An approach based on Landsat images for shoreline monitoring to support integrated coastal management - a case study, Ezbet Elborg, Nile Delta, Egypt

Monitoring the dynamic behavior of shorelines is an essential factor for integrated coastal management (ICM). In this study, satellite-derived shorelines and corresponding eroded and accreted areas of coastal zones have been calculated and assessed for 15 km along the coasts of Ezbet Elborg, Nile Delta, Egypt. A developed approach is designed based on Landsat satellite images combined with GIS to estimate an accurate shoreline changes and study the effect of seawalls on it. Landsat images for the period from 1985 to 2018 are rectified and classified using Supported Vector Machines (SVMs) and then processed using ArcGIS to estimate the effectiveness of the seawall that was constructed in year 2000. Accuracy assessment results show that the SVMs improve images accuracy up to 92.62% and the detected shoreline by the proposed method is highly correlated (0.87) with RTK-GPS measurements. In addition, the shoreline change analysis presents that a dramatic erosion of 2.1 km2 east of Ezbet Elborg seawall has occurred. Also, the total accretion areas are equal to 4.40 km2 and 10.50 km2 in between 1985-and-2000 and 2000-and-2018, respectively, along the southeast side of the study area

Hybrid Wavelet and Principal Component Analyses Approach for Extracting Dynamic Motion Characteristics from Displacement Series Derived from Multipath-Affected High-Rate GNSS Observations

Nowadays, the high rate GNSS (Global Navigation Satellite Systems) positioning methods are widely used as a complementary tool to other geotechnical sensors, such as accelerometers, seismometers, and inertial measurement units (IMU), to evaluate dynamic displacement responses of engineering structures. However, the most common problem in structural health monitoring (SHM) using GNSS is the presence of surrounding structures that cause multipath errors in GNSS observations. Skyscrapers and high-rise buildings in metropolitan cities are generally close to each other, and long-span bridges have towers, main cable, and suspender cables. Therefore, multipath error in GNSS observations, which is typically added to the measurement noise, is inevitable while monitoring such flexible engineering structures. Unlike other errors like atmospheric errors, which are mostly reduced or modeled out, multipath errors are the largest remaining unmanaged error sources. The high noise levels of high-rate GNSS solutions limit their structural monitoring application for detecting load-induced semi-static and dynamic displacements. This study investigates the estimation of accurate dynamic characteristics (frequency and amplitude) of structural or seismic motions derived from multipath-affected high-rate GNSS observations. To this end, a novel hybrid model using both wavelet-based multiscale principal component analysis (MSPCA) and wavelet transform (MSPCAW) is designed to extract the amplitude and frequency of both GNSS relative- and PPP- (Precise Point Positioning) derived displacement motions. To evaluate the method, a shaking table with a GNSS receiver attached to it, collecting 10 Hz data, was set up close to a building. The table was used to generate various amplitudes and frequencies of harmonic motions. In addition, 50-Hz linear variable differential transformer (LVDT) observations were collected to verify the MSMPCAW model by comparing their results. The results showed that the MSPCAW could be efficiently used to extract the dynamic characteristics of noisy dynamic movements under seismic loads. Furthermore, the dynamic behavior of seismic motions can be extracted accurately using GNSS-PPP, and its dominant frequency equals that extracted by LVDT and relative GNSS positioning method. Its accuracy in determining the amplitude approaches 91.5% relative to the LVDT observations

Validation of performance of real-time kinematic PPP. A possible tool for deformation monitoring

Structural failures (bridge or building collapses) and geohazards (landslides, ground subsi- dence or earthquakes) are worldwide problems that often lead to significant economic and loss of life. Monitoring the deformation of both natural phenomena and man-made struc- tures is a major key to assessing structural dynamic responses. Actually, this monitoring process is under real-time demand for developing warning and alert systems. One of the most used techniques for real-time deformation monitoring is the Global Navigation Satellite System (GNSS) real-time procedure, where the relative positioning approach, using a well-known reference station, has been applied. This study was conducted to evaluate the actual quality of the real-time kinematic Precise Point Positioning (PPP) GNSS solution for deformation monitoring, where it can be concluded that a promise tool is under development and should be taken into account on actual and near future real-time deformation monitoring studies and applications.This research was supported by the Spanish Science and Innovation Directorate project number AYA2010-18706 and the Generalitat Valenciana Geronimo Forteza research program with project number FPA/2014/056.Martín Furones, ÁE.; Anquela Julián, AB.; Dimas Pagés, A.; Cos-Gayón López, FJ. (2015). Validation of performance of real-time kinematic PPP. A possible tool for deformation monitoring. Measurement. 69:95-108. https://doi.org/10.1016/j.measurement.2015.03.026S951086

Detection of ground motions using high-rate GPS time-series

Monitoring surface deformation in real-time help at planning and protecting infrastructures and populations, manage sensitive production (i.e. SEVESO-type) and mitigate long-term consequences of modifications implemented. We present RT-SHAKE, an algorithm developed to detect ground motions associated with landslides, sub-surface collapses, subsidences, earthquakes or rock falls. RT-SHAKE detects first transient changes in individual GPS time series before investigating for spatial correlation(s) of observations made at neighbouring GPS sites and eventually issue a motion warning. In order to assess our algorithm on fast (seconds to minute), large (from 1cm to meters) and spatially consistent surface motions, we use the 1Hz GEONET GNSS network data of the Tohoku-Oki MW9.0 2011 as a test scenario. We show the delay of detection of seismic wave arrival by GPS records is of ~10 seconds with respect to an identical analysis based on strong-motion data and this time delay depends on the level of the time-variable noise. Nevertheless, based on the analysis of the GPS network noise level and ground motion stochastic model, we show that RT-SHAKE can narrow the range of earthquake magnitude, by setting a lower threshold of detected earthquakes to MW6.5-7, if associated with a real-time automatic earthquake location system

Time-Series and Frequency-Spectrum Correlation Analysis of Bridge Performance Based on a Real-Time Strain Monitoring System

Monitoring bridges’ performance is a vital task to ensure their safety and to plan their maintenance operations. Therefore, it is very important to monitor bridges’ behavior and to analyze their measured data. In this study, the time-series and frequency-spectrum correlation analyses are used to study the performance of Fu-Sui Bridge under harsh environmental and traffic loads. It investigates the bridge performance based on a real-time strain monitoring system, and the ambient environmental and traffic loads are studied and discussed. Furthermore, a simplified method based on signal processing is developed and used to estimate the traffic volumes. The results of this study reveal that the traffic loads influence on static strain is obviously lower than that of air temperature and temperature changes of the bridge cross-section; the non-linearity behavior of the bridge during summer time is more than winter time; and the stability of the whole bridge during winter time is more than during summer time. The time-series and vibration analyses also show that the bridge performance in terms of its rigidity and stability is higher during winter time

Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures

The Global Positioning System (GPS) is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents