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Chongshi Gu
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China

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Journal article
Published: 23 August 2021 in IEEE Access
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Deformation is the most intuitive indicator of the actual working status of a concrete dam. Zoning the variation regulation of dam deformation is one of the key parts of dam safety evaluation and risk assessment. However, the sample points reflecting deformation and variation characteristic information are non-uniformly distributed, thus it is difficult to cluster the data samples by traditional clustering methods. To solve this problem, a spatio-temporal zoning method of dam deformation considering non-uniform distribution of monitoring information is proposed. Firstly, the preprocessed deformation data are utilized to establish the similarity-distance zoning indicators using the absolute deformation, the deformation increase and the relative deformation increase respectively; then the deformation data are transferred into the Cartesian coordinate system, known as sample points. Secondly, utilize the improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster the points. The clustering parameters $M$ and $\delta $ are determined by an optimization algorithm with an evaluation index as the objective function, then the sample points representing time sections or spatial monitoring points are clustered through dynamically updating the neighborhood radius value $\varepsilon $ . Moreover, several artificial data sets are selected to demonstrate that the improved DBSCAN algorithm is with more obvious superiority in non-uniform clustering compared to traditional algorithms. Deformation data of an existing concrete dam are presented and discussed to validate the established zoning method.

ACS Style

Jiayi Wang; Hao Gu; Bo Chen; Chongshi Gu; Qinuo Zhang; Zikang Xing. A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information. IEEE Access 2021, 9, 117615 -117628.

AMA Style

Jiayi Wang, Hao Gu, Bo Chen, Chongshi Gu, Qinuo Zhang, Zikang Xing. A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information. IEEE Access. 2021; 9 ():117615-117628.

Chicago/Turabian Style

Jiayi Wang; Hao Gu; Bo Chen; Chongshi Gu; Qinuo Zhang; Zikang Xing. 2021. "A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information." IEEE Access 9, no. : 117615-117628.

Journal article
Published: 02 July 2021 in Mathematical Problems in Engineering
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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.

ACS Style

Wei Wei; Chongshi Gu; Xiao Fu. Processing Method of Missing Data in Dam Safety Monitoring. Mathematical Problems in Engineering 2021, 2021, 1 -12.

AMA Style

Wei Wei, Chongshi Gu, Xiao Fu. Processing Method of Missing Data in Dam Safety Monitoring. Mathematical Problems in Engineering. 2021; 2021 ():1-12.

Chicago/Turabian Style

Wei Wei; Chongshi Gu; Xiao Fu. 2021. "Processing Method of Missing Data in Dam Safety Monitoring." Mathematical Problems in Engineering 2021, no. : 1-12.

Research article
Published: 24 February 2021 in Complexity
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A dam is a complex and important water-retaining structure. Once the dam is broken, the flood will cause immeasurable damage to the lives and properties of the downstream people, so it is particularly important to have the dam risk management. Since the dam-break flood is a severe-consequence low-frequency event, the corresponding fatalities caused by it are difficult to estimate due to the lack of relevant data and poor data continuity. This paper analyzes the direct and indirect factors affecting the risk of life loss in dam failures and studies the characteristics, distribution rules, and membership functions of each factor. An adaptive differential evolution method is constructed through an optimization of the mutation factors and cross factors of the differential evolution method. This proposed evaluation method also combines with the fuzzy clustering iterative method that is capable of evaluating the similarity of life loss in dam accidents. The effectiveness of the proposed method is verified by 16 dam-break case studies.

ACS Style

Yantao Zhu; Xinqiang Niu; Chongshi Gu; Bo Dai; Lixian Huang. A Fuzzy Clustering Logic Life Loss Risk Evaluation Model for Dam-Break Floods. Complexity 2021, 2021, 1 -15.

AMA Style

Yantao Zhu, Xinqiang Niu, Chongshi Gu, Bo Dai, Lixian Huang. A Fuzzy Clustering Logic Life Loss Risk Evaluation Model for Dam-Break Floods. Complexity. 2021; 2021 ():1-15.

Chicago/Turabian Style

Yantao Zhu; Xinqiang Niu; Chongshi Gu; Bo Dai; Lixian Huang. 2021. "A Fuzzy Clustering Logic Life Loss Risk Evaluation Model for Dam-Break Floods." Complexity 2021, no. : 1-15.

Journal article
Published: 02 February 2021 in IEEE Access
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Seepage monitoring is a vital task in the risk management of concrete dams. Considering the lag effect of input factors, this paper presents a novel seepage monitoring model for concrete dams and proposes an effective identification method of lag process. Firstly, extreme gradient boosting (XGBoost) were adopted to predict the dam seepage. Hybridizing grey wolf optimization (HGWO) which integrates differential evolution (DE) into grey wolf optimization (GWO) and five–fold cross validation were utilized to optimize the hyper–parameters of XGBoost. Secondly, under the same search range and four evaluation indicators, the models optimized respectively by HGWO and three other algorithms were compared to confirm the global optimization capability of HGWO. Six state–of–art methods were also introduced to verify the effectiveness and feasibility of the proposed model. Then, based on the computation method of factor importance in decision tree models, we evaluated the relative importance of each component in the proposed model. Finally, according to the factor importance, the lag process of upstream water level and rainfall was identified, meanwhile a new equivalent water level calculation method is proposed. Monitoring data from three piezometric tubes on a concrete dam were taken as the experimental object. The results show that the improved HGWO has stronger global optimization ability, and the HGWO–XGBoost model achieves satisfactory prediction for seepage in concrete dams. Compared with the traditional trial–and–error method, the lag process computation method proposed in this paper provides a better recognition effect, which is of great value to the seepage monitoring and control of concrete dams.

