scholarly journals A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xudong Qu ◽  
Jie Yang ◽  
Meng Chang
Keyword(s):  
Prediction Model ◽  
Evaluation System ◽  
Prediction Models ◽  
Evaluation Index ◽  
Accuracy Evaluation ◽  
Engineering Project ◽  
Deformation Prediction ◽  
Concrete Dam ◽  
Evaluation Indexes ◽  
Dam Deformation

Deformation is a comprehensive reflection of the structural state of a concrete dam, and research on prediction models for concrete dam deformation provides the basis for safety monitoring and early warning strategies. This paper focuses on practical problems such as multicollinearity among factors; the subjectivity of factor selection; robustness, externality, generalization, and integrity deficiencies; and the unsoundness of evaluation systems for prediction models. Based on rough set (RS) theory and a long short-term memory (LSTM) network, single-point and multipoint concrete dam deformation prediction models for health monitoring based on RS-LSTM are studied. Moreover, a new prediction model evaluation system is proposed, and the model accuracy, robustness, externality, and generalization are defined as quantitative evaluation indexes. An engineering project shows that the concrete dam deformation prediction models based on RS-LSTM can quantitatively obtain the representative factors that affect dam deformation and the importance of each factor relative to the effect. The accuracy evaluation index (AVI), robustness evaluation index (RVI), externality evaluation index (EVI), and generalization evaluation index (GVI) of the model are superior to the evaluation indexes of existing shallow neural network models and statistical models according to the new evaluation system, which can estimate the comprehensive performance of prediction models. The prediction model for concrete dam deformation based on RS-LSTM optimizes the factors that influence the model, quantitatively determines the importance of each factor, and provides high-performance, synchronous, and dynamic predictions for concrete dam behaviours; therefore, the model has strong engineering practicality.

Evaluation Indexes of Sand Liquefaction Analysed by Rough Set

2011 ◽  
Vol 243-249 ◽  
pp. 3087-3091
Author(s):  
Nian Ping Liu ◽  
Hong Tu Wang ◽  
Zhi Gang Yuan
Keyword(s):  
Rough Set ◽  
Groundwater Level ◽  
Analytical Method ◽  
Evaluation System ◽  
Evaluation Index ◽  
Determination Method ◽  
Sand Liquefaction ◽  
Evaluation Indexes ◽  
Result Show ◽  
The Evaluation System

Sand liquefaction is a problem of complex evolution of the disaster, there is no accurate way to judge at present, this study put forward an analytical method to improve and optimize the evaluation system of sand liquefaction based on rough set. The significance of indexes are confirmed by calculating rough dependability between indexes and result for appraisement, the result show that SPT blow count has the greatest impact on the evaluation system, the groundwater level has greater impact, followed by the sand depth, epicenteral distance and duration. The proposed approach overcame the subjectivity of traditional weight determination method, so it is more objective and accurate, and it is reasonable and effective to optimize the evaluation index of sand liquefaction.

Software Maintainability: Systematic Literature Review and Current Trends

2016 ◽  
Vol 26 (08) ◽  
pp. 1221-1253 ◽  
Author(s):  
Ruchika Malhotra ◽  
Anuradha Chug
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Evaluation System ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Design Metrics ◽  
Current Trends ◽  
Software Maintainability ◽  
Early Phases

Software maintenance is an expensive activity that consumes a major portion of the cost of the total project. Various activities carried out during maintenance include the addition of new features, deletion of obsolete code, correction of errors, etc. Software maintainability means the ease with which these operations can be carried out. If the maintainability can be measured in early phases of the software development, it helps in better planning and optimum resource utilization. Measurement of design properties such as coupling, cohesion, etc. in early phases of development often leads us to derive the corresponding maintainability with the help of prediction models. In this paper, we performed a systematic review of the existing studies related to software maintainability from January 1991 to October 2015. In total, 96 primary studies were identified out of which 47 studies were from journals, 36 from conference proceedings and 13 from others. All studies were compiled in structured form and analyzed through numerous perspectives such as the use of design metrics, prediction model, tools, data sources, prediction accuracy, etc. According to the review results, we found that the use of machine learning algorithms in predicting maintainability has increased since 2005. The use of evolutionary algorithms has also begun in related sub-fields since 2010. We have observed that design metrics is still the most favored option to capture the characteristics of any given software before deploying it further in prediction model for determining the corresponding software maintainability. A significant increase in the use of public dataset for making the prediction models has also been observed and in this regard two public datasets User Interface Management System (UIMS) and Quality Evaluation System (QUES) proposed by Li and Henry is quite popular among researchers. Although machine learning algorithms are still the most popular methods, however, we suggest that researchers working on software maintainability area should experiment on the use of open source datasets with hybrid algorithms. In this regard, more empirical studies are also required to be conducted on a large number of datasets so that a generalized theory could be made. The current paper will be beneficial for practitioners, researchers and developers as they can use these models and metrics for creating benchmark and standards. Findings of this extensive review would also be useful for novices in the field of software maintainability as it not only provides explicit definitions, but also lays a foundation for further research by providing a quick link to all important studies in the said field. Finally, this study also compiles current trends, emerging sub-fields and identifies various opportunities of future research in the field of software maintainability.

