scholarly journals Structural Safety Monitoring of High Arch Dam Using Improved ABC-BP Model

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yantao Zhu ◽  
Chongshi Gu ◽  
Erfeng Zhao ◽  
Jintao Song ◽  
Zhiyun Guo
Keyword(s):  
Temporal Distribution ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Arch Dam ◽  
Monitoring Data ◽  
Structural Safety ◽  
Local Optimum ◽  
High Arch Dam ◽  
Monitoring Method ◽  
Monitoring Model

The establishment of a structural safety monitoring model of a dam is necessary for the evaluation of the dam’s deformation status. The structural safety monitoring method based on the monitoring data is widely used in traditional research. On the basis of the analysis of the high arch dam’s deformation principles, this study proposes a structural safety monitoring method derived from the dam deformation monitoring data. The method first analyzes and establishes the spatial and temporal distribution of high arch dam’s safety monitoring, overcoming the standard artificial bee colony (ABC) algorithm’s shortcoming of easily falling into the local optimum by adopting the adaptive proportion and average Euclidean distance afterwards. The improved ABC algorithm is used to optimize the backpropagation (BP) neural network’s initial weight and threshold. The application example proves that ABC-BP model’s improvement method is important for the establishment of a high arch deformation safety monitoring model and can effectively improve the model’s fitting and forecasting ability. This method provides a reference for the establishment of a structural safety monitoring model of dam and provides guidance for the establishment of a forecasting model in other fields.

scholarly journals Outlier Detection Based on Multivariable Panel Data and K-Means Clustering for Dam Deformation Monitoring Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jintao Song ◽  
Shengfei Zhang ◽  
Fei Tong ◽  
Jie Yang ◽  
Zhiquan Zeng ◽  
...  
Keyword(s):  
Panel Data ◽  
Outlier Detection ◽  
Correlation Coefficients ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Structural Safety ◽  
Dam Safety ◽  
Dam Safety Monitoring ◽  
Dam Deformation

A dam is a super-structure widely used in water conservancy engineering fields, and its long-term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long-time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K-means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K-means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping-I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.

Ultrasonic Transceiver-Based Structural Deformation Monitoring with Location Ability

2014 ◽  
Vol 721 ◽  
pp. 442-445
Author(s):  
Wei Zheng ◽  
Chun Xian Wu ◽  
Rong Rong Cui
Keyword(s):  
Spatial Relations ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Structural Deformation ◽  
Monitoring Method ◽  
Regional Monitoring ◽  
Space Deformation ◽  
Structural Surface

Regional coverage monitoring for structural deformation remains a challenge for current technologies. A coverage regional monitoring method based on dual ultrasonic transceivers and exhibiting deformation location ability is presented. The spatial projecting model of dual ultrasonic beams is established to determine the monitoring scope of the structural surface in space. Deformation location principles are induced by analyzing the spatial relations of the monitoring data of dual ultrasonic transceivers. Finally, an experiment is proposed to illustrate the method.

Safety Monitoring Information System of Railway Tunnel Construction Based on GIS

2013 ◽  
Vol 303-306 ◽  
pp. 811-814 ◽  
Author(s):  
Ning Suo ◽  
Hui Lin Wang
Keyword(s):  
Data Analysis ◽  
Early Warning ◽  
Information Acquisition ◽  
Warning System ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Tunnel Construction ◽  
Railway Tunnel ◽  
Engineering Information

This paper puts forward the railway tunnel construction based on GIS for deformation monitoring data analysis as the foundation of railway tunnel construction safety monitoring and risk early warning system. Practice shows that the system in engineering information acquisition, construction deformation data analysis, early warning and monitoring data has obvious advantages. And it is still in help users to make decisions and plays an important role to ensure the safety of tunnel construction.

scholarly journals A Fractal Prediction Method for Safety Monitoring Deformation of Core Rockfill Dams

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liang Pei ◽  
Jiankang Chen ◽  
Jingren Zhou ◽  
Huibao Huang ◽  
Zhengjun Zhou ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Prediction Method ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Dimension Theory ◽  
Deformation Prediction ◽  
The Core ◽  
Dam Deformation ◽  
And Control

