Search Results (47)

Rapid and accurate building assessment after an earthquake remains a persistent challenge for engineers in seismic areas. Manual inspections are often slow, hampered by road blockages, damaged utilities, and ongoing aftershock risks. This study presents the design, field deployment, and validation of a scalable IoT-based structural health monitoring (SHM) platform developed for real-time post-earthquake decision support. The system integrates multi-axis MEMS accelerometers and inclinometers, supported by on-site signal processing and a cloud-based analytics backend. A comprehensive damage assessment algorithm evaluates parameters such as frequency changes, inter-storey drift, roof displacement, torsional irregularities, and permanent tilt by combining multiple indicators rather than relying on a single measure. The system was deployed in a 22-storey reinforced concrete office building and continuously recorded several seismic events, including a Mw 6.2 earthquake. The results showed that drift values remained within code-defined limits and no permanent deformation occurred. Event-driven edge processing and optimized data management confirmed the system’s scalability for large building portfolios. The findings indicate that IoT-based SHM platforms can complement conventional inspections by providing rapid, data-driven screening to support resilient urban recovery. Full article

Distributed fiber-optic photoacoustic non-destructive testing (DFP-NDT) represents a paradigm shift from passive sensing to active probing, fundamentally transforming structural health monitoring through integrated fiber-based ultrasonic generation and detection capabilities. This review systematically examines DFP-NDT’s evolution by following the technology’s natural progression from fundamental principles to practical implementations. Unlike conventional approaches that require external excitation mechanisms, DFP-NDT leverages photoacoustic transducers as integrated active components where fiber-optical devices themselves generate and detect ultrasonic waves. Central to this technology are photoacoustic materials engineered to maximize conversion efficiency—from carbon nanotube-polymer composites achieving 2.74 × 10⁻² conversion efficiency to innovative MXene-based systems that combine high photothermal conversion with structural protection functionality. These materials operate within sophisticated microstructural frameworks—including tilted fiber Bragg gratings, collapsed photonic crystal fibers, and functionalized polymer coatings—that enable precise control over optical-to-thermal-to-acoustic energy conversion. Six primary distributed fiber-optic photoacoustic transducer array (DFOPTA) methodologies have been developed to transform single-point transducers into multiplexed systems, with low-frequency variants significantly extending penetration capability while maintaining high spatial resolution. Recent advances in imaging algorithms have particular emphasis on techniques specifically adapted for distributed photoacoustic data, including innovative computational frameworks that overcome traditional algorithmic limitations through sophisticated statistical modeling. Documented applications demonstrate DFP-NDT’s exceptional versatility across structural monitoring scenarios, achieving impressive performance metrics including 90 × 54 cm² coverage areas, sub-millimeter resolution, and robust operation under complex multimodal interference conditions. Despite these advances, key challenges remain in scaling multiplexing density, expanding operational robustness for extreme environments, and developing algorithms specifically optimized for simultaneous multi-source excitation. This review establishes a clear roadmap for future development where enhanced multiplexed architectures, domain-specific material innovations, and purpose-built computational frameworks will transition DFP-NDT from promising laboratory demonstrations to deployable industrial solutions for comprehensive structural integrity assessment. Full article

This paper presents the design, development, and field testing of an Operational Fire Management System (OFMS) aimed at enhancing situational awareness and improving the safety and efficiency of firefighting operations. The system integrates real-time intelligence and remote monitoring to provide emergency management personnel and first responders with accurate information on vehicle location, communication status, and water level monitoring. Developed in collaboration with the Australian Capital Territory Rural Fire Service (ACT RFS), the OFMS prototype encompasses three core subsystems: the Monitoring and Environmental Sensing Subsystem (MESS), the Communication and Vital Monitoring Subsystem (CVMS), and the Command-and-Control Interface Subsystem (CCIS). MESS introduces a tilt-compensated ultrasonic algorithm for accurate water level estimation in moving fire trucks, CVMS leverages an open-source smartwatch with LoRa communication for real-time physiological tracking, and CCIS offers a cloud-based interface for live visualisation and coordination. Together, these subsystems form a practical and scalable framework for supporting frontline operations, particularly in rural firefighting contexts where vehicles are required to operate off-road and deliver large volumes of water to isolated locations. By providing real-time visibility of resource availability and crew status, the system strengthens operational coordination and decision-making in environments where connectivity is often limited. This paper discusses the design and implementation of the prototype, highlights key performance results, and outlines opportunities for future development, including improved environmental resilience, expanded sensor integration, and multi-agency interoperability. The findings confirm that the OFMS represents a novel and field-ready approach to fireground management, empowering firefighting teams to respond more effectively to emergencies and better protect lives, property, and the environment. Full article

