Search Results (6,143)

In urban construction, the efficient demolition of concrete structures imposes high-precision requirements on blasting technology. The mesoscopic evolution mechanism of concrete blasting damage is the key to optimizing blasting parameters. In this study, a numerical model of concrete blasting is established using Particle Flow Code (PFC). By comparing it with an experimental model containing a blast hole and a horizontal single fissure, the rationality and reliability of the model in simulating blasting damage evolution are verified. On this basis, four groups of control variable schemes are designed (concrete particle size distribution, aggregate content, prefabricated fissure inclination angle, and fissure length) to systematically explore the effects of mesoscopic structures and macroscopic defects on blasting damage. The results show that larger concrete particles make it easier for damage cracks to avoid large particles, forming sparse and irregular crack networks. A higher aggregate content enhances the “obstruction-guidance” effect of aggregate distribution on damage. When the aggregate content is 40%, the vertical damage expansion is the most prominent, reaching up to 3.05 cm. Fissure inclination angle affects the damage direction by guiding the propagation path of stress waves. Fissures inclined at 30°~60° serve as preferential damage channels, while 90° vertical fissures make vertical damage more significant. An increased fissure length expands the damage range, and the damage degree is the highest for a 40 mm long fissure, being 1.29 times that of a 30 mm fissure. The research results reveal the mesoscopic evolution laws of concrete blasting damage, providing a theoretical basis for the optimization of engineering blasting parameters and safety control. Full article

Due to the shortage of construction aggregates, carbonate rock aggregates—including mainly limestone aggregates—have long been used in structural concrete in many countries worldwide. On the other hand, earlier tests on the shear fracture toughness of concretes with limestone aggregates were very limited and were even abandoned for many years. For the above reasons, in this paper, completely new fracture toughness tests were performed according to the mode II fracture for limestone concretes with different grain size distributions. Two types of aggregate grain were used, i.e., two with maximum grain sizes of 8 mm (M1 series concrete) and 16 mm (M2 series concrete). During the experiments, the critical stress-intensity factor (K_IIc) and critical unit work of failure (J_IIc) were determined. Based on the conducted studies, it was found that higher values of fracture mechanics parameters were noted as the grain sizes of the aggregate used increased. The increases in the analyzed fracture mechanics parameters were noticeably greater in the M2 series concrete compared to the results for the M1 series concrete, specifically by 27% for K_IIc and 35% for J_IIc. In addition to macroscopic tests, detailed microstructural analyses of the ITZ area between the coarse aggregate grains and the cement matrix were conducted. Based on the captured images, it was determined that, in the M1 series concrete, the contacts between the aggregate grains and the cement paste exhibit a loose structure with visible microcracks. In contrast, the M2 series concrete showed no visible damages within the ITZ area itself nor at their displacement at a distance of approximately a few μm away from this area. This microstructure of both materials resulted in the M1 series concrete being more prone to rapid and sudden fracture propagation, leading to its brittle behavior during the fracture process. In contrast, the large, well-developed limestone aggregate grains in the M2 series concrete facilitated improved stress transfer beyond the ITZ area into the cement matrix, preserving the continuity of the material structure and consequently leading to quasi-plastic behavior of the concrete during the fracture process. The novelty and utilitarianism of the research undertaken result from the fact that exploring the properties of concretes with limestone aggregates using mode II fracture is an important aspect of evaluating the durability and safety of concrete structures subjected mainly to shear forces. Full article

