Search Results (6,650)

To preserve flood control infrastructure, it is essential to quickly detect and accurately identify concealed leakage hazards within embankment projects. In this paper, we propose a novel embankment monitoring method based on the time-lapse transient electromagnetic method and complemented by a theoretical framework for analyzing time-lapse data through the lens of resistivity change rates. A time-lapse model that scrutinizes dynamic response patterns associated with leakage anomalies is constructed, while the efficacy of this methodology is verified through rigorous field experiments. Our research findings reveal a well-defined negative correlation between the resistivity variation rate and the development stage of anomalies. Our proposed method demonstrates enhanced sensitivity in the detection of dynamic evolutionary patterns in latent seepage defects, particularly in low-resistivity environments. Moreover, it successfully delineates both the spatial expansions and electrical property alterations of anomalies, providing a novel technical approach for latent seepage defect monitoring and risk management in embankments. Full article

Superior electricity-optimized business ecosystems (EOBEs) have evolved into pivotal determinants in catalyzing industrial–commercial prosperity. The access to electricity index (AEI) constitutes a valid instrument for assessing the EOBE. To realize the accurate evaluation of EOBE and the root cause tracing of its changes, this paper constructs a new analytical model for evaluating and monitoring changes in EOBE. First, this paper constructs a new evaluation model of EOBE based on the Business Ready (B-READY) evaluation system, considering three factors: the power regulatory quality, the public service level, and the enterprises’ gain power efficiency. Then, the model uses the raw data collected to calculate a score for AEI to enable an accurate assessment of EOBE. Next, this paper uses a priori assessment to extract the coupling features of indicators and combines the time series features and policy features to construct the feature matrix. Finally, the characteristic contribution was analyzed using support vector regression (SVR) and Shapley’s additive interpretation (SHAP) value. The experiment shows that the factors affecting the change in AEI are time series features, policy features, and coupling features in decreasing order of importance. This study provides reference cases and improvement ideas for the assessment and optimization of EOBE. Full article

Wheat canopy height is an important parameter for monitoring growth status. Accurately predicting the wheat canopy height can improve field management efficiency and optimize fertilization and irrigation. Changes in the growth characteristics of wheat at different growth stages affect the canopy structure, leading to changes in the quality of the LiDAR point cloud (e.g., lower density, more noise points). Multispectral data can capture these changes in the crop canopy and provide more information about the growth status of wheat. Therefore, a method is proposed that fuses LiDAR point cloud features and multispectral feature parameters to estimate the canopy height of winter wheat. Low-altitude unmanned aerial systems (UASs) equipped with LiDAR and multispectral cameras were used to collect point cloud and multispectral data from experimental winter wheat fields during three key growth stages: green-up (GUS), jointing (JS), and booting (BS). Analysis of variance, variance inflation factor, and Pearson correlation analysis were employed to extract point cloud features and multispectral feature parameters significantly correlated with the canopy height. Four wheat canopy height estimation models were constructed based on the Optuna-optimized RF (OP-RF), Elastic Net regression, Extreme Gradient Boosting, and Support Vector Regression models. The model training results showed that the OP-RF model provided the best performance across all three growth stages of wheat. The coefficient of determination values were 0.921, 0.936, and 0.842 at the GUS, JS, and BS, respectively. The root mean square error values were 0.009 m, 0.016 m, and 0.015 m. The mean absolute error values were 0.006 m, 0.011 m, and 0.011 m, respectively. At the same time, it was obtained that the estimation results of fusing point cloud features and multispectral feature parameters were better than the estimation results of a single type of feature parameters. The results meet the requirements for canopy height prediction. These results demonstrate that the fusion of point cloud features and multispectral parameters can improve the accuracy of crop canopy height monitoring. The method provides a valuable method for the remote sensing monitoring of phenotypic information of low and densely planted crops and also provides important data support for crop growth assessment and field management. Full article

Rational and effective utilization of rainfall is crucial to vegetation restoration and ecological reconstruction for engineering slopes. However, plant and vegetated concrete considerably affect soil water distribution and rainfall replenishment, which is rarely accounted for in current studies. To this end, the effects of plant and vegetated concrete on spatiotemporal distribution and soil water recharge were explored. First, four field model slopes were constructed to monitor soil water content. The spatiotemporal variations and distribution characteristics of soil water under different restoration modes were analyzed. The indicators including amount, efficiency, and threshold of soil water recharge in ecological slopes were assessed. At last, the effects of plant and vegetated concrete on the spatiotemporal distribution and recharge characteristics of soil water were discussed. Results showed that ecological restoration alters spatiotemporal distribution characteristics and reduces soil water content of engineering slopes. During rainfall process, ecological restoration extends the lag time but increases amount and efficiency of rainfall replenishment. Comparably, ecological shrub slope gains the highest lag time and rainfall threshold. Cynodon dactylon is superior to Magnolia multiflora in raising rainfall replenishment capacity. Additionally, vegetated concrete enhances rainfall replenishment efficiency by altering soil properties and interacting with plants. This study deepened the understanding of hydrological effects of ecological restoration on slopes and provided a theoretical basis for ensuring sustainable slope management. Full article