The structural safety monitoring of industrial heritage is of great significance for global urban renewal and the preservation of cultural heritage. However, traditional tilt monitoring methods suffer from limited accuracy, low efficiency, poor global perception, and a lack of intelligence, making them inadequate for meeting the tilt monitoring requirements of super-high-rise industrial heritage chimneys. To address these issues, this study proposes a tilt monitoring method for super-high-rise industrial heritage chimneys based on LiDAR point clouds. Firstly, LiDAR point cloud data were acquired using a ground-based LiDAR measurement system. This system captures high-density point clouds and precise spatial attitude data, synchronizes multi-source timestamps, and transmits data remotely in real time via 5G, where a data preprocessing program generates valid high-precision point cloud data. Secondly, multiple cross-section slicing segmentation strategies are designed, and an automated tilt monitoring algorithm framework with adaptive slicing and collaborative optimization is constructed. This algorithm framework can adaptively extract slice contours and fit the central axes. By integrating adaptive slicing, residual feedback adjustment, and dynamic weight updating mechanisms, the intelligent extraction of the unit direction vector of the central axis is enabled. Finally, the unit direction vector is operated with the x- and z-axes through vector calculations to obtain the tilt-azimuth, tilt-angle, verticality, and verticality deviation of the central axis, followed by an accuracy evaluation. On-site experimental validation was conducted on a super-high-rise industrial heritage chimney. The results show that, compared with the results from the traditional method, the relative errors of the tilt angle, verticality, and verticality deviation of the industrial heritage chimney obtained by the proposed method are only 9.45%, while the relative error of the corresponding tilt-azimuth is only 0.004%. The proposed method enables high-precision, non-contact, and globally perceptive tilt monitoring of super-high-rise industrial heritage chimneys, providing a feasible technical approach for structural safety assessment and preservation. Full article

This study proposes an innovative method that integrates multi-source remote sensing technologies and artificial intelligence to meet the urgent needs of deformation monitoring and ecohydrological environment analysis in Great Wall heritage protection. By integrating synthetic aperture radar (InSAR) technology, low-altitude oblique photogrammetry models, and the three-dimensional Gaussian splatting model, an integrated air–space–ground system for monitoring and understanding the Great Wall is constructed. Low-altitude tilt photogrammetry combined with the Gaussian splatting model, through drone images and intelligent generation algorithms (e.g., generative adversarial networks), quickly constructs high-precision 3D models, significantly improving texture details and reconstruction efficiency. Based on the 3D Gaussian splatting model of the AHLLM-3D network, the integration of point cloud data and the large language model achieves multimodal semantic understanding and spatial analysis of the Great Wall’s architectural structure. The results show that the multi-source data fusion method can effectively identify high-risk deformation zones (with annual subsidence reaching −25 mm) and optimize modeling accuracy through intelligent algorithms (reducing detail error by 30%), providing accurate deformation warnings and repair bases for Great Wall protection. Future studies will further combine the concept of ecological water wisdom to explore heritage protection strategies under multi-hazard coupling, promoting the digital transformation of cultural heritage preservation. Full article

Given the harsh working conditions of underwater pan-tilt systems, their energy consumption management is particularly crucial. This study proposes an underwater pan-tilt operation method with an automatic wake-up mechanism, which activates only upon target detection, replacing conventional timer-based triggering. Furthermore, departing from fixed-duration observation strategies, we introduce a Q-learning algorithm to optimize operational modes. The algorithm dynamically adjusts working modes based on surrounding biological activity frequency: employing a low-power mode (reduced energy consumption with lower monitoring intensity) during periods of sparse biological presence and switching to a high-performance mode (extended observation duration, higher energy consumption, and enhanced monitoring intensity) during frequent biological activity. Simulation results demonstrate that compared to fixed-duration observation schemes, the proposed optimization strategy achieves a 11.11% improvement in monitoring effectiveness while achieving 16.21% energy savings. Full article