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.

scholarly journals A monitoring model for the stress on a super-high arch dam during pre-impoundment construction

2020 ◽  
Vol 20 (8) ◽  
pp. 3604-3614
Author(s):  
Jingtai Niu ◽  
Xiang Luo ◽  
Zhiping Deng ◽  
Yang Zhang ◽  
Yingjia Guo ◽  
...  
Keyword(s):  
Structural Characteristics ◽  
In Situ Stress ◽  
Arch Dam ◽  
Stress Monitoring ◽  
High Arch Dam ◽  
Factors Affecting ◽  
Arch Dams ◽  
Monitoring Model ◽  
Elastoplastic Constitutive Model ◽  
High Arch Dams

Abstract This paper presents a proposed model for monitoring the stress on a super-high arch dam during construction. Using mathematics, mechanics, and dam engineering principles, the mathematical expressions of the self-weight component of the dam prior to and following the sealing of the bottom of the arch were derived. The visco-elastoplastic constitutive model of dam concrete during construction was identified and used to develop a stress monitoring model for a super-high arch dam. Based on in-situ stress monitoring data collected during the construction of a super-high arch dam, the stress monitoring model was applied to a super-high arch dam accounting for future impoundment, and the key components of the monitoring model were isolated. The results show that the model has high fitting accuracy and incorporates an appropriate selection of factors affecting dam stress. The hydrograph of each component conforms to the structural characteristics of super-high arch dams during construction. This model overcomes the limitations of applying the complete self-weight of the dam body on the cantilever beam and was validated using data from a super-high arch dam construction project. Thus, this paper provides evidence for a safety monitoring model for super-high arch dams during construction.

scholarly journals Feedback Analysis and Prevention & Control Strategy of the Real Causes of the Gallery Cracks in Super-High Arch Dam

2021 ◽  
Author(s):  
Yaosheng Tan ◽  
Chunfeng Liu ◽  
Youzhi Liu ◽  
Jingtao Li
Keyword(s):  
Control Strategy ◽  
Safety Monitoring ◽  
Influential Factors ◽  
Arch Dam ◽  
High Arch Dam ◽  
Feedback Analysis ◽  
The Real ◽  
Actual Temperature ◽  
Cracking Mechanism ◽  
Cracking Process

Gallery cracks occur commonly in concrete dams, but their cracking mechanism has yet to be effectively revealed. In this paper, the actual temperature, stress change history and cracking process of a gallery area were uncovered, based on the safety monitoring data of cracks in a super-high arch dam. In addition, the basic development and change laws, as well as the corresponding cracking mechanism, were analyzed, and the real causes and influential factors of cracks at the site were revealed, which will provide a reference for the prevention of cracks in similar projects in the future.

scholarly journals A Novel Seepage Safety Monitoring Model of CFRD with Slab Cracks Using Monitoring Data

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhongwen Shi ◽  
Chongshi Gu ◽  
Erfeng Zhao ◽  
Bo Xu
Keyword(s):  
Early Stage ◽  
Cloud Model ◽  
Water Pressure ◽  
Influencing Factor ◽  
Safety Monitoring ◽  
Monitoring Data ◽  
Factors Affecting ◽  
Normal Distribution Curve ◽  
Monitoring Model ◽  
Influence Of Water

The traditional regression model usually simulates the influence of water pressure and rainfall in the early stage based on experience, but it is not suitable. To solve this problem, the normal distribution curve is used to simulate the lagging effect of water pressure and rainfall on dam seepage. In view of problem of slab cracks, the influence of cracks on seepage is analyzed. In this paper, a safety monitoring model for concrete face rockfill dam (CFRD) seepage with cracks considering the lagging effect is proposed, in which slab cracks are considered as an influencing factor. The radial basis function neural network (RBFNN) optimized by genetic algorithm (GA) is used to establish a safety monitoring model for a CFRD seepage. Seepage of the dam is predicted by this model, whose results are similar to the monitoring data, which indicates that the method has certain applicability. Through the analysis of the proportion of factors affecting CFRD seepage, it is found that the rainfall component has the greatest impact on the total seepage, accounting for more than 50%, and the crack component accounts for about 10%. Finally, through the cloud model, the monitoring index of CFRD seepage is worked out, which has certain guiding significance for the treatment of abnormal seepage monitoring data.

