Analysis and prediction of ground deformation in Yinxi Industrial Park based on time-series InSAR technology.

Monitoring ground deformation in industrial parks is of great importance for the economic development of urban areas. However, limited research has been conducted on the deformation mechanism in industrial parks, and there is a lack of integrated monitoring and prediction models. Therefore, this study proposes a comprehensive monitoring and prediction model for industrial parks, utilizing time-series Interferometry Synthetic Aperture Radar (InSAR) technology and the Whale Optimization Algorithm-Back Propagation (WOA-BP) neural network algorithm. Taking Yinxi Industrial Park in Baiyin District as a case study, we used 68 scenes of Sentinel-1A ascending and descending orbit data from June 2018 to April 2021. The Stanford Method for Persistent Scatterers-Permanent Scatterers (StaMPS-PS) and the Small Baseline Subsets-Interferometry Synthetic Aperture Radar (SBAS-InSAR) technologies were employed to obtain the surface deformation information of the park. The deformation information obtained by the two technologies was cross-validated in terms of temporal and spatial distribution, and the vertical and east-west deformation of the park was obtained by combining the ascending and descending orbit data. The results show that the deformation feature points in the line of sight (LOS) direction obtained by the two technologies have a high consistency in spatial distribution, using the ascending orbit data as an example. Additionally, the SBAS-InSAR technology was used to obtain the east-west and vertical deformation results of the park after merging the ascending and descending orbit data for the same period. It was found that the park is mainly affected by vertical deformation, with a maximum subsidence rate of 14.67 mm/yr. The subsidence areas correspond to the deformation positions observed in field survey photos. Based on the ascending orbit deformation data, the two technologies were validated with 585 points of the same latitude and longitude, and the coefficient of determination R2 was found to be 0.82, with a root mean square error (RMSE) of 2.20 mm/a. The deformation rates were also highly consistent. Due to the 47% increase in the number of sampling points provided by the StaMPS-PS technique compared to the SBAS-InSAR technique, the former was found to be more applicable in the industrial park. Based on the ground deformation mechanism in the park, we combined the StaMPS-PS technique with the WOA-BP neural network to construct a deformation zone prediction model. We conducted predictive studies on the deformation zones of buildings and roads within the park, and the results showed that the WOA-optimized BP neural network achieved higher accuracy and lower overall error compared to the unoptimized network. Finally, we analyzed and discussed the geological conditions and inducing factors of ground deformation in the park, providing a reference for a better understanding of the deformation mechanism and early warning of disasters in the industrial park.

Effective fall detection and post-fall breath rate tracking using a low-cost CW Doppler radar sensor.

Existing low-cost Doppler radar-based fall detection systems encounter challenges due to false alarms and the absence of post-fall health tracking, significantly impacting their accuracy and overall compatibility for fall detection. This paper presents a cost-effective, robust solution for a fall detection system with the post-fall health tracking facility using a 3.18 GHz continuous-wave Doppler radar sensor. The experimental data acquisition is conducted in-house under the guidance of a healthcare expert, involving various activities such as standing, sitting, sleeping, running, walking, falling, sit-to-stand, and stand-to-sit transitions. We propose an algorithm comprising four hierarchical stages, each with specific objectives. Considering the complexity, the model is trained differently for each stage to optimize the classification accuracy. The system architecture is designed to minimize computational costs and power consumption through modular implementation in stages, utilizing low-power equipment and incorporating traditional machine-learning algorithms. Experimental results demonstrate a fall detection accuracy of 93.24% and breath rate measurement error of 2.26%, which is competitive with recent state-of-the-art approaches. Obtained results highlight the effectiveness of the proposed system in addressing the challenges of false alarms and post-fall health tracking while maintaining cost-efficiency and accuracy in fall detection.

Origami plot: a novel multivariate data visualization tool that improves radar chart.

