Processing Method of Missing Data in Dam Safety Monitoring

A large amount of data obtained by dam safety monitoring provides the basis to evaluate the dam operation state. Due to the interference caused by equipment failure and human error, it is common or even inevitable to suffer the loss of measurement data. Most of the traditional data processing methods for dam monitoring ignore the actual correlation between different measurement points, which brings difficulties to the objective diagnosis of dam safety and even leads to misdiagnosis. Therefore, it is necessary to conduct further study on how to process the missing data in dam safety monitoring. In this study, a data processing method based on partial distance combining fuzzy C-means with long short-term memory (PDS-FCM-LSTM) was proposed to deal with the data missing from dam monitoring. Based on the fuzzy clustering performed for the measurement points of the same category deployed on the dam, the membership degree of each measurement point to cluster center was described by using the fuzzy C-means clustering algorithm based on partial distance (PDS-FCM), so as to determine the clustering results and preprocess the missing data of corresponding measurement points. Then, the bidirectional long short-term memory (LSTM) network was applied to explore the pattern of changes of measurement values under identical clustering conditions, thus processing the data missing from monitoring effectively.

Development of Dam Safety Remote Monitoring and Evaluation System

More than 4,000 dams are constructed in Thailand for several purposes, including water supply, flood control, irrigation, and hydropower generation. Among these dams, 14 large dams are operated by the Electricity Generating Authority of Thailand (EGAT). As a dam operator, EGAT is committed to ensuring dam safety by regularly conducting dam inspections and maintenance. This paper presents the development and practical applications of the Dam Safety Remote Monitoring System (DS-RMS). The objective of DS-RMS is to enhance the EGAT’s implementation of its dam safety program in terms of dam monitoring by instrumentation to satisfy international recommendations. DS-RMS consists of five subsystems: Dam Behavior, Reservoir Operation, Earthquake Monitoring, Expert System and Public Communication. DS-RMS has been deployed at 14 large EGAT-operated dams across the country since 2016. Results show that the novel features of DS-RMS enable faster and more reliable dam safety monitoring and evaluation processes.

Toward a Comprehensive Dam Monitoring: On-Site and Remote-Retrieved Forcing Factors and Resulting Displacements (GNSS and PS–InSAR)

Many factors can influence the displacements of a dam, including water level variability and environmental temperatures, in addition to the dam composition. In this work, optical-based classification, thermal diachronic analysis, and a quasi-PS (Persistent Scatter) Interferometric SAR technique have been applied to determine both forcing factors and resulting displacements of the crest of the Castello dam (South Italy) over a one-year time period. The dataset includes Sentinel-1A images acquired in Interferometric Wide swath mode using the Terrain Observation with Progressive Scans SAR (TOPSAR); Landsat 8 Thermal Infrared Sensor (TIRS) thermal images, and Global Navigation Satellite System (GNSS) for interpreting the motion of the top of the dam retrieved via interferometry. Results suggest that it is possible to monitor both dam water level and temperature periodic forcing factors and resulting displacements via a synergistic use of different satellite images.

Choice of mathematic models of soils in static and seismic analyses of embankment dams

Relevance. Numerical analyses of behavior or stress-strain state (SSS) of embankment dams are usually performed using various computational programs, which use different, often complex mathematical models of soils. However, the right choice of these models is left to the program user, who usually has not enough experience in this field of knowledge, and therefore the results of these analyses are often obscure and erroneous. The aims of the work - development of recommendations for selecting the most reliable mathematical models of soils in numerical analyses of embankment dams and comparing these models with the most common models in modern world practice of their application. Methods. Deep comparative analysis of many soil models was conducted on the use of the soil models in numerical analyses of embankment dams during authors work in ICOLD Committee on Analysis and Dam Design. Results. On the basis of the evaluation of the reliability of soil models, recommendations were obtained on the choice and application of these models in the numerical analyses of SSS of embankment dams under action of static and seismic impacts; interactions between the results of these analyses and dam monitoring data were identified.

The Methods Used in Monitoring of Large Dams in Turkey

Turkey is a very rich country in terms of water resources. However, in order to use these resources efficiently, water must be stored safely. In this context, the best form of storage is the construction of dams. In our country, large dams, which are among the world's important dams, are being built. Although there are many advantages of large dams, the loss of life and property will be so great in case of a possible deformation or destruction. The fact that dams are under the influence of various forces, as well as being located in the earthquake zone of our country, necessitates continuous monitoring of our dams. Dams are generally monitored using geodetic methods and geotechnical devices. However, mostly details of these two methods are evaluated separately in dam monitoring. Evaluating the results of both methods together is very important for the comprehensive monitoring of the dam and the correct interpretation of possible deformations. In this study, large dams in our country and monitoring studies carried out by using geodetic and geotechnical methods in these dams are given in detail.

Enhancing Large Dam Safety Using IoT Technologies: A Case of a Smart Dam

This paper investigates how to transform a manual monitoring system into a smart environment using IoT technologies. Large dams are of huge importance as water reservoirs and potential energy sources primarily because of their use for electric energy generation. Monitoring and predicting dams' behavior are quite challenging. Due to a variety of limitations, environmental obstacles and dam system complexity, the application of RFID, Bluetooth, and IoT technologies has proven to be an adequate, affordable and reliable dam safety solution. This paper introduces a comprehensive model of dam safety management and monitoring based on pervasive technologies. The model described in this work has been applied within the large dam "Iron Gate 1", on the river Danube in the Republic of Serbia. The results show that the use of new technologies in the dam monitoring process can reduce human error and improve overall process.

DAMSAT: An operational system for tailings dam monitoring by bringing together remote sensing, meteorological and on-site observations with site metadata

<p>Tailings dams and storage facilities store toxic mine waste and effluent. Failure of a tailings storage facility can cause dramatic local ecosystem damage, water contamination and, if a tailings dam fails, loss of life due to inundation of the downstream area. The failure rate of tailings dams is known to be significantly greater than that of conventional water retention dams, but monitoring all tailings dams and storage facilities through frequent site visits could be an expensive and resource-demanding task.</p><p>Monitoring tools based on remote sensing and internet of things (IoT) sensors have the potential to reduce the risk from tailings storage failures by enabling the organisations responsible to conduct some monitoring remotely, and hence direct their resources for detailed monitoring more efficiently.</p><p>We present an overview of DAMSAT (Dam Monitoring from SATellites), an operational tool for monitoring tailings dams, tailings deposit areas and water dams. The tool consists of several different modules. Radar and optical satellite remote sensing data, and in situ internet of things (IoT) sensors are used to monitor surface movement and indicators of pollution at tailings storage sites. Meteorological forecasts are coupled to hydrological models in order to forecast changes in water level at the dams. DAMSAT presents the monitoring information together with risk information from hazard, consequence and evacuation models of possible dam failures in one integrated platform. The project is a partnership between UK and Peruvian organisations. This approach, alongside proactive user engagement activities and user requirements analysis, is designed to ensure that the system is developed with the needs of the user community in mind.</p>