Monitoring Method Research Articles

Classifying Food Items During an Eating Occasion: A Machine Learning Approach with Slope Dynamics for Windowed Kinetic Data

Background: Wearable devices equipped with a range of sensors have emerged as promising tools for monitoring and improving individuals’ health and lifestyle. Objectives: Contribute to the investigation and development of effective and reliable methods for dietary monitoring based on raw kinetic data generated by wearable devices. Methods: This study uses resources from the NOTION study. A total of 20 healthy subjects (9 women and 11 men, aged 20–31 years) were equipped with two commercial smartwatches during four eating occasions under semi-naturalistic conditions. All meals were video-recorded, and acceleration data were extracted and analyzed. Food recognition on these features was performed using random forest (RF) models with 5-fold cross-validation. The performance of the classifiers was expressed in out-of-bag sensitivity and specificity. Results: Acceleration along the x-axis and power show the highest and lowest rates of median variable importance, respectively. Increasing the window size from 1 to 5 s leads to a gain in performance for almost all food items. The RF classifier reaches the highest performance in identifying meatballs (89.4% sensitivity and 81.6% specificity) and the lowest in identifying sandwiches (74.6% sensitivity and 72.5% specificity). Conclusions: Monitoring food items using simple wristband-mounted wearable devices is feasible and accurate for some foods while unsatisfactory for others. Machine learning tools are necessary to deal with the complexity of signals gathered by the devices, and research is ongoing to improve accuracy further and work on large-scale and real-time implementation and testing.

A tool wear monitoring method driven by workpiece surface quality inversion and EfficientNet

Abstract Tool wear status monitoring plays an important role in the safety of machine and the efficiency of production. Due to the limitation of sensor installation and complex operation conditions, traditional machine vision methods are difficult to directly act on the tool itself to obtain corresponding wear status information. Meanwhile, the surface quality of the machined workpiece can provide indirect insights into the wear status of the tool to a significant degree. Therefore, an indirect tool wear status monitoring method is proposed in this paper by workpiece surface quality inversion and EfficientNet. Firstly, the surface texture data of workpiece are obtained in the form of image by an industrial camera, and the representative features that indirectly reflect tool wear status are further highlighted by using the weighted average image grayscale and the Laplacian operator. Secondly, to monitor tool wear effectively, we've optimized three key settings, width, model depth, and input image resolution, of the EfficientNet backbone network. This optimization boosts its ability to understand and generalize, ensuring it can handle various surface textures and extract crucial information accurately. This, in turn, enhances the accuracy of our tool wear monitoring system. Finally, the improved EfficientNet network is further utilized as a pre-trained network to achieve tool wear state monitoring. Utilizing the powerful representation ability of the pre-trained model and the surface texture features of workpiece, the model can accurately identify and judge the wear degree of the tool. The tool experiment results show that the proposed tool wear monitoring method has achieved remarkable results with recognition accuracy of 97.77%. While other popular methods such as VGG19, ResNet50, and ResNet152, whose training accuracies are 95.66%, 96.37%, and 91.27%, respectively. The comparative results highlight the excellent performance of the proposed method in recognition rate, training time and generalization performance.

Stability Analysis of Surrounding Rock in Mining Tunnels Based on Microseismic Monitoring and Numerical Simulation

In response to the safety hazards and environmental impacts caused by the decrease in the stability of the surrounding rock of the roadway and the frequent occurrence of microseismic activities during coal mining, the 4331 fully mechanized mining face of Nanpingdong Coal Mine was selected as a case study. Microseismic monitoring technology was used to analyze the spatial distribution of microseismic events in the surrounding rock during mining, and by establishing a FLAC3D numerical model, the displacement of surrounding rock and the evolution law of plastic zone during mining process are studied. The results confirmed that elastic strain energy in the rock is the primary source of microseismic energy. Using FISH language, a distribution cloud map of elastic strain energy was generated and compared with the microseismic event distribution and energy results. The findings indicate that as mining advances, the frequency and energy of microseismic events increase, particularly near faults, with roadway roof rupture exacerbating the events. The distribution of microseismic events correlates strongly with the depth of mining face advancement, highlighting the significant impact of mining activities on surrounding rock stability. The numerical simulation results closely align with on-site microseismic monitoring data, validating the simulation’s accuracy. This study proposes a method for dynamic monitoring and control of roadway surrounding rock stability through real-time microseismic monitoring and numerical simulation, aiming to mitigate surface environmental damage from underground mining.