scholarly journals Coupled Thermo-Hydro-Mechanical Analysis of Valley Narrowing Deformation of High Arch Dam: A Case Study of the Xiluodu Project in China

2020 ◽  
Vol 10 (2) ◽  
pp. 524 ◽  
Author(s):  
Tao Yin ◽  
Qingbin Li ◽  
Yu Hu ◽  
Sanda Yu ◽  
Guohe Liang
Keyword(s):  
Coupling Effect ◽  
Mechanical Analysis ◽  
Arch Dam ◽  
Water Infiltration ◽  
Structural Safety ◽  
Deformation Pattern ◽  
Field Variation ◽  
High Arch Dam ◽  
Key Factor

General studies examining reservoir bank deformation during its impoundment primarily consider the coupling effect between the seepage field and the stress field, but thermal field variation in the bedrock and its effect are rarely considered. In this paper, a case study concerning a 285.5 m high arch dam project, where a valley narrowing deformation occurs after the initial impoundment, is implemented. An analysis of in situ measurement is given to interpret the causes of the unique hydro-thermal phenomenon of the project. Possible reasons for the valley narrowing deformation pattern are discussed. A numerical model based on the thermo-hydro-mechanical (THM) coupling theory of porous medium is used to calculate the evolution processes of the thermal, seepage, and stress fields of the area after impoundment of the reservoir. The simulated deformation trend and pattern of the river valley are consistent with the monitoring data. The results demonstrate that water infiltration after impounding cools the bedrock and the temperature decrease makes the bedrock contract, which induces the narrowing deformation of the valley. Factor analysis of the hydrothermal field shows that temperature variation is the main cause of long-term deformation. Thus, it shall be considered as a key factor in terms of structural safety assessment. Furthermore, sensitivity analysis of the hydraulic conductivities of rock strata suggests that future development of the deformation can be eased off if the anti-seepage method is adopted on the bedrock.

scholarly journals Spatial-Temporal Features Based Sensor Network Partition in Dam Safety Monitoring System

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2517
Author(s):  
Hao Chen ◽  
Yingchi Mao ◽  
Longbao Wang ◽  
Hai Qi
Keyword(s):  
Time Series ◽  
Sensor Network ◽  
Dynamic Change ◽  
Safety Monitoring ◽  
Arch Dam ◽  
Monitoring Data ◽  
Dam Safety ◽  
Network Partition ◽  
Temporal Features ◽  
Dam Safety Monitoring

Many various types of sensors have been installed to monitor the deformation and stress in the dam structure. It is difficult to directly evaluate the operation status of the dam structure based on the massive monitoring data. The sensor network is divided into multiple regions according to the design specifications, simulation data, and engineering experiences. The local results from sub-regions are integrated to achieve overall evaluation. However, it ignores the spatial distribution of sensors and the variation of time series, which cannot meet the real-time evaluation for the dam safety monitoring. If the network partitions can provide the preliminary foundation for analyzing the dynamic change laws of the dam’s working conditions in a real-way, we should consider the similarity of structure and stresses in the local region of the dam and the correlation among the monitoring data. A time-series denoising autoencoder (TSDA) is proposed to represent the spatial and temporal features of the nodes by compressing high-dimensional monitoring data. Then, a network partitioning algorithm (NPA) based on spatial-temporal features based on the TSDA is presented. The NPA ensures that the partition results can support the analysis of the physical change laws by introducing the auxiliary objective variable to optimize the network partition objective function. Experimental results on the public datasets and a real dataset from an arch dam demonstrate that the proposed network partition algorithm NPA can achieve better partition performance than TSDA+K-Means and TSDA+GMM. The NPA can improve the silhouette coefficient by 45.1% and 58.4% higher than the TSDA+K-Means and TSDA+GMM, respectively. The NPA can increase the Calinski-Harabaz Index by 30.8% and 61.6%, respectively.

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