The monitoring of data anomaly identification is an important basis for dam safety online monitoring and evaluation. In this research, a cluster of anomaly identification models for dam safety ...monitoring data was constructed, and a three-stage online anomaly identification method was proposed to discriminate outliers. The proposed method combined anomaly detection for measured values based on a single-point time series simulation, measurement error reduction based on remote retesting and spatio-temporal analysis, and environmental response mutation recognition. It brought about efficient and accurate detection for data mutation and online classified identification for its inducement. Additionally, problems such as missing outliers, misjudging normal values induced by the environmental response, and difficulty in online identification for measurement errors were effectively solved. The research productions were applied to the online monitoring system for the safety risk of reservoirs and dams in the Dadu River Basin. The results showed that the proposed method could effectively improve the accuracy of anomaly identification and reduce the misjudgment and omission rate to less than 2%. It could also successfully recognize and subtract nonstructural anomalies such as accidental errors, instrument faults, and environmental responses online, which provided reliable data for online dam safety monitoring.
The computational cost of nonlinear numerical simulation of gravity dams brings resistance to the popularization of seismic risk analysis under the framework of Performance-based Earthquake ...Engineering. Insufficient seismic records and inappropriate parametric fragility models will mislead the assessment of structural performance and total risk. A computationally efficient methodology suitable for a large number of seismic waves is proposed based on screening for intensity measures and a surrogate model to obtain the non-parametric fragility curves. It specifically integrates the rigorous numerical technique of seismic damage to the dam-reservoir-foundation system, the comprehensive comparison of intensity measures, the surrogate model for classification of limit states, and the seismic risk analysis of gravity dams. A typical high gravity dam and a classical artificial neural network model are employed to illustrate the effectiveness of the proposed methodology through a step-by-step scheme. The result shows that the simulated non-parametric fragility curves are more competent to consider the uncertainty of ground motions. The obtained structural fragility and risk is more accurate than the conventional method.
•An non-parametric fragility model and matched scheme are proposed for gravity dams.•Risk analysis has been successfully performed under a large number of ground motions.•Conventional methods underrate the variability of records and lead to biased results.
This article presents GRAPE, a parallel GRAPh Engine for graph computations. GRAPE differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole, without ...the need for recasting the entire algorithm into a new model. Underlying GRAPE are a simple programming model and a principled approach based on fixpoint computation that starts with partial evaluation and uses an incremental function as the intermediate consequence operator. We show that users can devise existing sequential graph algorithms with minor additions, and GRAPE parallelizes the computation. Under a monotonic condition, the GRAPE parallelization guarantees to converge at correct answers as long as the sequential algorithms are correct. Moreover, we show that algorithms in MapReduce, BSP, and PRAM can be optimally simulated on GRAPE. In addition to the ease of programming, we experimentally verify that GRAPE achieves comparable performance to the state-of-the-art graph systems using real-life and synthetic graphs.
For an open-pit mine, the slope must remain stable throughout the life of mining operation, and it follows that an optimized ultimate pit limit (UPL) should have the slope stability commensurate with ...economic benefit. In the Shuguang gold and copper mine, a geostatistics-block-based method is used to characterize the heterogeneous mechanical properties of rock mass. Then, the detailed slope stability analyses for four possible slope configuration designs using heterogenous mechanical parameter block model are performed to determine the steepest safe slope angle, and the steepest safe slope angle is next used for the UPL optimization. Compared with the original UPL assuming the rock mass is homogeneous in the same lithology, the slope angle for the optimized UPL has an average 1° to 6° increase, and the optimized UPL can bring 15.84 million tons of ore and reduce 20.83 million tons of waste rock. The result indicated that the application of geostatistics can make practical use of geotechnical information to improve slope stability, and slope configurations, and thereby optimize the UPL and so bring economic benefit.
Weak planes affect the strength and deformational behaviors of rock slopes, and the anisotropic characteristics of rock mass should be considered in slope stability analysis. Effects of joint plane ...orientations on failure mechanism and strength response of inherently anisotropic rock samples were firstly investigated. The specimens with various orientations of joints were evaluated under uniaxial compression, Brazilian tensile, and direct shear tests. By treating the foliated rock as transversely isotropic materials, the relevant elastic constants and strength parameters were obtained from experimental results. The slope damage zone was then investigated using Comsol Multiphysics code based on Hoffman criterion. It is indicated that the failure mechanism and strength response depend highly on the inclination of specimens with respect to the loading direction. For disks with the same inclination angle, the value of tensile strength has an increasing trend with the total fracture length. Numerical results show that partial slope mass failed in single slope and no large-scale landslide occurred. The failure pattern in numerical results agrees well with the field observations. The cooperation between the experimental results and the numerical results allows an in-depth analysis of the experimental results and thus better understanding the dominant effect of joints on the deformation and failure of rock mass.