OBJECTIVES: We propose the origami plot, which maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multicriteria decision-making. STUDY DESIGN AND SETTING: Built upon a radar chart, the origami plot adds additional auxiliary axes and points such that the area of the connected region of all dots is invariant to the ordering of axes. As such, it enables ranking different individuals by the overall performance for multicriteria decision-making while maintaining the intuitive visual appeal of the radar chart. We develop extensions of the origami plot, including the weighted origami plot, which allows reweighting of each attribute to define the overall performance, and the pairwise origami plot, which highlights comparisons between two individuals. RESULTS: We illustrate the different versions of origami plots using the hospital compare database developed by the Centers for Medicare & Medicaid Services (CMS). The plot shows individual hospital's performance on mortality, readmission, complication, and infection, as well as patient experience and timely and effective care, as well as their overall performance across these metrics. The weighted origami plot allows weighing the attributes differently when some are more important than others. We illustrate the potential use of the pairwise origami plot in electronic health records (EHR) system to monitor five clinical measures (body mass index [BMI]), fasting glucose level, blood pressure, triglycerides, and low-density lipoprotein ([LDL] cholesterol) of a patient across multiple hospital visits. CONCLUSION: The origami plot is a useful visualization tool to assist multicriteria decision making. It improves radar charts by avoiding potential misuse of the connected regions. It has several new features and allows flexible customization.

Analysis of Perception Accuracy of Roadside Millimeter-Wave Radar for Traffic Risk Assessment and Early Warning Systems.

Millimeter-wave (MMW) radar is essential in roadside traffic perception scenarios and traffic safety control. For traffic risk assessment and early warning systems, MMW radar provides real-time position and velocity measurements as a crucial source of dynamic risk information. However, due to MMW radar's measuring principle and hardware limitations, vehicle positioning errors are unavoidable, potentially causing misperception of the vehicle motion and interaction behavior. This paper analyzes the factors influencing the MMW radar positioning accuracy that are of major concern in the application of transportation systems. An analysis of the radar measuring principle and the distributions of the radar point cloud on the vehicle body under different scenarios are provided to determine the causes of the positioning error. Qualitative analyses of the radar positioning accuracy regarding radar installation height, radar sampling frequency, vehicle location, posture, and size are performed. The analyses are verified through simulated experiments. Based on the results, a general guideline for radar data processing in traffic risk assessment and early warning systems is proposed.

Analysis of the characteristics and causations of surface deformation based on TS-InSAR: a case study of Jimo district, China.

Due to the rapid economic development and urban construction and the high exploitation rate of groundwater and geothermal resource, Jimo district existed a potential threat of surface deformation. To clarify the characteristics and causations of surface deformation, this study firstly used SBAS-InSAR (Small Baseline Subset-Interferometric Synthetic Aperture Radar) technology to analyze the surface defor-mation distribution in the whole research area. Then, three areas with different surface cover conditions were selected to analyze the causations of surface deformation. Lastly, taking central urban area as the key research area, surface deformation causations were analyzed in detail based on PS-InSAR (Persistent Scatter-Interferometric Synthetic Aperture Radar) technology. The study found that, in coastal mollisol area, farmland area, and hot spring area, the maximum subsidence velocity reached up to 46.8 mm/a, 24 mm/a, and 19.1 mm/a, respectively. The factors, including surface loading, precipitation, and the groundwater level, were the causations of surface deformation in different research areas. The trend of the surface deformation curve was consistent with that of the groundwater level curve in the central urban area, but the response time of surface deformation lagged behind the change of groundwater level by approximately 4 months.

Detection of marine oil spills from radar satellite images for the coastal ecological risk assessment.