In the domain of intelligent inspection, the precise recognition of pointer meter readings is of paramount importance for monitoring equipment conditions. To address the challenges of insufficient robustness and diminished detection accuracy encountered in practical applications of existing methods for recognizing pointer meter readings based on object detection, we propose a novel approach that integrates YOLOv11-OBB rotating object detection with adaptive template matching techniques. Firstly, the YOLOv11 object detection algorithm is employed, incorporating a rotational bounding box (OBB) detection mechanism; This effectively enhances the feature extraction capabilities related to pointer rotation direction and dial center, thereby boosting detection robustness. Subsequently, an enhanced angle resolution algorithm is leveraged to develop a mapping model that establishes a relationship between pointer the deflection angle and the instrument range, facilitating precise reading calculation. Experimental findings demonstrate that the proposed method achieves a mean Average Precision (mAP) of 99.1% in a self-compiled pointer instrument dataset. The average relative error of readings is 0.41568%, with a maximum relative error of less than 1.1468%. Furthermore, the method exhibits robustness and reliability when handling low-quality meter images characterized by blur, darkness, overexposure, and tilt. The proposed approach provides a highly adaptable and reliable solution for pointer meter reading recognition in the intelligent industrial field, with significant practical value. Full article

To address the inefficiencies and limited spatial resolution of traditional single-point monitoring techniques, this study proposes a multi-scale analysis method that integrates the Multi-Scale Model-to-Model Cloud Comparison (M3C2) algorithm with least-squares plane fitting. This approach employs the M3C2 algorithm for qualitative full-field deformation detection and utilizes least-squares plane fitting for quantitative feature extraction. When applied to the approach span of a cross-river bridge in Hubei Province, China, this method leverages dense point clouds (greater than 500 points per square meter) acquired using a Leica RTC360 scanner. Data preprocessing incorporates curvature-adaptive cascade denoising, achieving over 98% noise removal while retaining more than 95% of structural features, along with octree-based simplification. By extracting multi-level slice features from bridge decks and piers, this method enables the simultaneous analysis of global trends and local deformations. The results revealed significant deformation, with an average settlement of 8.2 mm in the left deck area. The bridge deck exhibited a deformation trend characterized by left and higher right in the vertical direction, while the bridge piers displayed noticeable tilting, particularly with the maximum offset of the rear pier columns reaching 182.2 mm, which exceeded the deformation of the front pier. The bridge deck’s micro-settlement error was ±1.2 mm, and the pier inclination error was ±2.8 mm, meeting the Chinese Highway Bridge Maintenance Code (JTG H11-2004) and the American Association of State Highway and Transportation Officials (AASHTO) standards, and the multi-scale algorithm achieved engineering-level accuracy. Utilizing point cloud densities >500 pt/m², the M3C2 algorithm achieved a spatial resolution of 0.5 mm, enabling sub-millimeter full-field analysis for complex scenarios. This method significantly enhances bridge safety monitoring precision, enhances the precision of intelligent systems monitoring, and supports the development of targeted systems as pile foundation reinforcement efforts and as improvements to foundations. Full article

Background: Scoliosis is a disorder characterized by an abnormal spinal curvature, which can lead to negative effects on patients, affecting their quality of life. Given its progressive nature, the classification of the scoliosis severity requires an accurate diagnosis and effective monitoring. The Cobb angle measurement method has been widely considered as the gold standard for a scoliosis assessment. Commonly, an expert assesses scoliosis severity manually by identifying the most tilted vertebrae of the spine. However, this method requires time, effort, and presents limitations in measurement accuracy, such as the intra- and inter-observer variability. Artificial intelligence provides more objective tools that are less sensitive to manual intervention aiming to transform the diagnosis of scoliosis. Objectives: The objective of this study was to address three key research questions regarding automated Cobb angle quantification: “Where is the spine in this radiograph?”, “What is its exact shape?”, and “Is the proposed method accurate?”. We propose the use of Mask R-CNN architecture for spine detection and segmentation in response to the first two questions, and a set of algorithms to tackle the third. Methods: The network’s detection and segmentation performance was evaluated through various metrics. An automated workflow for Cobb angle quantification and severity classification was developed. Finally, statistical methods provided the agreement between manual and automated measurements. Results: A high segmentation accuracy was achieved, highlighting the following: mIoU of 0.8012, and a mean precision of 0.9145. MAE was 2.96° ± 2.60° demonstrating a high agreement. Conclusions: The results obtained in this study demonstrate the potential of the proposed automated approach in clinical scenarios, which provides experts with a clear visualization of each stage in the scoliosis assessment by overlaying the results onto the X-ray image. Full article

