Search Results (6,177)

Groundwater is an important factor for the stability of the subway station pit constructed in the offshore area. To reflect the effects of groundwater drawdown on the stability of the station pit, this work uses a surface settlement formula based on Rayleigh distribution to construct a continuous deformation velocity field based on Terzaghi's mechanism, so as to derive a theoretical calculation method for the safety factor of the deep station pit anti-uplift considering the effect of seepage force. Taking the seepage force as an external load acting on the soil skeleton, a simplified calculation method is proposed to describe the variation in shear strength with depth. Substituting the external work rate induced by self-weight, surface surcharge, seepage force, and plastic shear energy into the energy equilibrium equation, an explicit expression of the safety factor of the station pit is obtained. According to the parameter study and engineering application analysis, the validity and applicability of the proposed procedure are discussed. The parameter study indicated that deep excavation pits are significantly affected by construction drawdown and seepage force; the presence of seepage, to some extent, reduces the anti-uplift stability of the station pit. The calculation method in this work helps to compensate for the shortcomings of existing methods and has a higher accuracy in predicting the safety and stability of station pits under seepage situations. Full article

The article presents research on concrete creep in bridge structures, focusing on the influence of concrete mix composition and the use of advanced rheological models with fractional-order derivatives. Laboratory tests were performed on nine mixes varying in blast furnace slag content (0%, 25%, and 75% of cement mass) and air-entrainment. The results were used to calibrate fractal rheological models—Kelvin–Voigt and Huet–Sayegh—where the viscous element was replaced with a fractal element. These models showed high agreement with experimental data and improved the accuracy of creep prediction. Comparison with Eurocode 2 revealed discrepancies up to 64%, especially for slag-free concretes used in prestressed bridge structures. The findings highlight the important role of mineral additives in reducing creep strains and the need to consider individual mix characteristics in design calculations. In the context of modern bridge construction technologies, such as balanced cantilever or incremental launching, reliable modeling of early-age creep is particularly important. The proposed modeling approach may enhance the precision of long-term structural behavior analyses and contribute to improved safety and durability of concrete infrastructure. Full article

Background: Multi-epitope vaccines have become the preferred strategy for protection against infectious diseases by integrating multiple MHC-restricted T-cell and B-cell epitopes that elicit both humoral and cellular immune responses against pathogens. Computational methods address various aspects independently, yet their orchestration is technically challenging, as most bioinformatics tools are accessible through heterogeneous interfaces and lack interoperability features. The present work proposes a novel framework for rationalized multi-epitope vaccine design that streamlines end-to-end analyses through an integrated web-based environment. Results: VaccineDesigner is a comprehensive web-based framework that streamlines the design of protective epitope-based vaccines by seamlessly integrating computational methods for B-cell, CTL, and HTL epitope prediction. VaccineDesigner incorporates single-epitope prediction and evaluation as well as additional analyses, such as multi-epitope vaccine generation, estimation of population coverage, molecular mimicry, and proteasome cleavage. The functionalities are transparently integrated into a modular architecture, providing a single access point for rationalized, multi-epitope vaccine generation in a time- and cost-effective manner. Conclusions: VaccineDesigner is a web-based tool that identifies and evaluates candidate B-cell, CTL, and HTL epitopes and constructs a library of multi-epitope vaccines that combine strong immunogenic responses, safety, and broad population coverage. The source code is available under the academic license and freely accessible. Full article

The integration of robotic agents into complex indoor construction environments is increasing, particularly through human–robot collaboration (HRC) and multi-robot collaboration (MRC). These collaborative frameworks hold great potential to enhance productivity and safety. However, indoor construction environments present unique challenges, such as dynamic layouts, constrained spaces, and variable lighting conditions, which complicate the safe and effective deployment of collaborative robot teams. Existing studies have primarily addressed various HRC and MRC challenges in manufacturing, logistics, and outdoor construction, with limited attention given to indoor construction settings. To this end, this review presents a comprehensive analysis of human–robot and multi-robot collaboration methods within various indoor domains and critically evaluates the potential of adopting these methods for indoor construction. This review presents three key contributions: (1) it provides a structured evaluation of current human–robot interaction techniques and safety-enhancing methods; (2) it presents a summary of state-of-the-art multi-robot collaboration frameworks, including task allocation, mapping, and coordination; and (3) it identifies major limitations in current systems and provides research directions for enabling scalable, robust, and context-aware collaboration in indoor construction. By bridging the gap between current robotic collaboration methods and the needs of indoor construction, this review lays the foundation for the development of adaptive and optimized collaborative robot deployment frameworks for indoor built environments. Full article

