concrete conical shell silo roofs continue to present construction challenges, despite the widespread adoption of slip-form technology for silo walls. This study introduces a novel combined temporary support system for cast-in-place conical shell silo roofs, validated through an engineering case in Suiping. The proposed system consists of (i) an umbrella-type conical shell combined support structure and (ii) a cross-type vertical core-tube support. Focusing on the umbrella subsystem, a shell–truss framework is developed based on the geometry of cylindrical–conical shell roofs. Special structural components, along with prestressed reinforcement techniques, are introduced following the principles of structural and elastic mechanics. The traditional inclined-beam shoring concept is incorporated into an arched load path: inclined members are circumferentially connected at nodes to form a shell–arch support mechanism, thereby improving force transfer efficiency and reducing flexural demands. Finite element analyses of representative construction stages are conducted to evaluate displacement and stress responses. The results show that the proposed combined support system meets strength and stiffness requirements during roof construction and provides an efficient and practical solution for large-span conical shell silo roofs. Full article

High-precision localization of Mars rovers is essential for safe path planning and efficient navigation toward scientific targets. As planetary rovers traverse the surface, their positional uncertainty accumulates, which can be corrected through global localization by registering rover images to orbital maps. To date, image-based solutions are widely adopted; however, substantial manual intervention is often required, which is time-consuming and limits the range of autonomous navigation. To address these challenges, we propose a two-stage localization framework, comprising the Mars cross-view few-shot training paradigm (MarsCVFP), Mars cross-view feature extraction network (MCVN) trained under MarsCVFP, and a robust template matching algorithm. Specifically, the MarsCVFP model can leverage implicit cross-view feature as guidance without relying on a large amount of high-precision location-level supervision and explicitly annotated, specific learning targets in the scene. MCVN can capture discriminative fine-grained features on the weakly textured and unstructured surface of Mars by constructing the multi-scale feature pyramid structure (MSFPS) and the feature interaction module (FIM). We validate our framework on 85 unit-planned sites and 20 panoramic sites, respectively, as traversed by the Zhurong rover. The experimental results demonstrate that our framework consistently outperforms both the traditional approaches and the representative learning-based methods across diverse terrains, including dunes, bedrock, craters, and flat plains, achieving a localization success rate above 82% while maintaining a localization accuracy of better than 4 pixels, even under coarse prior positions uncertainties spanning 40 m × 40 m. Full article

Accurate perception of deformable objects on the lunar surface is essential for autonomous construction and in situ resource utilization (ISRU) missions. However, the lack of representative lunar imagery limits the development of data-driven models for pose estimation and manipulation. We present SynBag 1.0, a large-scale synthetic dataset designed for training and benchmarking six-degree-of-freedom (6-DoF) pose estimation algorithms on regolith-filled construction bags. SynBag 1.0 employs rigid-body representations of bag meshes designed to approximate deformable structures with varied levels of feature richness. The dataset was generated using a novel framework titled MoonBot Studio, built in Unreal Engine 5 with physically consistent lunar lighting, low-gravity dynamics, and dynamic dust occlusion modeled through Niagara particle systems. SynBag 1.0 contains approximately 180,000 labeled samples, including RGB images, dense depth maps, instance segmentation masks, and ground-truth 6-DoF poses in a near-BOP format. To verify dataset usability and annotation consistency, we perform zero-shot 6-DoF pose estimation using a state-of-the-art model on a representative subset of the dataset. Variations span solar azimuth, camera height, elevation, dust state, surface degradation, occlusion level, and terrain type. SynBag 1.0 establishes one of the first open, physically grounded datasets for 6-DoF-object perception in lunar construction and provides a scalable basis for future datasets incorporating soft-body simulation and robotic grasping. Full article