Image-Based Monitoring of Thermal Ablation

Thermal therapy is a commonly used local treatment technique in clinical practice. Monitoring the treatment process is essential for ensuring its success. In this review, we analyze recent image-based methods for thermal therapy monitoring, focusing particularly on their feasibility for synchronous or immediate postoperative monitoring. This includes thermography and other techniques that track the physical changes in tissue during thermal ablation. Potential directions and challenges for further clinical applications are also summarized.

Study on corrosion monitoring and assessment method of reinforced concrete based on multi-sensor fusion

Purpose The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions. Design/methodology/approach A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data. Findings The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models. Practical implications Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety. Originality/value The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.

Environmental implications of residential to commercial land use succession on formal housing stock: A conceptual analysis

This study investigates the environmental implications of land use succession on formal housing stock. It is pursues to provide information that will assist in reducing or total elimination of the negative environmental impacts of land use succession on formal housing stock with the aim of enhancing housing and property investment decisions. The study methodology relied primarily on past issues relating to the environmental implications of residential to commercial land use succession on formal housing stock. The study looked at the concepts and theories related to the study. The study also examined Nigerian organizations and legal frameworks in charge of environmental activities. The study observed that the environmental implications resulting from residential succession by commercial land use among others includes; population displacement, traffic congestion, air pollution, noise pollution, water erosion, waste management issues, and climatic changes. The study suggests that the city’s relevant government entities should regulate, monitor, and control land use succession. Commercial and residential zones should be clearly defined in land use policies. There should be a sound legal framework for urban cities and more effective and efficient land use monitoring and management methods. In addition, individual land owners and investors/developers should be informed about the negative environmental impacts of land use succession on formal housing stock, thus preserving formal housing by reducing or eliminating population displacement and other negative environmental impacts.

System Safety Monitoring of Learned Components Using Temporal Metric Forecasting

In learning-enabled autonomous systems, safety monitoring of learned components is crucial to ensure their outputs do not lead to system safety violations, given the operational context of the system. However, developing a safety monitor for practical deployment in real-world applications is challenging. This is due to limited access to internal workings and training data of the learned component. Furthermore, safety monitors should predict safety violations with low latency, while consuming a reasonable computation resource amount. To address the challenges, we propose a safety monitoring method based on probabilistic time series forecasting. Given the learned component outputs and an operational context, we empirically investigate different Deep Learning (DL)-based probabilistic forecasting to predict the objective measure capturing the satisfaction or violation of a safety requirement ( safety metric ). We empirically evaluate safety metric and violation prediction accuracy, and inference latency and resource usage of four state-of-the-art models, with varying horizons, using autonomous aviation and autonomous driving case studies. Our results suggest that probabilistic forecasting of safety metrics, given learned component outputs and scenarios, is effective for safety monitoring. Furthermore, for both case studies, the Temporal Fusion Transformer (TFT) was the most accurate model for predicting imminent safety violations, with acceptable latency and resource consumption.

Quantifying and Mapping the Impact of Construction Land Expansion on Cultivated Land Fragmentation—A Case Study of Fuqing City, China

To ensure the sustainable utilization of cultivated land resources, it is essential to quantify and map the characteristics of construction land and cultivated land and analyze the mechanisms by which construction land expansion affects cultivated land. However, few studies have been conducted focusing on this issue. This study integrated morphological spatial pattern analysis, spillover effect analysis, landscape pattern analysis, and a land use transition monitoring method to investigate the characteristics of construction land expansion and cultivated land fragmentation. Fuqing City of China was selected as the case study area for demonstration. The results demonstrated that the expansion of construction land resulted in fragmented patterns within the cultivated land landscape: (1) The large core area of cultivated land was subdivided into smaller core areas during 2000–2020, while the construction land exhibited a tendency towards aggregation and a spillover effect; (2) The expansion rate of the construction land in the study area accelerated, while the extent of the cultivated land decreased; (3) Cultivated land fragmentation intensified as landscape aggregation weakened, leading to an expansion in the agglomeration of construction land. The highlights of this study are: (1) examining the characteristics of construction land expansion and cultivated land fragmentation from morphological and geospatial perspectives; (2) categorizing the core areas of cultivated land based on their size to facilitate the analysis of factors contributing to cultivated land fragmentation. The findings in this study can be used to develop models to predict future patterns of cultivated and construction land to provide suggestions for landscape planning.