The nexus between sustainability and safety in construction is crucial for improving a resilient and responsible built environment. By adhering to sustainable principles, construction practices can not only mitigate the environmental impact but also prioritize the health and safety of workers and communities. To prevent accidents and enhance safety in construction, unmanned aerial vehicles (UAVs) are utilized for aerial inspection, site monitoring, surveying, emergency response, and training purposes. However, no systematic review has yet identified UAV deployment’s adoption, challenges, and prospects. UAVs have emerged as promising technologies for improving safety through applications such as aerial inspections, site monitoring, surveying, emergency response, and training. However, a comprehensive review of UAV adoption, challenges, and prospects in the construction industry is still lacking. To address this gap, this study conducts a systematic literature review and bibliometric analysis to examine the current state of UAV implementation in construction safety management. The analysis reveals the interconnectedness between construction, engineering disciplines, and safety management, providing a holistic overview of influential contributors and prevalent themes. Content analysis further uncovers significant barriers hindering widespread UAV implementation, emphasizing technical, regulatory, and safety concerns. This study highlights strategies for overcoming these challenges and optimizing UAV deployment to enhance safety in construction, aligning with broader principles of social sustainability. Full article

The electric drive is strategically placed in the power industry. It is exposed to wear and tear, defects, and constructional damage, as is any technical device. An information–analytical system is presented in this work. It performs the tasks of monitoring, diagnostics, general assessment of technical condition, and continuous assessment of energy and mechanical efficiency of the electric drive based on the analysis of immediate values of currents and voltages. The system modules are finished products with practical application, which are supported by experimental validation. This article contains a detailed description of the methods implemented in the system development, as well as a description of the laboratory bench and equipment used in our experiments. The information–analytical system is shown and proved on the basis of a fault reconstruction example with electric drive misalignment. According to the obtained results, recommendations for preventive control and proposals for development in this direction are formulated. Full article

Autonomous navigation and target search for unmanned aerial vehicles (UAVs) have extensive application potential in search and rescue, surveillance, and environmental monitoring. Reinforcement learning (RL) has demonstrated excellent performance in real-time UAV navigation through dynamic optimization of decision-making strategies, but its application in large-scale environments for target search and obstacle avoidance is still limited by slow convergence and low computational efficiency. To address this issue, a hybrid framework combining RL and artificial potential field (APF) is proposed to improve the target search algorithm. Firstly, a task scenario and training environment for UAV target search are constructed. Secondly, RL is integrated with APF to form a framework that combines global and local strategies. Thirdly, the hybrid framework is compared with standalone RL algorithms through training and analysis of their performance differences. The experimental results demonstrate that the proposed method significantly outperforms standalone RL algorithms in terms of target search efficiency and obstacle avoidance performance. Specifically, the SAC-APF hybrid framework achieves a 161% improvement in success rate compared to the baseline SAC model, increasing from 0.282 to 0.736 in obstacle scenarios. Full article

Flexible wearable electronics face critical challenges in achieving reliable physiological monitoring, particularly due to the trade-off between sensitivity and durability in flexible electrodes, compounded by mechanical modulus mismatch with biological tissues. To address these limitations, we develop an anti-freezing ionic hydrogel through a chitosan/acrylamide/LiCl system engineered via the solution post-treatment strategy. The optimized hydrogel exhibits exceptional ionic conductivity (24.1 mS/cm at 25 °C) and excellent cryogenic tolerance. Leveraging these attributes, we construct a gel-based triboelectric nanogenerator (G-TENG) that demonstrates ultrahigh sensitivity (1.56 V/kPa) under low pressure. The device enables the precise capture of subtle vibrations at a frequency of 1088 Hz with a signal-to-noise ratio of 16.27 dB and demonstrates operational stability (>16,000 cycles), successfully differentiating complex physiological activities including swallowing, coughing, and phonation. Through machine learning-assisted analysis, the system achieves 96.56% recognition accuracy for five words and demonstrates good signal recognition ability in different ambient sound scenarios. This work provides a paradigm for designing environmentally adaptive wearable sensors through interfacial modulus engineering and ion transport optimization. Full article