This article proposes an Adaptive Asynchronous Parallel (AAP) model for graph computations. As opposed to Bulk Synchronous Parallel (BSP) and Asynchronous Parallel (AP) models, AAP reduces both ...stragglers and stale computations by dynamically adjusting relative progress of workers. We show that BSP, AP, and Stale Synchronous Parallel model (SSP) are special cases of AAP. Better yet, AAP optimizes parallel processing by adaptively switching among these models at different stages of a single execution. Moreover, employing the programming model of GRAPE, AAP aims to parallelize existing sequential algorithms based on simultaneous fixpoint computation with partial and incremental evaluation. Under a monotone condition, AAP guarantees to converge at correct answers if the sequential algorithms are correct. Furthermore, we show that AAP can optimally simulate MapReduce, PRAM, BSP, AP, and SSP. Using real-life and synthetic graphs, we experimentally verify that AAP outperforms BSP, AP, and SSP for a variety of graph computations.
Rock landslides, one of the most common disasters in an open-pit mine, may occur at any point during the mining process. To manage landslide emergencies and define adequate preventive measures, a ...two-stage monitoring system was designed in this study to identify the potential landslide area and record multiple real-time data regarding an impending landslide. A case-based reasoning (CBR) approach was then designed to find similar rock landslide cases with detailed geological and monitoring information. The similar cases can provide workable engineering analogies for landslide prediction. Finally, an early warning system including slope stability analysis results, multifactor monitoring data, and early warning indicators obtained by CBR approach was established. Three warning levels were defined to support the system: ordinary “blue” level, attention “yellow” level, and alarm “red” level. A case study in the Dagushan open-pit mine indicates that the proposed system can provide ideas and solutions for rock landslide early warning in similar open-pit mines.
Deformation mechanism in the core rockfill dams with heavy load and high-stress level is difficult to predict and control, which is one of the key problems to be solved in the dam operation safety ...management and control. Aiming at the large error problems obtained by the parameter-based functional models (regression model, grey theory model, etc.) in the deformation prediction of the core rockfill dams, a fractal prediction method and its technical process by combining the variable dimension fractal dimension and the "metabolism" of prediction data are proposed through analyzing the fractal adaptability and deformation characteristics of original monitoring data based on the resealed-range (R/S) method and fractal dimension theory. It effectively solves the error in the process of constant dimension fractal accumulation and transformation greatly in dam deformation prediction and provides a new way for dam safety monitoring deformation prediction and early warning. The trend analysis of deformation monitoring data of the Pubugou core rockfill dam and the deformation prediction show that the fractal prediction information of dam deformation has a good corresponding relationship with its physical causes, which is in line with the actual deformation trend and operation state of the dam. Compared with the traditional stepwise regression method, the prediction results obtained by the proposed method in this paper are of high accuracy, implying that the improved fractal prediction of dam deformation is effective and the Hurst fractal index is applicable in the evaluation of the dam deformation trend.
Companies providing cloud-scale data services have increasing needs to store and analyze massive data sets, such as search logs, click streams, and web graph data. For cost and performance reasons, ...processing is typically done on large clusters of tens of thousands of commodity machines. Such massive data analysis on large clusters presents new opportunities and challenges for developing a highly scalable and efficient distributed computation system that is easy to program and supports complex system optimization to maximize performance and reliability. In this paper, we describe a distributed computation system, Structured Computations Optimized for Parallel Execution (
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), targeted for this type of massive data analysis.
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combines benefits from both traditional parallel databases and MapReduce execution engines to allow easy programmability and deliver massive scalability and high performance through advanced optimization. Similar to parallel databases, the system has a SQL-like declarative scripting language with no explicit parallelism, while being amenable to efficient parallel execution on large clusters. An optimizer is responsible for converting scripts into efficient execution plans for the distributed computation engine. A physical execution plan consists of a directed acyclic graph of vertices. Execution of the plan is orchestrated by a job manager that schedules execution on available machines and provides fault tolerance and recovery, much like MapReduce systems.
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is being used daily for a variety of data analysis and data mining applications over tens of thousands of machines at Microsoft, powering Bing, and other online services.
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