The changing global climate and rapid urbanization make extreme rainstorm events frequent, and the flood disaster caused by rainstorm has become a prominent problem of urban public safety in China, which severely restricts the healthy and sustainable development of social economy. The weight calculation method of traditional risk assessment model is single and ignores the difference of multi-dimensional information space involved in risk analysis. This study constructs a flood risk assessment model by incorporating natural, social, and economic factors into an indicator system structured around four dimensions: hazard, exposure, vulnerability, and disaster prevention and mitigation capacity. A combination of the Analytic Hierarchy Process (AHP) and the entropy weight method is employed to optimize both subjective and objective weights. Taking the central urban area of Wuhan with a high flood risk as an example, based on the risk assessment values, spatial autocorrelation analysis, cluster analysis, outlier analysis, and hotspot analysis are applied to explore the spatial clustering characteristics of risks. The results show that the overall assessment level of flood hazard in central urban area of Wuhan is medium, the overall assessment level of exposure and vulnerability is low, and the overall disaster prevention and mitigation capability is medium. The overall flood risk levels in Wuchang and Jianghan are the highest, while some areas in Qingshan and Hanyang have the lowest levels. The spatial characteristics of each dimension evaluation index show obvious autocorrelation and spatial differentiation. These findings aim to provide valuable suggestions and references for reducing urban disaster risks and achieving sustainable urban development. Full article

The increasing frequency and intensity of wildfires pose serious threats to ecosystems, property, and human safety worldwide. Accurate semantic segmentation of wildfire images is essential for real-time fire monitoring, spread prediction, and disaster response. However, existing deep learning methods heavily rely on large volumes of pixel-level annotated data, which are difficult and costly to obtain in real-world wildfire scenarios due to complex environments and urgent time constraints. To address this challenge, we propose a semi-supervised wildfire image segmentation framework that enhances segmentation performance under limited annotation conditions by integrating multi-scale structural information fusion and pixel-level contrastive consistency learning. Specifically, a Lagrange Interpolation Module (LIM) is designed to construct structured interpolation representations between multi-scale feature maps during the decoding stage, enabling effective fusion of spatial details and semantic information, and improving the model’s ability to capture flame boundaries and complex textures. Meanwhile, a Pixel Contrast Consistency (PCC) mechanism is introduced to establish pixel-level semantic constraints between CutMix and Flip augmented views, guiding the model to learn consistent intra-class and discriminative inter-class feature representations, thereby reducing the reliance on large labeled datasets. Extensive experiments on two public wildfire image datasets, Flame and D-Fire, demonstrate that our method consistently outperforms other approaches under various annotation ratios. For example, with only half of the labeled data, our model achieves 5.0% and 6.4% mIoU improvements on the Flame and D-Fire datasets, respectively, compared to the baseline. This work provides technical support for efficient wildfire perception and response in practical applications. Full article

To enhance the ability to identify and analyze the construction safety risks of prefabricated building projects, this paper explores the risk factors affecting the construction safety of prefabricated buildings from the perspective of the construction stage. Based on the WSR theory, this paper identifies risk-influencing factors from five dimensions: personnel, materials, management, technology, and environment, and constructs a safety risk assessment index system. This paper establishes a risk assessment model based on the G1-CRITIC method and cloud model. Firstly, it quantitatively analyzes the weights of the risk indicators for prefabricated building construction, and then evaluates the specific degree of impact of each indicator on the construction risk of this type of project. The research results show that the project is at the low-risk level, but there are still some potential risks in terms of material and technical factors, which require close attention and targeted management. The evaluation results obtained by applying this model are consistent with the current actual situation of prefabricated building construction, further demonstrating the applicability of this model. The risk assessment model proposed in this paper, by focusing on a specific type of risk, comprehensively incorporates the fuzziness and randomness of risk factors, thereby more effectively capturing the dynamic characteristics of risk evolution. This model can effectively evaluate the level of safety risk management and plays a positive role in reducing the incidence of engineering accidents. Furthermore, it also provides practical experience that can be drawn upon by risk managers of similar projects which holds significant theoretical value and practical guiding significance. Full article