The increasing adoption of industrialized offsite construction (IOC) offers substantial benefits in efficiency, quality, and sustainability, yet presents persistent challenges related to data fragmentation, real-time monitoring, and coordination. This systematic review investigates the transformative role of artificial intelligence (AI)-enhanced digital twins (DTs) in addressing these challenges within IOC. Employing a hybrid re-view methodology—combining scientometric mapping and qualitative content analysis—52 relevant studies were analyzed to identify technological trends, implementation barriers, and emerging research themes. The findings reveal that AI-driven DTs enable dynamic scheduling, predictive maintenance, real-time quality control, and sustainable lifecycle management across all IOC phases. Seven thematic application clusters are identified, including logistics optimization, safety management, and data interoperability, supported by a layered architectural framework and key enabling technologies. This study contributes to the literature by providing an early synthesis that integrates technical, organizational, and strategic dimensions of AI-driven DT implementation in IOC context. It distinguishes DT applications in IOC from those in onsite construction and expands AI’s role beyond conventional data analytics toward agentive, autonomous decision-making. The proposed future research agenda offers strategic directions such as the development of DT maturity models, lifecycle-spanning integration strategies, scalable AI agent systems, and cost-effective DT solutions for small and medium enterprises. Full article

Currently, there is a lack of methods for detecting the mechanism of gas explosion propagation within flameproof enclosures and the dynamic behavior of flameproof enclosures under explosion impact. Therefore, this paper studies a method for detecting the vibration characteristics of coal mine explosion-proof equipment under internal gas explosions using laser Doppler. First, a model of gas explosion propagation and explosion transmission response in flameproof enclosures is established to reveal the mechanism of gas explosion transmission inside coal mine flameproof enclosures. Second, a laser Doppler measurement method for coal mine flameproof enclosures is proposed, along with a step-by-step progressive vibration characteristic analysis method. This begins with a single-frequency dimension analysis using the Fourier transform (FFT), extends to time–frequency joint analysis using the short-time Fourier transform (STFT) to incorporate a time scale, and then advances to a three-dimensional linkage of scale, time, and frequency using the wavelet transform (DWT) to solve the limitation of the fixed window length of the STFT, thereby achieving a dynamic characterization of the detonation response characteristics. Finally, a non-symmetric Gaussian impact load inversion model is constructed to validate the overall scheme. The experimental results show that the FFT analysis identified a 2000 Hz main frequency, along with the global frequency components of the flameproof enclosure vibration signal, the STFT analysis revealed the dynamic evolution of the 2000 Hz main frequency and global frequency over time, and the wavelet transform achieved higher accuracy positioning of the frequency amplitude in the time domain, with better time resolution. Finally, the experimental platform showed an error of less than 5% compared with the actual measured impact load, and the error between the inverted impact load and the actual load was less than 15%. The experimental platform is feasible, and the inversion model has good accuracy. The laser Doppler measurement method has significant advantages over traditional coal mine flameproof equipment measurement and analysis methods and can provide further failure analysis and prevention, design optimization, and safety performance evaluation of flameproof enclosures in the future. Full article

In recent years, the deterioration of social infrastructure such as bridges has become a serious issue in many countries around the world. To maintain the functionality of aging bridges over the long term, it is necessary to conduct regular inspections, detect damage at an early stage, and perform timely repairs. However, inspections require significant cost and time, and ensuring the safety of inspectors remains a major challenge. As a result, inspection using robots has attracted increasing attention. This study focuses on a wire-driven bridge inspection robot designed to inspect the underside of bridge girders. To use this robot, wires must be installed in the space beneath the girders. However, it is difficult to install wires over areas such as rivers. To address this problem, we developed a robotic arm capable of throwing a spear attached to a string. In order to throw the spear accurately to the target location, a three-dimensional dynamic model of the spear in flight was constructed, considering the tension of the string. Using this model, we accurately estimated the required throwing conditions and confirmed that the robotic arm could successfully throw the spear to the target location. Full article