Cable-suspended structures are important auxiliary structures for the construction of long-span arch bridges. Due to topographic constraints, the cable-suspended structure of Liuchehe Bridge adopts an asymmetric structure form with a main span of 736 m. Nevertheless, research focusing on the mechanical behavior of large-span asymmetric cable hoisting structures remains limited at present. Under unfavorable loads, including temperature and cable saddle friction, tower buckling failure may occur in cable hoisting structures as a result of overstress. In addition, inappropriate changes in physical parameters and temperature of the main cable will alter its sag and consequently compromise construction precision. For the sake of the safety of the cable hoisting structure, a temperature gradient experiment was conducted on the steel pipes of the prefabricated tower by virtue of a practical engineering project. The change rule of the measured point temperature was analyzed, a temperature gradient pattern for tower steel pipes was proposed, and the deficiencies of the specifications were compensated for. On this basis, the effects of variations in temperature, main cable weight, main cable elastic modulus, guy cable tension, and saddle friction resistance on the mechanical behavior of the cable-suspended structure were analyzed. According to the temperature tests on the tower steel pipes, the maximum radial temperature gradient of the steel pipe section reaches 15 °C, which is higher than the thermal gradient value stipulated in the codes. Moreover, the steel pipe stress under the thermal gradient model proposed in the current research is greater than that under the thermal gradient model in the codes. The steel tube stress under the temperature gradient model adopted in this study is 7.6 times that specified in the design code. Temperature and the elastic modulus of the main cable have a significant influence on the mid-span deformation of the main cable. For every 1 °C temperature variation, the vertical displacement at the main cable mid-span changes by 25 mm. During the construction of the main cable, the sag of the main cable should be adjusted according to the rule governing temperature’s influence on the mid-span of the main cable to avoid elevation deviations of the main cable arising from temperature. Saddle frictional resistance exerts a notable effect on tower deformation, guy cable tension, and tower stress. At a friction coefficient of 0.3, the stress caused by friction in the steel tube at the tower bottom constitutes 35.1% of the total stress under the maximum design hoisting load. During construction, the free rotation of rollers at the saddle should be ensured to reduce the mechanical response of the structure. The findings of this study can provide a basis for the design and construction of long-span asymmetric cable-suspended structures. Full article

Addressing the issues of the complex mechanical responses and significant spatial effects of asymmetric large-span cable-stayed steel box girder bridges with shared public-rail traffic under operational loads (live load, static wind, and structural temperature differences), this paper uses the Lijiatuo Yangtze River Double-Line Bridge on Chongqing Metro Line 18 as the engineering background to construct multi-scale finite element models for the entire bridge and the closure segment, and validates them against GNSS displacement and strain monitoring data from the actual bridge. The study shows that the spatiotemporal asymmetry of operational live loads induces significant lateral bias effects in the main bridge, resulting in reverse displacements in the mid-span section, and with stress distributions characterized by “oscillation in the side spans and concentration in the mid-span.” The study also shows that, under static wind loads, the bridge’s lateral displacement approximately increases linearly with wind speed, and the mid-span response is higher than that of the side spans, showing significant spatial sensitivity to wind loads. Finally, the study shows that, although the system temperature difference causes small overall displacements, it still induces symmetrical lateral deformations and local stress concentrations near the closure segment. Local refined analyses further reveal the displacement distribution mechanism of the closure segment under operational loads. The health monitoring data agree well with the simulation results, validating the reliability of the numerical model. The research systematically reveals the spatial mechanical behavior of such bridges under operational loads, providing theoretical basis and engineering references for the design optimization and safety monitoring of similar asymmetric cable-stayed bridges. Full article

Focusing on the construction of a 58-m-diameter double-layer steel space frame dome at the Han Culture Museum Assembly Hall, this study addresses scheme selection and safety control challenges in staggered jacking of large-span spatial structures. A three-dimensional finite element model in MIDAS Gen simulated the three-stage jacking process to compare three temporary support layouts. Numerical evaluation metrics included maximum vertical displacements, peak internal forces, the proportion of members undergoing stress state transitions, and spatio-temporal evolution of stress concentrations. Scheme B demonstrated superior performance, reducing peak vertical displacement by 44% under critical conditions, lowering peak stresses, and enabling more uniform internal force redistribution—effectively mitigating tension–compression cycling and buckling risks. Crucially, only nodal displacements and support elevations were monitored in situ using a 3D system based on magnetic prisms and total stations; no strain or force measurements were conducted due to practical constraints during construction. Monitoring data show good agreement with simulated displacements and support elevations under Scheme B, validating the model’s deformation response. However, localized deviations—including a 29 mm deflection discrepancy and elevation errors up to 28 mm—reveal the influence of uneven boundary conditions, with potential implications for long-term structural behavior. The findings confirm that numerical predictions of deformation are reliable, while internal forces remain unvalidated by field data. The integrated approach of “scheme comparison–construction simulation–full-process displacement monitoring” proves effective for safety control and decision-making in complex jacking operations, offering a transferable framework for similar large-span double-layer space frame projects. Full article