In this study, an effective strengthening method was investigated to improve the seismic performance of masonry brick walls. The strengthening method comprised the use of shotcrete, which was applied in both glass fiber-reinforced and unreinforced forms for steel plates and tie rods. Thirteen wall specimens constructed with vertical perforated masonry block bricks were tested under diagonal compression in accordance with ASTM E519 (2010). Reinforcement plates with different thicknesses (1.5 mm, 2 mm, and 3 mm) were anchored using 6 mm diameter tie rods. A specially designed steel frame and an experimental loading program with controlled deformation increments were employed to simulate the effects of reinforced concrete beam frame system on walls under the effect of diagonal loads caused by seismic loads. In addition, numerical simulations were conducted using three-dimensional finite element models in Abaqus Explicit software to validate the experimental results. The findings demonstrated that increasing the number of tie rods enhanced the shear strength and overall behavior of the walls. Steel plates effectively absorbed tensile stresses and limited crack propagation, while the fiber reinforcement in the shotcrete further improved wall strength and ductility. Overall, the proposed strengthening techniques provided significant improvements in the seismic resistance and energy absorption capacity of masonry walls, offering practical and reliable solutions to enhance the safety and durability of existing masonry structures. Full article

Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article

To enhance road-testing safety for autonomous driving robotic vehicles (ADRVs), collision avoidance with sudden obstacles is essential during testing processes. This paper proposes an upper-level collision avoidance strategy integrating model predictive control (MPC) and improved artificial potential field (APF). The kinematic model of the driving robot is established, and a vehicle dynamics model considering road curvature is used as the foundation for vehicle control. The improved APF constraints are constructed. The boundary constraint uses a three-circle vehicle shape suitable for roads with arbitrary curvatures. A unified obstacle potential field constraint is designed for static/dynamic obstacles to generate collision-free trajectories. An auxiliary attractive potential field is designed to ensure stable trajectory recovery after obstacle avoidance completion. A multi-objective MPC framework coupled with artificial potential fields is designed to achieve obstacle avoidance and trajectory tracking while ensuring accuracy, comfort, and environmental constraints. Results from Carsim-Simulink and semi-physical experiments validate that the proposed strategy effectively avoids various obstacles under different road conditions while maintaining reference trajectory tracking. Full article

Accurate finite element (FE) models are essential for the safety assessment of civil engineering structures. However, obtaining reliable model parameters for existing bridges remains challenging due to the inability to conduct static load tests without disrupting traffic flow. To address this, this study proposes an FE model updating framework that integrates the response surface method and the nutcracker optimization algorithm (NOA). This framework is characterized by the incorporation of ambient vibration data into parameter optimization, thereby enhancing model accuracy. The stochastic subspace identification method is first adopted to extract the bridge’s natural frequencies from vibration data. The response surface method is then employed to construct a response surface function that approximates the FE model. The NOA is subsequently applied to iteratively optimize this response surface function, ensuring rapid convergence and the precise adjustment of the FE model parameter. To validate the effectiveness of the proposed framework, a continuous beam–arch composite bridge with a span of 204.783 m was selected as a case study. The results indicate that the proposed method reduced the average frequency error from 5.58% to 2.75% by updating the model parameters. While the whale optimization algorithm required 21 iterations and the grey wolf optimizer needed 41 iterations to converge near the minimum, the NOA achieved this in merely 13 iterations, demonstrating the NOA’s superior convergence speed. Furthermore, the NOA significantly outperformed both the whale optimization algorithm and the grey wolf optimizer in reducing the error of the first transverse vibration frequency. Full article

This study addresses the complex challenges associated with flexible mattress (soft mattress) installation in the sharply curved and deep-water sections of the Yangtze River, particularly in the Yaozui revetment reconstruction project. Under extreme hydrodynamic conditions—water depths exceeding 30 m and velocities over 2.5 m/s—the risk of structural failures such as displacement, flipping, or tearing of the mattress becomes significant. To improve construction safety and stability, the study integrates numerical modeling and on-site strain monitoring to analyze the mechanical response of flexible mattresses during deployment. A three-dimensional finite element model based on the catenary theory was developed to simulate stress distributions under varying flow velocities and angles, revealing stress concentrations at the mattress’s upper edge and reinforcement junctions. Concurrently, a real-time monitoring system using high-precision strain sensors was deployed on critical shipboard components, with collected data analyzed through a remote IoT platform. The results demonstrate strong correlations between mattress strain, flow velocity, and water depth, enabling the identification of high-risk operational thresholds. The proposed monitoring and early-warning framework offers a practical solution for managing construction risks in extreme riverine environments and contributes to the advancement of intelligent construction management for underwater revetment works. Full article