Real-time detection of powder bed defects in laser powder bed fusion using deep learning on 3D point clouds

ABSTRACT Powder bed defects are critical factors affecting the print quality and stability in Laser Powder Bed Fusion (LPBF). However, traditional 2D image-based powder bed defect monitoring methods are limited by sensitivity to lighting conditions and insufficient data capture. This study proposes a real-time defect monitoring system based on 3D point cloud data and deep learning approach. The system uses binocular vision to capture point cloud data in real time, enabling high-precision defect segmentation with advanced deep learning models. However, direct deep learning on point clouds can result in the loss of small defect features during downsampling. To address this, an indirect point cloud deep learning method based on 2D projection is introduced, which improves segmentation accuracy for small defects while reducing inference time. By deploying the trained model, this study establishes a closed-loop control system for powder bed defect detection and conducts real-world printing tests, demonstrating effective defect remediation capabilities. Although larger-scale industrial testing is still required, this research illustrates the significant potential of 3D point cloud-based deep learning in enhancing defect detection and quality control in additive manufacturing.

Evaluating Methods for Aflatoxin B1 Monitoring in Selected Food Crops Within Decentralized Agricultural Systems

Aflatoxin B1 (AFB1) contamination of food crops pose severe public health risks, particularly in decentralized agricultural systems common in low-resource settings. Effective monitoring tools are critical for mitigating exposure, but their adoption is limited by barriers such as cost, infrastructure, and technical expertise. The objectives of this study were: (1) to evaluate common AFB1 detection methods, including enzyme-linked immunosorbent assays (ELISA) and lateral-flow assays (LFA), validated via high-performance liquid chromatography (HPLC), focusing on their suitability for possible applications in decentralized, low-resource settings; and (2) to conduct a barriers-to-use assessment for commonly available AFB1 detection methods and their applicability in low-resource settings. Among four ELISA kits, the AgraQuant Aflatoxin B1 2/50 ELISA Kit demonstrated the highest accuracy and precision, reliably quantifying AFB1 in maize and tortillas across 5–150 ppb with minimal cross-reactivity. For LFA, a smartphone-based algorithm achieved a high presence/absence accuracy rate of 84% but struggled with concentration prediction. The barriers-to-use analysis highlighted the practicality of low-cost tools like moisture readers for field screening but underscored their qualitative limitations. Advanced methods like HPLC and LC-MS offer greater precision but remain impractical due to their high costs and infrastructure requirements, suggesting a potential role for adapted ELISA or LFA methods as confirmatory approaches. These findings support the development of multi-tiered frameworks integrating affordable field tools with regional or centralized confirmatory testing. Addressing systemic barriers through capacity building, partnerships, and improved logistics will enhance AFB1 monitoring in decentralized systems, protecting public health in vulnerable communities.

Deformation Detection Method for Substation Noise Barrier Column Based on Deep Learning and Digital Image Technology

The dynamic identification of the deformation of a noise barrier column is of great significance to the monitoring of its health. At the same time, the maximum stress of the column is an important indicator for the evaluation of its health status. Traditional contact displacement monitoring installs sensors on the structure, requires a lot of wiring and data acquisition equipment, and establishes a relatively independent and stable displacement reference system. Affected by the environment, wear, and material aging, the efficiency and reliability of data acquisition are reduced. A monitoring method based on digital image has the advantages of non-contact monitoring, high precision, and strong reliability. The existing DIC detection methods are limited by processor performance and image resolution, which are difficult to apply to engineering detection. In this paper, a structural displacement identification method based on convolutional neural networks (CNNs) and DIC technology is proposed. In this method, the data set is formed according to the column displacement cloud image obtained by DIC analysis, and the data set is enhanced by data normalization and region division. Through the analysis of the number of network layers and learning rate, the model design of the deep learning network is carried out. The high-speed camera image results of the test are introduced and identified by the static loading test of the equal-scale sound barrier. The results show that the structural displacement identification method based on CNN and DIC technology can accurately identify the displacement change in the structure, which greatly improves the efficiency of image displacement calculation using DIC technology.