Traditional displacement monitoring struggles to provide early warnings for sudden rock collapses. This study proposes a Micro-Electro-Mechanical System (MEMS) sensor-based monitoring method using dual dynamic indicators. By analyzing sensor mechanisms through vibration dynamics theory and establishing theoretical models via moment equilibrium equations, we derived a quantitative correlation between natural frequency (NF) and safety factor, identifying a 4:3 scaling coefficient specific to toppling-type unstable rocks. An innovative stability criterion algorithm integrating NF and the root mean square velocity amplitude ratio (RMS-VAR) was developed, revealing that RMS-VAR detects stability degradation three times faster than tilt measurements. Laboratory tests confirmed MEMS sensors’ reliability in monitoring NF, amplitude ratio, and tilt angles, demonstrating that sensor deployment strategies and rock geometry jointly determine model accuracy. This quantitative approach offers a novel solution for monitoring sudden geological hazards, combining timeliness with cost-effectiveness. Full article

Deformation monitoring of Gas-Insulated Transmission Lines (GILs) is critical for the early detection of structural issues and for ensuring safe power transmission. In this study, we introduce a rapid monocular measurement method that leverages deep learning for real-time monitoring. A YOLOv10 model is developed for automatically identifying regions of interest (ROIs) that may exhibit deformations. Within these ROIs, grayscale data is used to dynamically set thresholds for FAST corner detection, while the Shi–Tomasi algorithm filters redundant corners to extract unique feature points for precise tracking. Subsequent subpixel refinement further enhances measurement accuracy. To correct image tilt, ArUco markers are employed for geometric correction and to compute a scaling factor based on their known edge lengths, thereby reducing errors caused by non-perpendicular camera angles. Simulated experiments validate our approach, demonstrating that combining refined ArUco marker coordinates with manually annotated features significantly improves detection accuracy. Our method achieves a mean absolute error of no more than 1.337 mm and a processing speed of approximately 0.024 s per frame, meeting the precision and efficiency requirements for GIL deformation monitoring. This integrated approach offers a robust solution for long-term, real-time monitoring of GIL deformations, with promising potential for practical applications in power transmission systems. Full article

Holographic detection technology has found extensive applications in biomedical imaging, surface profilometry, vibration monitoring, and defect inspection due to its unique phase detection capability. However, the accuracy of quantitative holographic phase imaging is significantly affected by the interference from direct current and twin image terms. Traditional methods, such as multi-exposure phase shifting and off-axis holography, have been employed to mitigate these interferences. While off-axis holography separates spectral components by introducing a tilted reference beam, it inevitably induces phase disturbances that compromise measurement accuracy. This study provides a computational explanation for the incomplete phase compensation issue in existing algorithms and establishes precision criteria for phase compensation based on theoretical formulations. We propose two novel phase compensation methods—the non-iterative compensation approach and the multi-iteration compensation technique. The principles and applicable conditions of these methods are thoroughly elucidated, and their superiority is demonstrated through comparative experiments. The results indicate that the proposed methods effectively compensate for phase disturbances induced by the tilted reference beam, offering enhanced precision and reliability in quantitative holographic phase measurements. Full article