Driven by cleaner energy demands, environmental regulations, and technological advances, coal science is rapidly evolving, creating the need to understand its transition and transformation within the global energy research landscape. Building upon earlier national- and topic-specific bibliometric studies, this study presents a comprehensive long-term global bibliometric analysis of coal research (1975–2024), based on 272,370 Web of Science records, applying the Cross-Disciplinary Publication Index (CDPI), the Technology–Economic Linkage Model (TELM), VOSviewer, and Excel to assess research growth, structural shifts, and interdisciplinary integration. Results show that coal research is dominated by articles (74%) with publication output peaking at ~19,500 in 2024, reflecting fluctuations in global coal prices due to energy transition market dynamics. CDPI results highlight Energy & Fuels (0.83), Chemical Engineering (0.80), Environmental Sciences (0.77), Materials Science (0.74), and Geosciences (0.66), showing coal’s central role across technology, environment, and geological research domains and revealing a clear shift toward sustainability-oriented and advanced material applications. China leads output (122,130 publications), with strong contributions from the China University of Mining and Technology and the Chinese Academy of Sciences, while the USA, Australia, and Europe maintain strong international collaboration networks. The evolution of coal research can be divided into three major phases: conventional mining, coal preparation, combustion, and coalbed methane commercialization (1975–2004; ~64,000 publications); integrated gasification combined cycle (IGCC) and carbon capture and storage (CCS) technologies (2005–2014; ~58,707 publications); and a recent phase dominated by by-product valorization, carbon capture utilization and storage (CCUS), and digital technologies (AI, IoT, ML) (2015–2024; ~146,174 publications). Contemporary coal research spans three interconnected domains: energy supply (≈36% of global electricity generation and ~15 Gt CO₂ emissions), resource and geoscience applications (including large-scale fly ash utilization and critical element recovery), and environmental and health impacts related to greenhouse gas and pollutant emissions. The findings demonstrate that coal science is transitioning from a conventional fossil fuel-centered discipline toward an integrated, interdisciplinary energy research field, emphasizing emission reduction, resource efficiency, digitalization, and circular economy applications, thereby extending prior bibliometric studies through unprecedented temporal coverage, global scope, and the combined application of CDPI and TELM frameworks, providing critical insights for future energy strategies and policy development. Full article

This study examined whether large language models (LLMs) can generate clinically realistic profiles of cognitive decline and whether simulated deficits reflect architectural constraints rather than superficial role-playing artifacts. Using GPT-4o-mini, we generated synthetic cohorts (n = 10 per group) representing healthy aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD), assessed through a conversational neuropsychological battery covering episodic memory, verbal fluency, narrative production, orientation, naming, and comprehension. Experiment 1 tested whether synthetic subjects exhibited graded cognitive profiles consistent with clinical progression (Control > MCI > AD). Experiment 2 systematically manipulated prompt context in AD subjects (short, rich biographical, and few-shot prompts) to dissociate robust from manipulable deficits. Significant cognitive gradients emerged (p < 0.001) across eight of thirteen domains. AD subjects showed impaired episodic memory (Cohen’s d = 4.71), increased memory intrusions, and reduced narrative length (d = 3.07). Critically, structurally constrained memory tasks (episodic recall, digit span) were invariant to prompting (p > 0.05), whereas generative tasks (narrative length, verbal fluency) showed high sensitivity (F > 100, p < 0.001). Rich biographical prompts paradoxically increased memory intrusions by 343%, indicating semantic interference rather than cognitive rescue. These results demonstrate that LLMs can serve as in silico test benches for exploring candidate digital biomarkers and clinical training protocols, while highlighting architectural constraints that may inform computational hypotheses about memory and language processing. Full article