Naked-Eye Molecular Testing for the Detection of Xylella fastidiosa in Mallorca (Balearic Island) Almond Orchards by Colorimetric LAMP

Xylella fastidiosa (Xf) is a quarantine pathogen heavily affecting economically important crops worldwide. Different sequence types (STs) belonging to Xf subspecies are present in various areas of Spain, including the Balearic Islands, and cause the almond leaf scorch disease (ALSD) in Prunus spp. The increased demand for rapid tests for early detection of the pathogen should enforce strict containment measures. Molecular detection through isothermal amplification reactions enables simplified instrumentation and the use of raw nucleic acid extracts. Colorimetric loop-mediated isothermal amplification (cLAMP) was applied to rapidly detect Xf in naturally infected almonds on Mallorca Island (Spain), using a quick crude sap extraction without DNA purification. Following tissue homogenization, an alkaline treatment for target DNA extraction was conducted before the cLAMP test. The cLAMP assay was able to detect up to 100 CFU/mL of the Xf bacterial suspension diluted in healthy almond sap. The same crude extracts used in the cLAMP test were also tested by qPCR. An overall positive agreement of about 47% was observed between the results of the two techniques, while a decrease in cLAMP sensitivity was evident as the bacterial titer declined in infected plants over Cq > 26–27. This study shows the potential of the cLAMP application as a rapid and low-cost point-of-care diagnostic method for the timely monitoring of Xf directly in the field.

Algorithm optimization based on intelligent management of computer electrical equipment: A comprehensive method for PT power monitoring and remote fault indicator

In response to the accuracy limitations and response delays in current power and fault detection of electromechanical equipment, long short-term memory (LSTM) network algorithm was applied for intelligent optimization and management. Firstly, voltage and power data were collected through a potential transformer (PT), and wavelet transform (WT) was applied to remove noise. Fast Fourier transform (FFT) was utilized to extract key features. Secondly, a multi-layer long short-term memory network was designed, and back propagation algorithms and time series power data were used for LSTM network training to analyze abnormal fluctuations and trends in power data in real-time, and adjust threshold settings. Then, combining the model output and historical fault data, a fault mode knowledge base was established. Potential faults were determined through pattern matching, and signals were sent out through remote indicators. Finally, the algorithm model was evaluated. The research results showed that the weighted response time of voltage drop faults was shortened by 3.3%, and the information entropy values of nine experiments were distributed from 5.11 to 5.28. The diagnostic accuracy for frequency drift was improved by 11.1%. The comprehensive algorithm model used can effectively improve the accuracy of power monitoring and response speed. This study optimizes power monitoring and fault diagnosis by applying the LSTM network algorithm, addressing the limitations of existing methods in terms of real-time performance and accuracy. It effectively enhances the fault prediction capability and response speed of power systems, offering significant application value in the fields of smart grids and equipment management.

Research on Concrete Beam Damage Detection Using Convolutional Neural Networks and Vibrations from ABAQUS Models and Computer Vision

Researchers have already used vibration data and deep learning methods, such as Convolutional Neural Networks (CNNs), to detect structural damage. Moreover, some researchers have employed image-based displacement sensors (such as the template matching and edge detection methods) to obtain structural vibration information. It is necessary to verify whether deep learning methods can detect minor damage inside beams, for example, small hollowing in concrete. In addition, there is an urgent need to develop an effective image-based displacement sensor that can simultaneously detect a large number of reliable vibration data from different measurement points. In this study, the vibration data of two beam-ABAQUS models were used as the input data for a newly designed deep learning-based structural health monitoring method. There were 500 vibration samples for each case, and the peak of vibrations was several millimeters. The proposed CNN model can locate damage positions in beams with high accuracy (close to 100%), and the damage sizes are 3 cm and 6 cm. Laboratory experiments were carried out on four beams with different damage. The optimized displacement sensor developed based on the edge detection method was used to detect the displacement of the beams. Each beam had 200 vibration data, and there were 800 vibration data in total. These vibration data were used as input data to train the proposed deep learning architecture, and satisfactory accuracy was achieved in detecting the damage of the beams with an accuracy of 97%. The training process is satisfactory in that the training loss and validation loss dropped very quickly.