Improving construction site safety remains a critical challenge in Saudi Arabia’s rapidly growing construction sector, where high accident rates and diverse labor forces demand evidence-based managerial interventions. This study investigated the influence of Managers’ Safety Perceptions and Practices (MSP) on Workers’ Safety Behaviors (WSB) in the Saudi construction industry, emphasizing the mediating roles of Workers’ Safety Awareness (WSA), Safety Competency (WSC), and Safety Actions (SA). The conceptual framework integrates these three mediators to explain how managerial attitudes and practices translate into frontline safety outcomes. A quantitative, cross-sectional design was adopted using a structured questionnaire distributed among construction workers, supervisors, and project managers. A total of 352 from 384 valid responses were collected, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. The findings revealed that MSP does not directly influence WSB but has significant indirect effects through WSA, WSC, and SA. Among these, WSC emerged as the most powerful mediator, followed by WSA and SA, indicating that competency is the most critical driver of safe worker behavior. These results provide robust empirical support for a multidimensional mediation model, highlighting the need for managers to enhance safety behaviors not merely through supervision but through fostering awareness and competency, providing technical training, and implementing proactive safety measures. Theoretically, this study contributes a novel and integrative framework to the occupational safety literature, particularly within underexplored Middle Eastern construction contexts. Practically, it offers actionable insights for safety managers, industry practitioners, and policymakers seeking to improve construction safety performance in alignment with Saudi Vision 2030. Full article

Urban rail transit networks’ rapid expansions have led to increasing intersections between existing and new lines, particularly in dense urban areas where new stations must underpass existing infrastructure at zero distance. Deformation controls during construction are critical for maintaining the operational safety of existing stations, especially in soft soil conditions where construction-induced settlement poses significant risks to structural integrity. This study systematically investigates the influence mechanisms of different construction schemes on base plate deformation when an open-top UBIT (underground bundle composite pipe integrated by transverse pre-stressing) underpasses existing stations. Through precise numerical simulation using PLAXIS 3D, the research comparatively analyzed the effects of 12 pipe jacking sequences, 3 pre-stress levels (1116 MPa, 1395 MPa, 1674 MPa), and 3 soil chamber excavation schemes, revealing the mechanisms between the deformation evolution and soil unloading effects. The continuous jacking strategy of adjacent pipes forms an efficient support structure, limiting maximum settlement to 5.2 mm. Medium pre-stress level (1395 MPa) produces a balanced deformation pattern that optimizes structural performance, while excavating side chambers before the central chamber effectively utilizes soil unloading effects, achieving controlled settlement distribution with maximum values of −7.2 mm. The optimal construction combination demonstrates effective deformation control, ensuring the operational safety of existing station structures. These findings enable safer and more efficient urban underpassing construction. Full article

Nanotechnologies bring a rapid paradigm shift in hard and soft bone tissue regeneration (BTR) through unprecedented control over the nanoscale structures and chemistry of biocompatible materials to regenerate the intricate architecture and functional adaptability of bone. This review focuses on the transformative analyses and prospects of current and next-generation nanomaterials in designing bioactive bone scaffolds, emphasizing hierarchical architecture, mechanical resilience, and regenerative precision. Mainly, this review elucidated the innovative findings, new capabilities, unmet challenges, and possible future opportunities associated with biocompatible inorganic ceramics (e.g., phosphates, metallic oxides) and the United States Food and Drug Administration (USFDA) approved synthetic polymers, including their nanoscale structures. Furthermore, this review demonstrates the newly available approaches for achieving customized standard porosity, mechanical strengths, and accelerated bioactivity to construct an optimized nanomaterial-oriented scaffold. Numerous strategies including three-dimensional bioprinting, electro-spinning techniques and meticulous nanomaterials (NMs) fabrication are well established to achieve radical scientific precision in BTR engineering. The contemporary research is unceasingly decoding the pathways for spatial and temporal release of osteoinductive agents to enhance targeted therapy and prompt healing processes. Additionally, successful material design and integration of an osteoinductive and osteoconductive agents with the blend of contemporary technologies will bring radical success in this field. Furthermore, machine learning (ML) and artificial intelligence (AI) can further decode the current complexities of material design for BTR, notwithstanding the fact that these methods call for an in-depth understanding of bone composition, relationships and impacts on biochemical processes, distribution of stem cells on the matrix, and functionalization strategies of NMs for better scaffold development. Overall, this review integrated important technological progress with ethical considerations, aiming for a future where nanotechnology-facilitated bone regeneration is boosted by enhanced functionality, safety, inclusivity, and long-term environmental responsibility. Therefore, the assimilation of a specialized research design, while upholding ethical standards, will elucidate the challenge and questions we are presently encountering. Full article