Coastal ecosystems offer substantial support and space for the sustainable development of human society, and hence the ecological risk evaluation of coastal ecosystems is of great significance. In this article, we propose an innovative framework for evaluating coastal ecological risk by considering oil spill risk information and environmental vulnerability information. Specifically, a deep learning based marine oil spill monitoring method is presented to obtain the oil spill risk information from Sentinel-1 polarimetric synthetic aperture radar (PolSAR) images. The environmental vulnerability information is then obtained from biological sample data and habitat information. Finally, a weighted probability model is introduced to utilize the oil spill risk and environmental vulnerability information, to evaluate the coastal ecological risk. In the experimental part, the proposed oil spill monitoring method shows its reliability in global ocean areas, and the proposed model is adopted to evaluate the ecological risk in Jiaozhou Bay, China. The results show that the ecological situation of more than half of the areas in Jiaozhou Bay is unstable, and the areas with high risk are mainly concentrated in the ports, shipping channels, and those areas with high biodiversity. This study provides some new perspectives on ecological risk assessment for coastal ecosystems, facilitating the planning process and the actions to be taken in response to the accidents that occur in the ocean, especially oil spill accidents.

Assessment of ground deformation and seismicity in two areas of intense hydrocarbon production in the Argentinian Patagonia.

The exploitation of both conventional and unconventional hydrocarbons may lead to still not well-known environmental consequences such as ground deformation and induced/triggered seismicity. Identifying and characterizing these effects is fundamental for prevention or mitigation purposes, especially when they impact populated areas. Two case studies of such effects on hydrocarbon-producing basins in Argentina, the Neuquén and the Golfo de San Jorge, are presented in this work. The intense hydrocarbon production activities in recent years and their potential link with the occurrence of two earthquakes of magnitude 4.9 and 5 near the operating well fields is assessed. A joint analysis of satellite radar interferometry and records of fluid injection and extraction demonstrate that, between 2017 and 2020, vertical ground displacements occurred in both study areas over active well fields that might indicate a correlation to hydrocarbon production activities. Coseismic deformation models of the two earthquakes constrain source depths to less than 2 km. The absence of seismicity before the beginning of the hydrocarbon activities in both areas, and the occurrence of the two largest and shallow earthquakes in the vicinity of the active well fields just after intensive production periods, points towards the potential association between both phenomena.

Can the Supido Radar Be Used for Measuring Ball Speed during Soccer Kicking? A Reliability and Concurrent Validity Study of a New Low-Cost Device.

The aim was to analyze the reliability and validity of a low-cost instrument, based on a radar system, to quantify the kicking ball speed in soccer. A group of 153 male soccer players (under-13, n = 53; under-15, n = 54; under-18, n = 46) participated in this study. Each player performed three kicks on the goal in a standardized condition while the ball speed was measured with three different devices: one Radar Stalker ATS II® (reference criterion) and two Supido Radar® (Supido-front of the goal and Supido-back of the goal). The standard error of measurement (SEM) expressed as a coefficient of variation (CV) and the intraclass correlation coefficient (ICC) were employed for assessing the reliability of each instrument. Stalker and Supido-back showed very high absolute (CV = 4.0-5.4%) and relative (ICC = 0.945-0.958) reliability, whereas Supido-front resulted in moderate to low reliability scores (CV = 7.4-15%, ICC = 0.134-0.693). In addition, Lin's concordance correlation coefficient (CCC) values revealed an 'almost perfect' agreement between Stalker and Supido-back for the average (r = 0.99) and maximal (r = 0.98) ball speed, regardless of the ball speed range analyzed. However, Supido-front resulted in a poor degree of concordance (CCC = 0.688) and a high magnitude of error (17.0-37.5 km·h-1) with the reference Stalker radar gun. The Supido Radar® placed behind the goal could be considered a reliable and valid device for measuring ball speed in soccer.

Noncontact assessment for fatigue based on heart rate variability using IR-UWB radar.

Physical fatigue can be assessed using heart rate variability (HRV). We measured HRV at rest and in a fatigued state using impulse-radio ultra wideband (IR-UWB) radar in a noncontact fashion and compared the measurements with those obtained using electrocardiography (ECG) to assess the reliability and validity of the radar measurements. HRV was measured in 15 subjects using radar and ECG simultaneously before (rest for 10 min before exercise) and after a 20-min exercise session (fatigue level 1 for 0-9 min; fatigue level 2 for 10-19 min; recovery for ≥ 20 min after exercise). HRV was analysed in the frequency domain, including the low-frequency component (LF), high-frequency component (HF) and LF/HF ratio. The LF/HF ratio measured using radar highly agreed with that measured using ECG during rest (ICC = 0.807), fatigue-1 (ICC = 0.712), fatigue-2 (ICC = 0.741) and recovery (ICC = 0.764) in analyses using intraclass correlation coefficients (ICCs). The change pattern in the LH/HF ratios during the experiment was similar between radar and ECG. The subject's body fat percentage was linearly associated with the time to recovery from physical fatigue (R2 = 0.96, p < 0.001). Our results demonstrated that fatigue and rest states can be distinguished accurately based on HRV measurements using IR-UWB radar in a noncontact fashion.

Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning.

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram and point clouds for 19 human joints. We developed a multilayer Convolutional Neural Network (CNN) to recognize and classify five different gait patterns with an accuracy of 95.7 to 98.8% using MMW radar data. During training of the CNN algorithm, we used the extracted 3D coordinates of 25 joints using the Kinect V2 sensor and compared them with the point clouds data to improve the estimation. Finally, we performed a real-time simulation to observe the point cloud behavior for different activities and validated our system against the ground truth values. The proposed method demonstrates the ability to distinguish between different human activities to obtain clinically relevant gait information.

Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter.

This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.

A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm.

A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R2 = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.

Derivation and validation of the Rapid Assessment of Dementia Risk (RADaR) for older adults.

BACKGROUND: There is no practical dementia risk score in the clinical setting. OBJECTIVE: To derive and validate a score obtained by a rapid and simple assessment, which guides primary care providers in predicting the risk of dementia among older adults. DESIGN: A total of 4178 participants from three longitudinal cohorts (mean age at baseline = 76.8 [SD = 7.6] years), without baseline dementia, followed annually for a median of 10 years (IQR: 5 to16 years, Reverse Kaplan-Meier). PARTICIPANTS: To derive the score, we used data from 1,780 participants from the Rush Memory and Aging Project (93% White). To validate the score, we used data from 1,299 participants from the Religious Order Study (92% White), and to assess generalizability, 679 participants from the Minority Aging Research Study (100% Black). MEASUREMENTS: Clinician-based dementia diagnosis at any time after baseline and predictive variables associated with dementia risk that can be collected in a primary care setting: demographics, clinical indicators, medical history, memory complaints, cognitive and motor tests, and questions to assess functional disability, depressive symptoms, sleep, social isolation, and genetics (APOE e4 and AD polygenic risk score). RESULTS: At baseline, age, memory complaint, the ability to handle finances, the recall of the month, recall of the room, and recall of three words, were associated with the cumulative incidence of dementia, in the derivation cohort. The discrimination of the RADaR (Rapid Risk Assessment of Dementia) was good for the derivation and external-validation cohorts (AUC3 years = 0.82-0.86), compared to the overall discrimination of age alone (AUC3 years = 0.73), a major risk factor for dementia. Adding genetic data did not increase discrimination. LIMITATIONS: Participants were volunteers, may not represent the general population. CONCLUSIONS: The RADaR, derived from community-dwelling older persons, is a brief and valid tool to predict dementia risk at 3 years in older White and Black persons.

Assessment of patient burden from dry eye disease using a combination of five visual analogue scales and a radar graph: a pilot study of the PENTASCORE.