When developing deep subsurface infrastructure in areas with intense geothermal activity, the significant temperature gradient inevitably leads to low-temperature contraction and high-temperature expansion of the rock body, resulting in changes in the rock’s mechanical properties. These thermodynamic effects can easily lead to the destabilization and subsequent collapse of the rock. There exists a pressing necessity to methodically evaluate the surrounding rock stability encountered in deep underground engineering under the action of thermal-solid coupling. This study constructed a multi-physical field coupling nonlinear calculation model based on a high-precision three-dimensional finite difference method, systematically analyzed the interdependent effects between the original rock temperature and excavation-induced disturbance, and then analyzed the dynamic changes in temperature, stress, and displacement fields along with plastic zone of surrounding rock of the deep underground engineering under thermal-solid coupling. The results indicate that the closer to the excavation contour surface, the lower the surrounding rock temperature, while the temperature gradient increased correspondingly. The farther away from the excavation contour face, the closer the temperature was to the original rock temperature. As the original rock temperature climbed from 30 °C to 90 °C, the increment of vault displacement was 2.45 times that of arch bottom displacement, and the influence of temperature change on vault deformation was more significant. The horizontal displacement magnitudes at the different original temperatures followed the following order: sidewall > spandrel > skewback, and at an original rock temperature of 90 °C, the sidewall horizontal displacement reached 15.31 cm. With the elevation of the original rock temperature, the distribution range and concentration degree of the maximum and minimum principal stresses increased obviously, and both were compression-dominated. The types of plastic zones in the surrounding rock were mainly characterized by shear stress-induced yielding and tensile stress-induced damage failure. When the original rock temperature increased to 90 °C, the rock mass extending up to 1.5 m from the excavation contour surface formed a large area of damage zone. The closer the working face was to the monitoring section, the faster the temperature dropped, and the displacement changed in the monitoring section. The findings offer a theoretical basis for engineering practice, and it is of great significance to ensure the safety of the project. Full article

In the new media era, the green technology alliance with multi-participation has emerged as a powerful contributor to achieving the strategic goal of a green economy. Therefore, this paper constructs a stochastic evolutionary game model of green technology innovation led by the government under an uncertain environment and jointly promoted by enterprises, universities, and research institutes. Then, this study firstly explores the influence of different factors on evolutionary equilibrium and secondly discusses the role of main factors on the behavior strategies of each game subject. Furthermore, numerical simulation analysis using Matlab R2019a 9.6 will be used to prove the model’s validity. The research has shown (1) that media monitoring positively impacts the stability of the alliance and that product greenness can further accelerate alliance evolution when media monitoring is in place. When this factor is small, it will lead to the transformation of Industry-University-Research’s (IUR) optimal strategy into non-cooperation in the early stage. (2) The green degree of products positively affects the decision-making choice of the IUR, but it is not the case for the government. And the role of media supervision will further coordinate its influence and accelerate the evolution of alliances. (3) The enhancement of media monitoring capacity can encourage game subjects to evolve in a more beneficial way. In addition, the implementation of media supervision will help reduce the cost of government supervision and provide reputation benefits. The research fully accounts for the complexity and variability of the environment, and the results provide theoretical support and practical advice for the high-quality development of the green technology innovation alliance. Full article

Digital twin is a virtual replica of a physical system that updates in real time using sensor data to enable simulations and predictions. For bridges constructed using rotation construction methods, the rotation phase demands continuous monitoring of structural behavior and coordination with surrounding traffic infrastructure. Therefore, a digital twin system for monitoring rotation construction is vital to ensure safety and schedule compliance. This paper explores the application of model-based systems engineering (MBSE), a modern approach that replaces text-based documentation with visual system models, to design a digital twin system for monitoring the rotation construction of a 90 m + 90 m single-tower cable-stayed bridge. A V-model architecture for the digital twin system, based on requirements analysis, functional analysis, logical design, and physical design analysis (RFLP), is proposed. Based on SysML language, the system’s requirements, functions, behaviors, and other aspects are modeled and analyzed using the MBSE approach, converting all textual specifications into the unified visual models. Compared to the traditional document-driven method, MBSE improves design efficiency by reducing ambiguities in system specifications and enabling early detection of design flaws through simulations. The digital twin system allows engineers to predict potential risks during bridge rotation and optimize construction plans before implementation. These advancements demonstrate how MBSE supports proactive problem-solving (forward design) and provides a robust foundation for future model validation and engineering applications. Full article