Graphene, owing to its exceptionally high specific surface area, abundant surface functional groups, and outstanding biocompatibility, exhibits tremendous potential in the development of nanodrug delivery systems. This review systematically outlines the latest research advancements regarding graphene and its derivatives in drug loading, targeted delivery, and smart release. It covers delivery strategies and mechanisms for various types of drugs, including small molecules and macromolecules, with a particular emphasis on their applications in major diseases such as cancer, neurological disorders, and infection control. The article also discusses stimulus-responsive release mechanisms, such as pH-responsiveness and photothermal responsiveness, and highlights the critical role of surface functionalization of graphene and its derivatives in enhancing therapeutic efficacy while reducing systemic toxicity. Furthermore, the review evaluates key challenges to the clinical translation of graphene-based materials, including safety, toxicity, and metabolic uncertainties. It points out that future research should focus on integrating structural modulation of materials with biological behavior to construct intelligent nanoplatforms featuring biodegradability, low immunogenicity, and precise therapeutic targeting. The aim of this paper is to provide theoretical insights and technical guidance for the customized design and precision medicine applications of graphene and its derivative-based drug delivery systems. Full article

Concrete bridges, as a vital component of modern transportation infrastructure, have their structural durability directly tied to safety and service life. In recent years, with the aging of bridge structures and increasingly complex environmental conditions, various durability-related deteriorations have become more prominent, significantly affecting structural performance and maintenance costs. This paper presents a systematic analysis of concrete carbonation as a key chemical process and its impact on durability-related pathologies. Particular attention is given to the formation mechanisms and influencing factors of critical deterioration modes such as cracking, reinforcement corrosion, and freeze–thaw damage. A multi-level prevention and mitigation strategy is proposed, encompassing optimized structural material design, strict construction quality control, and effective maintenance and repair techniques. The study concludes that the durability issues of concrete bridge structures exhibit a strong multi-factor coupling effect and proposes a core durability assurance framework. Finally, the paper briefly outlines emerging trends in intelligent monitoring and digital operation and maintenance, offering insights for future durability management of bridges. Full article

This study employs Geographic Information Systems (GIS) combined with multivariate statistical techniques to evaluate soil contamination at two landfill sites in Al-Kharj, Saudi Arabia. A total of 32 soil samples were collected and analyzed for heavy metals and metalloids (HMs) using a range of contamination indices and established soil quality standards. GIS mapping revealed that the Al-Kharj landfill 1 (Kj1) experienced a steady area expansion from 2014 through 2025, while landfill Kj2 expanded from 2014 until 2022, after which its area contracted following the construction of additional facilities. The average values of HMs observed were as follows: Fe (9909 mg/kg), Al (6709 mg/kg), Mn (155.9 mg/kg), Zn (36.4 mg/kg), Cr (24.1 mg/kg), V (22.2 mg/kg), Ni (19.5 mg/kg), Cu (8.20 mg/kg), Pb (7.91 mg/kg), Co (4.32 mg/kg), and As (2.29 mg/kg). Notably, Kj2 exhibited overall higher HM concentrations than Kj1, with particularly elevated levels of Cr, Ni, and Pb. Although most HMs remained within internationally accepted safety limits, only three samples (9.4% of the total) exceeded the WHO threshold for Pb (>30 mg/kg). An analysis using contamination and enrichment factors pointed to increased concentrations of Pb, Zn, and Cr, suggesting localized anthropogenic contributions. Additionally, all samples recorded an ecological risk index (Eri) below 40, and the levels of As, Cr, and Pb consistently stayed under their respective effects range-low (ERL) thresholds, indicating minimal contamination risks. The variations in HM contamination between the sites are likely attributable to differences in the sources of metal inputs and removal processes. These findings highlight the need for continuous monitoring and localized remediation strategies to ensure environmental safety and sustainable landfill management. Full article

Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, whereas V2N primarily focuses on infotainment and information exchange. Achieving reliable V2V transmission alongside high-rate V2N services in resource-constrained, dynamically changing traffic environments poses a significant challenge for resource allocation. To address this, we propose a novel reinforcement learning (RL) framework, termed Graph Attention Network (GAT)-Advantage Actor–Critic (GAT-A2C). In this framework, we construct a graph based on V2V links and their potential interference relationships. Each V2V link is represented as a node, and edges connect nodes that may interfere. The GAT captures key interference patterns among neighboring vehicles while accounting for real-time mobility and channel variations. The features generated by the GAT, combined with individual link characteristics, form the environment state, which is then processed by the RL agent to jointly optimize the resource blocks allocation and the transmission power for both V2V and V2N communications. Simulation results demonstrate that the proposed method substantially improves V2N rates and V2V communication success ratios under various vehicle densities. Furthermore, the approach exhibits strong scalability, making it a promising solution for future large-scale intelligent vehicular networks operating in dynamic traffic scenarios. Full article

This research focuses on developing neural network-based models for predicting time and cost overruns in construction projects during the construction phase, incorporating sustainability considerations. Previous studies have identified seven key performance areas that affect the final outcome: productivity, quality, time, cost, safety, team satisfaction, and client satisfaction. Although the interconnections among these performance areas are recognized, their exact relationships and impacts are not fully understood. Hence, the utilization of a neural networks proves to be highly beneficial in predicting the outcome of future construction projects, as it can learn from data and identify patterns, without requiring a complete understanding of these mutual influences. The neural network was trained and tested on the data collected on five completed construction projects, each analyzed at three distinct stages of execution. A total of 182 experiments were conducted to explore different neural network architectures. The most effective configurations for predicting time and cost overruns were identified and evaluated, demonstrating the potential of neural network-based approaches to support more sustainable and proactive project management. The time overrun prediction model demonstrated high accuracy, achieving a MAPE of 10.93%, RMSE of 0.128, and correlation of 0.979. While the cost overrun model showed a lower predictive accuracy, its MAPE (166.76%), RMSE (0.4179), and correlation (0.936) values indicate potential for further refinement. These findings highlight the applicability of neural network-based approaches in construction project management and their potential to support more proactive and informed decision-making. Full article

This study reviews the methods and materials used in industry and ship maintenance to remove rust, marine deposits and paint from ships. It also reviews how this waste is transferred and repurposed into useful materials. The notion of recycling in this field of application represents the reuse of the waste blend of the abrasive grit material along with the mineral residues, antifouling agents and coatings removed in meaningful applications. They are used in building construction materials, road construction blends, insulation surfaces, renewed composites and coatings. The main concern of the experts is the presence of heavy metals that limit the applications of the waste mixes. Therefore, a thorough characterization of the waste stream is paramount to ensure its safety and suitability for repurposing. Furthermore, the study investigates the potential for upcycling these waste materials into higher-value products, moving beyond simple reuse to create new economic opportunities. Ultimately, the goal is to convert a former waste stream into a valuable resource, aligning with circular economic principles. Full article

Innovative technologies have been helping to improve comfort and safety at work in high-risk sectors for years. The study analysed the impact, along with an assessment of potential implementations (opportunities and limitations) of innovative technological solutions for improving occupational safety in two selected sectors of the economy: mining and construction. The technologies evaluated included unmanned aerial vehicles and inspection robots, the Internet of Things and sensors, artificial intelligence, virtual and augmented reality, innovative individual and collective protective equipment, and exoskeletons. Due to the extensive nature of the obtained materials, the research description has been divided into two articles (Part I and Part II). This article presents the first three technologies. After the scientific literature from the Scopus database was analysed, some research gaps that need to be filled were identified. In addition to the obvious benefits of increased occupational safety for workers, innovative technological solutions also offer employers several economic advantages that affect the industry’s sustainability. Innovative technologies are playing an increasingly important role in improving safety in mining and construction. However, further integration and overcoming implementation barriers, such as the need for changes in education, are needed to realise their full potential. Full article