Arch Dam Deformation Prediction Model Based on PSO-SVM

2013 ◽  
Vol 351-352 ◽  
pp. 1306-1311 ◽  
Author(s):  
Jing Yang Liu ◽  
He Zhi Liu
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Small Sample ◽  
Arch Dam ◽  
Support Vector ◽  
Model Parameters ◽  
Deformation Prediction ◽  
Dam Foundation ◽  
Model Based ◽  
Dam Deformation

Arch dam has gradually evolved as one of dam type as main large-scale hydraulic project, dam deformation prediction is an important part of dam safety monitoring, and it is difficult to forecast because of the complicated nonlinear characteristics of the monitoring data. Support Vector Machine (SVM) could solve the small sample, nonlinear high dimension problem due to the excellent generalization ability, and hence it has been widely used in the forecast of arch dam deformation. However, the forecast results considerably depend on the choice of SVM model parameters. In this paper, Particle Swarm Optimization (PSO), which has the characteristic of fast global optimization, was applied to optimize the parameters in SVM, and then the dam deformation prediction model based on PSO-SVM could be established. The model is applied to a certain arch dam foundation prediction. The accuracy of this employed approach was examined by comparing it with multiple regression method. In a word, the experimental results indicate that the proposed method based on PSO-SVM can be used in arch dam deformation prediction.

Prediction Model of Concrete Dam Deformation Based on Adaptive Unscented Kalman Filter and BP Neural Network

2014 ◽  
Vol 513-517 ◽  
pp. 4076-4079 ◽  
Author(s):  
Liang Hui Li ◽  
Sheng Jun Peng ◽  
Zhen Xiang Jiang ◽  
Bo Wen Wei
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Unscented Kalman Filter ◽  
Safety Monitoring ◽  
Hydraulic Engineering ◽  
Concrete Dam ◽  
Adaptive Unscented Kalman Filter ◽  
Dam Deformation

By using unscented kalman filter (UKF) theory and introducing adaptive factor into BP neural network, a new prediction model of concrete dam deformation was proposed. Example shows that this model can improve the convergence speed of BP neural network, and the calculation precision of this model meets engineering requirements. Meanwhile, this model can be applied in the safety monitoring of other hydraulic engineering structure.

scholarly journals The Health Evaluation of Wetland Ecosystem in the Yangtze River Economic Zone Based on Remote Sensing——A Case Study of Yunnan Wetland

2021 ◽  
Vol 276 ◽  
pp. 01001
Author(s):  
Haonan Wang ◽  
Fan Yu ◽  
Jun Li
Keyword(s):  
Health Status ◽  
Evaluation System ◽  
Ecosystem Health ◽  
Comprehensive Evaluation ◽  
Rate Of Change ◽  
Evaluation Index ◽  
Health Evaluation ◽  
Wetland Ecosystem ◽  
Evaluation Indexes ◽  
The Status

In order to provide a scientific basis for the development of the Yunnan wetland ecosystem, we construct a wetland ecosystem health evaluation system with 15 evaluation indexes selected from three aspects including pressure, status and response based on PSR mathematical model. Analytic Hierarchy Process(AHP) was used to determine the weight of indicators and the Yunnan wetland ecosystem was divided into “the five health status” of “health, sub-health, fragility, illness, and scurviness”, which are used to analyze and evaluate the health status of it through a comprehensive evaluation index. The results show that the comprehensive evaluation index of ecosystem health status of Yunnan wetland is 0.5524 in 2017, locating in the grade of “fragility” and is close to the status of “sub-healthy”; Among the 3 levels of pressure, state, and response, the pressure and state are locating in the grade of “fragility” and the response is close to the status of “health”. Among the 15 evaluation indexes, the rate of change of wetland area, the area of water body, the hydrological regulation and the wetland management level are the most important factors affecting the ecosystem health of Yunnan wetland.

scholarly journals A Concrete Dam Deformation Prediction Method Based on LSTM With Attention Mechanism

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185177-185186
Author(s):  
Dashan Yang ◽  
Chongshi Gu ◽  
Yantao Zhu ◽  
Bo Dai ◽  
Kang Zhang ◽  
...  
Keyword(s):  
Prediction Method ◽  
Attention Mechanism ◽  
Deformation Prediction ◽  
Concrete Dam ◽  
Dam Deformation

The Evaluation Index System of China’s Energy Security

2013 ◽  
Vol 318 ◽  
pp. 375-378
Author(s):  
Ying Chuan Zhao
Keyword(s):  
Energy Security ◽  
Evaluation System ◽  
Supply And Demand ◽  
Reduction Rate ◽  
Energy System ◽  
Economic Security ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Evaluation Indexes ◽  
Five Elements

With the highlighting of the energy security problem, it is particular important to evaluate energy security. At first, this article introduces the conception of energy security, the factors and characteristics of energy system, and the idea of evaluation index. Then according to the framework of goals-elements-evaluation, design an evaluation index that is progressive deduction and three levels-four subsystems, five elements, and forty evaluation indexes. Four subsystems are coal system, the oil and gas system, the power system and the comprehensive evaluation system of energy. Five elements are balance of supply and demand, transportation capacity, mutation effect, economic security, ecological environment. Forty evaluation indexes are storage and mining ratio, transport capacity, impact factor of change suddenly, fluctuation rate of price, SO reduction rate and so on.

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