To address the issue of control inaccuracy caused by the zero-offset fault in current sensors within the multiphase brushless DC motor (BLDCM) drive system, this paper proposes a fault diagnosis and fault-tolerant control method based on current signals. Different from traditional solutions that rely on hardware redundancy or precise modeling, this method constructs a dual-channel fault diagnosis framework by integrating the steady-state amplitude offset of the phase current after the fault and the abnormal characteristics of dynamic sector switching. Firstly, sliding time window monitoring is used to identify steady-state amplitude anomalies and locate faulty sectors. Subsequently, an algorithm for detecting the difference in current changes during sector switching is designed, and a logic interlocking verification mechanism is combined to eliminate false triggering and accurately locate single or multiple fault phases. Furthermore, based on the diagnostic information, a repeated iterative online correction method is adopted to restore the accuracy of the current measurement. This method only relies on phase current signals and rotor position information, does not require additional hardware support or accurate system models, and is not affected by the nonlinear characteristics of the motor. Finally, the experimental verification was carried out on a nine-phase BLDCM drive system. Experimental results indicate that the torque fluctuation of the system can be controlled within 5% through the fault-tolerant control strategy. Full article

Unregulated underground group mining in China has led to problems such as unclear locations and complex shapes of mine goafs in mineral engineering, posing serious safety hazards for subsequent mining operations. This paper takes mineral engineering with complex mine goafs as the research object, integrates multi-survey data from surface deformation remote sensing monitoring and 3D laser scanning measurement to survey the area where the surface deformation rate reaches 14cm/ year, accurately identifies the location of risky mine goafs, and constructs detailed representations of the real shapes of the complex mine goafs inside the mineral engineering. The FLAC^3D 6.0 software is used to establish a 3D numerical simulation model of the mine goafs, fully considering the mining process, and conducting characteristic analysis of the stress distribution, failure range and surface deformation response of the mine goafs, revealing the impact of void deformation on the stability of the mine. The numerical simulation results are combined with on-site investigations to verify whether geological disasters have been caused by mine goafs. The research methods and results can provide effective technical means for the detailed survey and stability assessment of mineral engineering with complex mine goafs, which can help to reduce the risk of geological disasters in mines and improve the safety of mineral engineering. Full article

Transition zones are located at points on a track where there has been a change in the main composition of the railway infrastructure; as such, there are many sections that undergo a sudden change in the stiffness of the structures built. When trains are running, a longitudinal shockwave is created by the wheels, hitting these building objects with a greater stiffness and deforming the surroundings of these zones. The greatest amount of attention should be paid to the transition points from the fixed track to the classic track with a track bed, including objects of the railway substructure, such as bridges and portals of tunnels. As part of the research on the main corridor lines, long-term inspection and monitoring studies were carried out using a trolley with a continuous measurement system; height changes in the deflections of rails are evidence of their behaviour. The measurements took place on a fixed track and a track with ballast. The changes in the height jumps between the fixed railway track and the track with a gravel bed are significant. These height deflections allow designers to develop new, more durable construction designs. Full article

Panoramic images in indoor construction sites are gaining attention as valuable tools for process monitoring and quality assessment. However, despite the environmental complexity and the demand for high segmentation performance in indoor construction environments, the scarcity of specialized segmentation models and datasets has created a gap between technological advancements and practical application, thus hindering the effective utilization of panoramic images. To address these challenges, this study proposes a novel approach leveraging the Segment Anything Model (SAM), a perspective image segmentation foundation model, to enhance the performance of existing segmentation models. The proposed method iteratively executes SAM with adjusted input parameters to extract objects of varying sizes and subsequently applies filtering algorithms to retain valid objects. Then, label assignment and merging processes are performed based on the predictions from the target model to improve segmentation accuracy. The experimental study was conducted using Panoplane360, a model specifically designed for plane segmentation, as the target model. A quantitative evaluation was conducted to measure the exactness of label assignment, and two qualitative evaluations were performed to assess whether the assigned labels accurately represent the actual planar information. The evaluation results confirmed that the proposed method significantly improves segmentation performance compared to conventional approaches. The findings of this study highlight the potential of SAM-based methods to enhance segmentation accuracy in dynamic indoor construction environments. Furthermore, the proposed approach provides practical advantages, as it improves segmentation performance without requiring the construction of additional datasets. Future research will focus on resolving computational efficiency issues resulting from iterative SAM execution and will extend the applicability of the proposed approach to diverse segmentation tasks and models. Full article