BACKGROUND/AIMS: Dry eye disease (DED) questionnaires would ideally be easy and fast to answer and explore the main aspects of disease burden and satisfaction (efficacy and tolerability) with treatment. This pilot study evaluates the Pentascore questionnaire for routinely assessing DED. METHODS: The Pentascore combines five visual analogue scales (VAS) to assess the intensity and frequency of ocular pain/discomfort, the impact of DED on daily activities and visual tasks and the efficacy and tolerability of ongoing DED treatment(s). This retrospective study compared Pentascore to the Ocular Surface Disease Index (OSDI) questionnaire, fluorescein tear break-up-time, corneal staining and Schirmer I test. RESULTS: For 161 DED patients, the algebraic mean (±SE) for the Pentascore was 52.6±1.8, the mean standardised area of the radar graph was 32.1±1.7 (out of 100) and the mean score for the OSDI was 52.6±1.8. Both questionnaires were highly statistically correlated (R=0.74 for both algebraic score and radar area, p<0.001), and each of five Pentascore VAS was significantly correlated with the OSDI (p<0.05). Corneal staining score (CSS) was correlated with two Pentascore VAS (impact of DED on daily activities and visual tasks), and there was a trend towards a correlation between CSS and the area of the radar graph (p=0.09). CONCLUSIONS: This pilot study indicates that the Pentascore can rapidly and effectively assess the burden of DED and satisfaction with treatments. Compared with the algebraic mean, the estimation of the area of the radar graph likely improves the sensitivity for detecting differences/changes in symptoms and treatment follow-up.

Flood damage assessment with Sentinel-1 and Sentinel-2 data after Sardoba dam break with GLCM features and Random Forest method.

Accurate mapping and monitoring of flooded areas are immensely required for disaster management purposes, such as for damage assessment and mitigation. In this study, the flood damage mapping performances of two satellite Earth Observation sensors, i.e., European Space Agency's Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) multispectral optical instruments, were evaluated using the Random Forest (RF) supervised classification method and various feature types. The study area was Sardoba Reservoir (Uzbekistan) and its surroundings, in which a disastrous dam failure occurred on May 1, 2020. After the failure of a part of the earthfill dam, a large region with settlements and agricultural areas in Uzbekistan and Kazakhstan was flooded. S1 and S2 cloudless data with a short temporal interval acquired soon after the event were available for the area. Four different data availability scenarios, such as (i) only S1 pre- and post-flood data; (ii) only S2 pre- and post-flood data; (iii) S1 pre- and post-flood and S2 pre-flood data; and (iv) S1 and S2 pre- and post-flood data were evaluated in terms of classification accuracy. In addition to the polarization information of S1 and the intensity values of S2 bands, feature maps produced from these datasets, such as vegetation and water indices, textural information obtained from gray level co-occurrence matrix (GLCM), and the principal component analysis (PCA) bands were employed in the RF method. The results show that the fusion of S1 and S2 data exhibit very high classification accuracy for the flooded areas and can separate the inundated vegetation as well. The use of S2 pre-event data together with the S1 pre- and post-event data is recommended for obtaining high accuracy even when post-event optical data is not available.

An assessment of flood event along Lower Niger using Sentinel-1 imagery.

Flood incidence, especially in global south countries, is one of the most challenging natural disasters in the light of changing climates, especially in Africa. This is because African countries have a large sub-section of vulnerable people who either live within flood-prone areas or depend on flood-prone areas for their means of livelihood such as we have in Nigeria. Recent flood disasters in Nigeria have been of major concern to people, communities, and institutions. Several studies have been conducted on flood events and their impacts in Nigeria. However, most of these studies are on public perception, flood modeling (rainfall-runoff), and the provision of binary maps with few studies engaging in the use of satellite observations, especially the use of Synthetic Aperture Radar, SAR, to enhance flood early warning designs, especially in Sub-Saharan Africa. This study is aimed at assessing the 2018 flood event in Lokoja, Kogi State, Nigeria, using the Sentinel-1 imagery. The study confirmed that a total of 69 buildings out of 611 buildings were affected by the flood disaster with about 24,902 people displaced by this singular flood event. The study shows that backscattering from microwave sensors provides very useful information for highlighting inundated areas that could prove useful in forecasting, monitoring, and precision-based flood early warning designs before, during, and after flood events.