With the launch of the ICESat-2 satellite, global-scale forest parameter monitoring has entered a new phase. However, the background noise in ICESat-2 lidar data significantly impairs the accuracy of signal photon extraction. This study introduces a direction-adaptive DBSCAN method for denoising ICESat-2 photon point clouds, integrating elevation histogram-based coarse denoising with adaptive clustering for fine denoising. The method is applied to data from the Gongbella River Nature Reserve. An innovative aspect of this approach is the introduction of elliptical tilt angle adaptation, which dynamically adjusts the elliptical orientation of the photon point cloud to determine the optimal tilt angle, thus optimizing the denoising effect and reducing computational and memory demands. The direction-adaptive DBSCAN algorithm improves denoising accuracy by dynamically adjusting the neighborhood radius based on the elliptic tilt angle and the distribution of the point cloud. Additionally, the density threshold selection is optimized using the Otsu method, enhancing the accuracy of distinguishing noise photons from signal photons. The method was validated using data from the Gongbella River Nature Reserve, showing significant improvements in denoising accuracy. Compared to existing methods, recall (R) increased by 6.91%, precision (P) improved by 8.82%, and both the F1-score and accuracy rose by 9.52%. The photon point cloud denoising algorithm demonstrated substantial accuracy improvements across multiple data strips, making it particularly effective for processing complex data from ICESat-2, with broad application potential. Both quantitative and qualitative analyses confirm that the algorithm outperforms traditional methods in signal-to-noise ratio and denoising performance, providing reliable technical support for extracting photon point cloud elevation data from forest surfaces and canopies. Full article

With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images. To address these challenges, we propose an end-to-end PMRR method based on a decoupled circle head detection algorithm (YOLOX-DC) and a Unet-like pure Transformer segmentation network (PM-SwinUnet). First, according to the characteristics of the pointer dial, the YOLOX-DC detection algorithm is designed based on the exceeding you only look once detector (YOLOX). The decoupled circle head of YOLOX-DC detects the pointer meter dial more accurately than the commonly used rectangular detection head. Second, the window multi-head attention of the PM-SwinUnet network enhances the feature extraction ability of pointer meter images and solves problems of missed scale detection and incomplete pointer segmentation. Additionally, the scale and pointer fitting module is introduced into the PM-SwinUnet to locate the accurate position of the scale and pointer. Finally, through the angle relationship between the pointer and the first two main scale lines, the pointer meter reading is accurately calculated by the improved angle method. Experimental results demonstrate the effectiveness and superiority of the proposed end-to-end method across three-pointer meter datasets. Furthermore, it provides a rapid and robust approach to the digital transformation of manufacturing systems. Full article

Hyperboloid cooling towers are counted among the largest cast-in-place industrial structures. They are an essential element of cooling systems used in many power plants in service today. Their main structural component, a reinforced-concrete shell in the form of a one-sheet hyperboloid with bidirectional curvature continuity, makes them stand out against other towers and poses very high construction and service requirements. The safe service and adequate durability of the hyperboloid structure are guaranteed by the proper geometric parameters of the reinforced-concrete shell and monitoring of their condition over time. This article presents an original concept for employing terrestrial laser scanning to conduct an end-to-end assessment of the geometric condition of a hyperboloid cooling tower as required by industry standards. The novelty of the proposed solution lies in the use of measurements of the interior of the structure to determine the actual thickness of the hyperboloid shell, which is generally disregarded in geometric measurements of such objects. The proposal involves several strategies and procedures for a reliable verification of the structure’s verticality, the detection of signs of ovalisation of the shell, the estimation of the parameters of the structure’s theoretical model, and the analysis of the distribution of the thickness and geometric imperfections of the reinforced-concrete shell. The idea behind the method for determining the actual thickness of the shell (including its variation due to repairs and reinforcement operations), which is generally disregarded when measuring the geometry of such structures, is to estimate the distance between point clouds of the internal and external surfaces of the structure using the M3C2 algorithm principle. As a particularly dangerous geometric anomaly of hyperboloid cooling towers, shell ovalisation is detected with an innovative analysis of the bimodality of the frequency distribution of radial deviations in horizontal cross-sections. The concept of a complete assessment of the geometry of a hyperboloid cooling tower was devised and validated using three measurement series of a structure that has been continuously in service for fifty years. The results are consistent with data found in design and service documents. We identified a permanent tilt of the structure’s axis to the northeast and geometric imperfections of the hyperboloid shell from −0.125 m to +0.136 m. The results also demonstrated no advancing deformation of the hyperboloid shell over a two-year research period, which is vital for its further use. Full article