Nuclear energy has undergone a significant transformation over the past decades, driven by technological innovation, shifting safety priorities, and the urgent need to mitigate climate change. This study presents a comprehensive review of the historical evolution, current developments, and future prospects of nuclear energy as a strategic low-carbon resource. A structured literature review was conducted following Kitchenham’s methodology, covering peer-reviewed articles and institutional reports from 2000 to 2025. Key advances examined include the deployment of Small Modular Reactors, Generation IV technologies, and fusion systems, along with progress in safety protocols, waste management, and regulatory frameworks. Comparative environmental data confirm nuclear power’s low life-cycle CO₂ emissions and high energy density relative to other generation sources. However, major challenges remain, including high capital costs, long construction times, complex waste disposal, and issues of public acceptance. The analysis underscores that nuclear energy, while not a standalone solution, is a critical component of a diversified and sustainable energy mix. Its successful integration will depend on adaptive governance, international cooperation, and enhanced social engagement. Overall, the findings support the role of nuclear energy in achieving global decarbonization targets, provided that safety, equity, and environmental responsibility are upheld. Full article

Mine roof water inrush represents a prevalent hazard in mining operations, characterized by its concealed onset, abrupt occurrence, and high destructiveness. Since mine water inrush is controlled by multiple factors, rigorous risk assessment in hydrogeologically complex coal mines is critically important for operational safety. This study focuses on the roof water inrush hazard in coal seams of the Banji coal mine, China. The conventional water-conducting fracture zone height estimation formula was calibrated through comparative analysis of empirical models and analogous field measurements. Eight principal controlling factors were systematically selected, with subjective and objective weights assigned using AHP and EWM, respectively. Game theory was subsequently implemented to compute optimal combined weights. Based on this, the vulnerability index model and fuzzy comprehensive evaluation model were constructed to assess the roof water inrush risk in the coal seams. The risk in the study area was classified into five levels: safe zone, relatively safe zone, transition zone, relatively hazardous zone, and hazardous zone. A zoning map of water inrush risk was generated using Geographic Information System (GIS) technology. The results show that the safe zone is located in the western part of the study area, while the hazardous and relatively hazardous zones are situated in the eastern part. Among the two models, the fuzzy comprehensive evaluation model aligns more closely with actual engineering practices and demonstrates better predictive performance. It provides a reliable evaluation and prediction model for addressing roof water hazards in the Banji coal seam. Full article

This study aims to develop an enhanced YOLO algorithm to improve the ship detection performance of synthetic aperture radar (SAR) in complex marine environments. Current SAR ship detection methods face numerous challenges in complex sea conditions, including environmental interference, false detection, and multi-scale changes in detection targets. To address these issues, this study adopts a technical solution that combines multi-level feature fusion with a dynamic detection mechanism. First, a cross-stage partial dynamic channel transformer module (CSP_DTB) was designed, which combines the transformer architecture with a convolutional neural network to replace the last two C3k2 layers in the YOLOv11n main network, thereby enhancing the model’s feature extraction capabilities. Second, a general dynamic feature pyramid network (RepGFPN) was introduced to reconstruct the neck network architecture, enabling more efficient multi-scale feature fusion and information propagation. Additionally, a lightweight dynamic decoupled dual-alignment head (DYDDH) was constructed to enhance the collaborative performance of localization and classification tasks through task-specific feature decoupling. Experimental results show that the proposed DRGD-YOLO algorithm achieves significant performance improvements. On the HRSID dataset, the algorithm achieves an average precision (mAP50) of 93.1% at an IoU threshold of 0.50 and an mAP50–95 of 69.2% over the IoU threshold range of 0.50–0.95. Compared to the baseline YOLOv11n algorithm, the proposed method improves mAP50 and mAP50–95 by 3.3% and 4.6%, respectively. The proposed DRGD-YOLO algorithm not only significantly improves the accuracy and robustness of synthetic aperture radar (SAR) ship detection but also demonstrates broad application potential in fields such as maritime surveillance, fisheries management, and maritime safety monitoring, providing technical support for the development of intelligent marine monitoring technology. Full article