Tiresias: A low-cost networked UWB radar system for in-home monitoring of dementia patients.

This paper describes Tiresias, a low-cost, unobtrusive networked radar system designed to monitor vulnerable patients in domestic environments and provide high quality behavioural and health data. Dementia is a disease that affects millions worldwide and progressively degrades an individual's ability to care for themselves. Eventually most people living with dementia will need to reside in assisted living facilities as they become unable to care for themselves. Understanding the effects dementia has on ability to self-care and extending the length of time people living with dementia can remain living independently are key goals of dementia research and care. The networked radar system proposed in this paper is designed to provide high quality behavioural and health data from domestic environments. This is achieved using multiple radar sensors networked together with their data outputs integrated and processed to produce high confidence measures of position and movement. It is hoped the data produced by this system will both provide insights into how dementia progresses, and also help monitor vulnerable individuals in their own homes, allowing them to remain independent longer than would otherwise be possible.

Accuracy Tests and Precision Assessment of Localizing Underground Utilities Using GPR Detection.

Applying georadar (GPR) technology for detecting underground utilities is an important element of the comprehensive assessment of the location and ground infrastructure status. These works are usually connected with the conducted investment processes or serialised inventory of underground fittings. The detection of infrastructure is also crucial in implementing the BIM technology, 3D cadastre, and planned network modernization works. GPR detection accuracy depends on the type of equipment used, the selected detection method, and external factors. The multitude of techniques used for localizing underground utilities and constantly growing accuracy demands resulting from the fact that it is often necessary to detect infrastructure under challenging conditions of dense urban development leads to the need to improve the existing technologies. The factor that motivated us to start research on assessing the precision and accuracy of ground penetrating radar detection was the need to ensure the appropriate accuracy, precision, and reliability of detecting underground utilities versus different methods and analyses. The results of the multi-variant GPR were subjected to statistical testing. Various analyses were also conducted, depending on the detection method and on the current soil parameters using a unique sensor probe. When planning detection routes, we took into account regular, established grids and tracked the trajectory of movement of the equipment using GNSS receivers (internal and external ones). Moreover, a specialist probe was used to evaluate the potential influence of the changing soil conditions on the obtained detection results. Our tests were conducted in a developed area for ten months. The results confirmed a strong correlation between the obtained accuracy and the measurement method used, while the correlation with the other factors discussed here was significantly weaker.

Development of Condition Assessment Index of Ballast Track Using Ground-Penetrating Radar (GPR).

The condition of the ballast is a critical factor affecting the riding quality and the performance of a track. Fouled ballast can accelerate track irregularities, which results in frequent ballast maintenance requirements. Severe fouling of the ballast can lead to track instability, an uncomfortable ride and, in the worst case, a derailment. In this regard, maintenance engineers perform routine track inspections to assess current and future ballast conditions. GPR has been used to assess the thickness and fouling levels of ballast. However, there are no potent procedures or specifications with which to determine the level of fouling. This research aims to develop a GPR analysis method capable of evaluating ballast fouling levels. Four ballast boxes were constructed with various levels of fouling. GPR testing was conducted using a GSSI (Geophysical Survey Systems, Inc.) device (400, 900, 1600 MHz), and a KRRI (Korea Railroad Research Institute) GPR device (500 MHz), which was developed for ballast tracks. The dielectric permittivity, scattering of the depth (thickness) values, signal strength at the ballast boundary, and area of the frequency spectrum were compared against the fouling level. The results show that as the fouling level increases, the former two variables increase while the latter two decrease. On the basis of these observations, a new integrated parameter, called a ballast condition scoring index (BCSI), is suggested. The BCSI was verified using field data. The results show that the BCSI has a strong correlation with the fouling level of the ballast and can be used as a fouling-level-indicating parameter.