Three-Dimensional Deformation Prediction Based on the Improved Segmented Knothe–Dynamic Probabilistic Integral–Interferometric Synthetic Aperture Radar Model

Coal is the main mineral resource, but over-exploitation will cause a series of geological disasters. Interferometric synthetic aperture radar (InSAR) technology provides a superior monitoring method to compensate for the inadequacy of traditional measurements for mine surface deformation monitoring. In this study, the whole process of mining a working face in Huaibei Mining District, Anhui Province, is taken as the object of study. The ALOS PALSAR satellite radar image data and ground measurements were acquired, and the ISK-DPIM-InSAR deformation monitoring model with the dynamic probabilistic integral model (DPIM) was proposed by combining the probabilistic integral method (PIM) and the improved segmented Knothe time function (ISK). The ISK-DPIM-InSAR model constructs the inversion equations of InSAR line-of-sight deformation, north–south and east–west horizontal movement deformation, vertical deformation, inverts the optimal values of the predicted parameters of the workforce through the particle swarm algorithm, and substitutes it into the ISK-DPIM-InSAR model for predicting the three-dimensional dynamic deformation of a mining face. Simulated workface experiments determined the feasibility of the model, and by comparing the level observation results of the working face, it is confirmed that the ISK-DPIM-InSAR model can accurately monitor the three-dimensional deformation of the surface in the mining area.

Pond Water eDNA Reflects Broad Consistency with Surrounding Terrestrial Plant Ecosystems

This study evaluates the potential of using pond water eDNA to reflect the surrounding terrestrial plant communities, aiming to develop a sustainable, large-scale, and long-term monitoring method for plant diversity in forest ecosystems. Water samples were collected four times from two ponds with different vegetation types during the late spring to autumn seasons in Japan. eDNA was extracted from dissolved particles fractionated by sequential filtration through pore sizes of 200 µm, 5 µm, and 0.45 µm, followed by high-throughput amplicon sequencing targeting the plant rbcL gene. By comparing field surveys with the eDNA data, we identified 79% and 63% of plant families and genera, respectively, suggesting that pond water eDNA may reflect the surrounding terrestrial plant ecosystem. Additionally, different trends were observed in the seasonal variation of plant taxa and their composition detected in eDNA, based on particle size. This study highlights the potential of pond water eDNA to provide valuable insights into forest plant richness and seasonal dynamics, offering a novel approach for ecological monitoring.

Application of Unmanned Aerial Vehicle Remote Sensing System in Low Temperature Rain, Snow and Ice Disaster Monitoring

The unmanned aerial vehicle (UAV) remote sensing system, as a versatile and efficient monitoring tool, exhibits unique advantages in monitoring low-temperature rain, snow, and freezing disasters. Equipped with a variety of sensors, it is capable of collecting high-precision data even under extreme climatic conditions, thus addressing the deficiencies of traditional monitoring methods in terms of timeliness, coverage, and accuracy. Particularly when confronted with large-scale and rapidly evolving disasters such as snowstorms and ice freezes, UAVs can swiftly respond and transmit real-time data, providing disaster response departments with timely and reliable decision-making support. The application of UAV remote sensing systems in disaster data collection, processing, analysis, as well as real-time monitoring and early warning systems, renders it a central tool in contemporary disaster monitoring technology. Looking ahead, with the further advancement of remote sensing technologies and the integration of artificial intelligence and big data analytics, the role of UAVs in disaster monitoring will become even more pronounced. This paper will explore the specific applications of UAV remote sensing systems in low-temperature rain, snow, and freezing disasters, analyze the associated technical and managerial challenges and solutions, and forecast future development directions.

Soil moisture partitioning strategies in blowouts in the Hulunbeier grassland and response to rainfall