The application of steel–concrete composite structures in the pylons of long-span cable-stayed bridges can effectively address the issue of insufficient structural stiffness. Shear connectors are critical load-transfer components in steel–concrete composite segments, where they are typically arranged to ensure coordinated force transmission between steel and concrete. The stud–PBL composite shear connector, as a novel type of connector, has been implemented in engineering practice. However, the collaborative load-bearing performance between studs and PBL connectors remains unclear. Most shear connectors operate within the elastic stage during service, making their elastic stiffness a key evaluation metric. Based on the Winkler elastic foundation beam theory, plane strain theory, and the spring series–parallel model, this study derives the elastic stiffness calculation formulas for stud shear connectors and PBL shear connectors, respectively. The primary focus of this study was the single-layer stud–PBL composite shear connector within the steel–concrete composite section of bridge pylons. Embedded push-out tests were designed and conducted, comprising three main categories and eight subcategories. The load–slip curves for the three types of shear connectors were generated, and the stiffness calculation formula for the stud–PBL composite shear connector was verified through finite element analysis. The comparative push-out tests and finite element simulations demonstrate that the theoretical formula proposed in this study can effectively analyze the elastic stiffness of three types of shear connectors. The elastic stiffness of composite shear connectors can be regarded as the superposition of the elastic stiffness of studs and PBL shear connectors. Compared with single shear connectors, composite shear connectors exhibit superior elastic stiffness and shear resistance, meeting the application requirements of steel–concrete composite bridge pylons. The research findings provide a theoretical basis for the optimal design of shear connectors in large-span cable-stayed bridge composite pylons. Furthermore, the established formula has broad applicability. Full article

Bacterial species differ dramatically in their growth rates, reflecting distinct ecological strategies and physiological constraints. Because protein synthesis is a major determinant of cellular replication, I examined how genomic investment in the translation machinery varies across bacteria with widely different doubling times. Using 20 bacterial species spanning two major bacterial kingdoms (Bacillati and Pseudomonadati), I quantified ribosomal RNA (rrn) operon number, total tRNA gene number, and the allocation of tRNA genes among amino acids. Rapidly replicating Vibrio natriegens has 11 rrn operons and 129 tRNA genes in its genome, whereas slowly replicating Borrelia burgdorferi has only one rrn operon and 32 tRNA genes. I show that both the rrn operon number and the tRNA gene number decline sharply with increasing generation time, a pattern observed independently within each bacterial kingdom. Moreover, tRNA gene allocation is highly non-uniform: amino acids that are frequently used in proteins and encoded by large synonymous codon families are supported by disproportionately more tRNA genes. This relationship is well described by a simple model incorporating amino acid usage and codon family size, as illustrated in rapidly growing species such as Vibrio natriegens and Clostridium perfringens. In contrast, slow-growing bacteria maintain relatively minimalist translation systems. Together, these results demonstrate that bacterial genomes are systematically optimized for translation in a manner tightly coupled to growth strategy, revealing how natural selection tunes both the capacity and structure of the translation machinery. Full article

With the increasing penetration of renewable energy sources and power-electronic devices, modern power systems exhibit pronounced wideband oscillation characteristics with large frequency spans, strong modal coupling, and significant time-varying behaviors. Accurate identification and classification of wideband oscillation patterns have therefore become critical challenges for ensuring the secure and stable operation of “dual-high” power systems. Existing methods based on signal processing or single-modality deep-learning models often fail to fully exploit the complementary information embedded in heterogeneous data representations, resulting in limited performance when dealing with complex oscillation patterns.To address these challenges, this paper proposes a multimodal attention-based fusion network for wideband oscillation classification. A dual-branch deep-learning architecture is developed to process Gramian Angular Difference Field images and raw time-series signals in parallel, enabling collaborative extraction of global structural features and local temporal dynamics. An improved Inception module is employed in the image branch to enhance multi-scale spatial feature representation, while a gated recurrent unit network is utilized in the time-series branch to model dynamic evolution characteristics. Furthermore, an attention-based fusion mechanism is introduced to adaptively learn the relative importance of different modalities and perform dynamic feature aggregation. Extensive experiments are conducted using a dataset constructed from mathematical models and engineering-oriented simulations. Comparative studies and ablation studies demonstrate that the proposed method significantly outperforms conventional signal-processing-based approaches and single-modality deep-learning models in terms of classification accuracy, robustness, and generalization capability. The results confirm the effectiveness of multimodal feature fusion and attention mechanisms for accurate wideband oscillation classification, providing a promising solution for advanced power system monitoring and analysis. Full article