IntroductionSoil moisture and soil water retention capacity are key influencing factors for the normal growth and development of vegetation. Understanding the dynamic change characteristics of soil moisture in blowouts and soil water retention capacity is of great significance for the management of blowouts.MethodsThis study employs drying and in situ monitoring methods to select typical blowouts in different regions (sand pits, fringe zones, sand accumulation zones, and sand-grass transition zones) on the Hulunbuir Grassland. A large area of natural grassland surrounding these regions was chosen as the control (CK). Soil moisture at depths of 20, 40, 60, 100 and 200 cm below the surface was measured along the soil profile using the ECH2O-10HS soil moisture automatic monitoring instrument. The HOBO-RG3-M self-recording rain gauge was used to monitor rainfall. Soil water storage, coefficient of variation, and Pearson’s correlation coefficient were calculated to study the differences in soil moisture and the dynamic change regularity in soil moisture under different rainfall conditions. This research provides important theoretical support for the soil moisture distribution and vegetation restoration in the blowouts of the Hulunbuir Grassland.Results and discussionThe volumetric water content of the soil in the blowouts was 15.95%, the volumetric soil water content in different parts of the soil varied from low to high as follows: sand pit-I < sand-grass transition zone-IV < fringe zone-II < CK < sand accumulation zone-III. The soil volumetric water content of the 0–40 cm soil layer of the blowout was higher than 17.47%, and the soil volumetric water content of the 40–200 cm soil layer ranged from 12.13% to 17.47%. The volumetric water content of soil in various parts of the blowouts under different rainfall amounts had significant differences, with rainstorms and heavy rainfall effectively recharging the blowouts to a depth of 200 cm, and the blowouts responded strongly to heavy rainfall (71.5 mm). A gradual recovery of the pre-rainfall volumetric soil moisture content was seen approximately a week after rainstorms. The water retention and storage capacity of blowout soils was significantly higher than that of CK, the soil water storage capacity of different zones ranked in descending order as the sand accumulation zone (1875.38 mm) > edge zone (1373.22 mm) > CK (1188.36 mm) > sand pit (1000.39 mm) > sand–grass transition zone (803.90 mm). The correlation coefficient of sand pits and sand cover was 0.5612, and that of sand accumulation zones and sand cover was 0.5845, which confirmed that sand cover enhanced the water retention capacity of the localized area of blowouts (sand accumulation zones).

Investigating LiDAR Metrics for Old-Growth Beech- and Spruce-Dominated Forest Identification in Central Europe

Old-growth forests are essential for maintaining biodiversity, as they are formed by the complexity of diverse forest structures, such as broad variations in tree height and diameter (DBH) and conditions of living and dead trees, leading to various ecological niches. However, many efforts of old-growth forest mapping from LiDAR have targeted only one specific forest structure (e.g., stand height, basal area, or stand density) by deriving information through a large number of LiDAR metrics. This study introduces a novel approach for identifying old-growth forests by optimizing a set of selected LiDAR standards and structural metrics. These metrics effectively capture the arrangement of multiple forest structures, such as canopy heterogeneity, multilayer canopy profile, and canopy openness. To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. The results demonstrate that the LiDAR structural metrics (i.e., advanced LiDAR metrics related to multiple canopy structures) are more important and effective in distinguishing old- and second-growth forests than LiDAR standard metrics (i.e., height- and density-based LiDAR metrics) using the European definition of a 150-year stand age threshold for old-growth forests. These structural metrics were then used as predictors for the final classification of old-growth forests, yielding an overall accuracy of 78%, with a true skill statistic (TSS) of 0.58 for the test dataset. This study demonstrates that using a few structural LiDAR metrics provides more information than a high number of standard LiDAR metrics, particularly for identifying old-growth forests in mixed temperate forests. The findings can aid forest and national park managers in developing a practical and efficient old-growth forest identification and monitoring method using LiDAR.

Novel Visualization of Building Earthquake Response Recorded by a Dense Network of Sensors

The strong motion records collected in full-scale structures provide the ultimate evidence of how real structures, in situ, respond to earthquakes. This paper presents a novel method for visualization, in three dimensions (3D), of the collective motion recorded by a dense array of sensors in a building. The method is based on one- and two-dimensional biharmonic spline interpolation of the motion recorded by multiple sensors on the same or multiple floors. It is demonstrated on novel data that have been recorded recently in a 50-story skyscraper, uniquely instrumented with multiple triaxial accelerometers per floor, approximately at every five floors above ground and at two basement levels, and with rotational seismometers and two borehole arrays measuring the motion of the soil very near the building foundation. The method is computationally efficient and suitable for real-time application and rapid assessment of structural health. The animations provide invaluable insight into the 3D structural response of the building as a whole, including wave propagation through the structure and the interplay between translations and rotations, which will be useful for testing existing and developing new methods for structural health monitoring of buildings and for the further development of building design codes. Animations of selected earthquakes can be found on YouTube at @TPYC-seismic.