The increasing frequency of interior renovation and fit-out in office buildings raises concerns about indoor environmental quality, occupant health, and sustainability performance, yet existing certification systems remain largely design-stage or whole-building oriented and provide limited guidance for recurring renovation cycles. This study develops a health-oriented assessment framework for office interior renovation as a structured decision-support tool for practitioners and policymakers. We adopted an integrated approach combining a targeted literature review, expert consultation, the Fuzzy Delphi Method (FDM) for indicator screening, and the Analytic Hierarchy Process (AHP) for hierarchical weighting, based on an expert panel of 20 professionals spanning green building certification, architecture/interior design, MEP engineering, property/facility management, and energy/environmental consulting. Through consensus screening and weighting, four assessment dimensions and eighteen key indicators were identified and prioritized. Environmental quality was ranked highest (39.2%), followed by safety management (23.0%), functional usability (21.1%), and resource efficiency and circularity (16.7%). At the indicator level, indoor air quality management, Heating, Ventilation and Air Conditioning (HVAC) energy efficiency, space-friendly layout, preliminary assessment and planning, and thermal comfort emerged as the top priorities. Overall, the framework bridges the gap between certification-oriented evaluation and the operational realities of office renovation, enabling more consistent integration of health and sustainability considerations across renovation decision-making. Full article

With the advancement of the dual carbon goals and the rapid increase in the proportion of new energy installations, the power system faces multiple challenges including insufficient flexibility resources, intensified fluctuations in generation and load, and reduced operational safety. Integrated energy systems (IESs), serving as key platforms for integrating diverse energy sources and flexible resources, possess complex internal structures and limited individual regulation capabilities, making direct participation in grid dispatch and market interactions challenging. To achieve large-scale resource coordination and efficient utilization, this paper investigates external characteristic modeling and cluster aggregation optimization methods for IES, proposing a comprehensive technical framework spanning from individual external characteristic identification to cluster-level coordinated control. First, addressing the challenge of unified dispatch for heterogeneous resources within IES, this study proposes an external characteristic modeling method based on operational feasible region projection. It constructs models for the active power output boundary, marginal cost characteristics, and ramping rate of virtual power plants (VPPs), enabling quantitative representation of their overall regulation potential. Second, a cluster aggregation optimization model for integrated energy systems is established, incorporating regional autonomy. This model pursues multiple objectives: cost–benefit matching, maximizing renewable energy absorption rates, and minimizing peak external power purchases. The Gini coefficient and Shapley value method are introduced to ensure fairness and participation willingness among cluster members. Furthermore, an optimization mechanism incorporating key constraints such as cluster scale, grid interaction, and regulation complementarity is designed. The NSGA-II multi-objective genetic algorithm is employed to efficiently solve this high-dimensional nonlinear problem. Finally, simulation validation is conducted on a typical regional energy scenario based on the IEEE-57 node system. Results demonstrate that the proposed method achieves average daily cost savings of approximately 3955 CNY under the optimal aggregation scheme, reduces wind and solar curtailment rates to 5.38%, controls peak external power purchases within 2292 kW, and effectively incentivizes all entities to participate in coordinated regulation through a rational benefit distribution mechanism. Full article

The demand for computing power has increased at a rate never seen before due to the quick development of artificial intelligence (AI) technologies and applications. Consequently, AI data centers, referring to computing facilities specifically designed for large-scale artificial intelligence workloads, have become one of the fastest-growing electricity consumers globally. Therefore, it is essential to understand the load characteristics of AI data centers and their impact on the grid. This paper provides a comprehensive review of the evolving energy landscape of AI data centers. Specifically, this paper (i) presents the energy consumption structure in AI data centers and analyzes the key workload features and patterns in four stages, emphasizing how high power density, temporal variability, and cooling requirements shape total energy use, (ii) examines the impacts of AI data centers for power systems, including impacts on grid stability, reliability and power quality, electricity markets and pricing, economic dispatch and reserve scheduling, and infrastructure planning and coordination, (iii) presents key technological, operational and sustainability challenges for AI data centers, including renewable energy integration, waste heat utilization, carbon-neutral operation, and water–energy nexus constraints, (iv) evaluates emerging solutions and opportunities, spanning grid-side measures, data-center-side strategies, and user-side demand-flexibility mechanisms, (v) identifies future research priorities and policy directions to enable the sustainable co-evolution of AI infrastructure and electric power systems. The review aims to support utilities, system operators, and researchers in maintaining reliable, resilient, and sustainable grid operation in the context of the rapid development of AI data centers. Full article