ACS Style

Kang Zhang; Chongshi Gu; Yantao Zhu; Siyu Chen; Bo Dai; Yangtao Li; Xiaosong Shu. A Novel Seepage Behavior Prediction and Lag Process Identification Method for Concrete Dams Using HGWO-XGBoost Model. IEEE Access 2021, 9, 23311 -23325.

AMA Style

Kang Zhang, Chongshi Gu, Yantao Zhu, Siyu Chen, Bo Dai, Yangtao Li, Xiaosong Shu. A Novel Seepage Behavior Prediction and Lag Process Identification Method for Concrete Dams Using HGWO-XGBoost Model. IEEE Access. 2021; 9 ():23311-23325.

Chicago/Turabian Style

Kang Zhang; Chongshi Gu; Yantao Zhu; Siyu Chen; Bo Dai; Yangtao Li; Xiaosong Shu. 2021. "A Novel Seepage Behavior Prediction and Lag Process Identification Method for Concrete Dams Using HGWO-XGBoost Model." IEEE Access 9, no. : 23311-23325.

Journal article
Published: 31 October 2020 in Applied Mathematical Modelling
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Majority of the existing dam deformation monitoring models focus on the prediction of individual displacement, and ignore the spatial correlation of data. In this study, we propose a method dealing with multi-target prediction called the Maximum Correlated Stacking of Single-Target. The proposed method can provide reliable predictions of multi-target simultaneously, while fully exploiting the internal relationships between target variables via the strategy of targets stacking. Moreover, it can be coupled with different existing baseline models for the prediction and anomaly detection of arch dam deformation. Jinping–I arch dam is taken as a case study, where the monitoring displacement of 23 different points are analyzed and modeled simultaneously. Three kernel-based machine learning algorithms (i.e., support vector machine, relevance vector machine, and kernel extreme learning machine) and the partial least squares regression are adopted as baseline models for multi-target regression methods. Compared with the single-target regression and two state-of-the-art multi-target regression methods, the simulated results reveal the higher accuracy of the proposed method. Furthermore, model performance is validated in terms of anomaly detection capability, where two progressive anomalous scenarios (i.e., anomalies of single or multiple points) are investigated. The proposed method can be adapted for the health monitoring of other infrastructures in which multiple responses (e.g., displacement, temperature, or stress) need to be predicted simultaneously.

ACS Style

Siyu Chen; Chongshi Gu; Chaoning Lin; Mohammad Amin Hariri-Ardebili. Prediction of arch dam deformation via correlated multi-target stacking. Applied Mathematical Modelling 2020, 91, 1175 -1193.

AMA Style

Siyu Chen, Chongshi Gu, Chaoning Lin, Mohammad Amin Hariri-Ardebili. Prediction of arch dam deformation via correlated multi-target stacking. Applied Mathematical Modelling. 2020; 91 ():1175-1193.

Chicago/Turabian Style

Siyu Chen; Chongshi Gu; Chaoning Lin; Mohammad Amin Hariri-Ardebili. 2020. "Prediction of arch dam deformation via correlated multi-target stacking." Applied Mathematical Modelling 91, no. : 1175-1193.

Journal article
Published: 08 October 2020 in IEEE Access
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Dams are the main water retaining structures in the hydraulic engineering field. Safe operations of dams are important foundations to ensure the hydraulic functionalities of these engineering structures. Deformation, as the most intuitive feature of the damsb’ operation behaviors, can comprehensively reflect the dam structural states. In this case, the analysis of the dam prototype deformation data and the establishment of a real-time prediction model become frontier research contents in the field of dam safety monitoring. Considering the multi-nonlinear relationships between dam deformation and relative influential factors as well as the time lag effect of these influential factors, this paper adopts long-short-term memory (LSTM) network algorithm in deep learning to deal with the long-term dependence existing in dam deformation and explore the deformation law. The method proposed in this work can effectively avoid the gradient disappearance and gradient explosion problems by using the recurrent neural network (RNN). In addition, this work adopts the Attention mechanism to screen the information that has significant influence on deformation, combining the Adam optimization algorithm that has high calculation efficiency and low memory requirement to improves the learning accuracy and speed of the LSTM. The model overfitting is avoided by applying the Dropout mechanism. The effectiveness of this proposed model in studing the long time series deformation prediction of concrete dams is confirmed by case studies, whose MSE (mean square error) and other 4 error indexes can be reduced.

ACS Style

Dashan Yang; Chongshi Gu; Yantao Zhu; Bo Dai; Kang Zhang; Zhiduan Zhang; Bo Li. A Concrete Dam Deformation Prediction Method Based on LSTM With Attention Mechanism. IEEE Access 2020, 8, 185177 -185186.

AMA Style

Dashan Yang, Chongshi Gu, Yantao Zhu, Bo Dai, Kang Zhang, Zhiduan Zhang, Bo Li. A Concrete Dam Deformation Prediction Method Based on LSTM With Attention Mechanism. IEEE Access. 2020; 8 (99):185177-185186.

Chicago/Turabian Style

Dashan Yang; Chongshi Gu; Yantao Zhu; Bo Dai; Kang Zhang; Zhiduan Zhang; Bo Li. 2020. "A Concrete Dam Deformation Prediction Method Based on LSTM With Attention Mechanism." IEEE Access 8, no. 99: 185177-185186.

Research article
Published: 29 September 2020 in Advances in Civil Engineering
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The physical and mechanical parameters of hydraulic structures in complicated operating conditions often change over time. Updating these parameters in a timely manner is important to comprehend the operating behaviors and monitor the safety of hydraulic structures. Conventional inverse analysis methods can only generate inversions on the comprehensive deformation modulus of concrete dam structures, which contradict practical conditions. Based on the researches on conventional reversion methods of the deformation modulus of the dam body, foundation, and reservoir basin, the objective fitness function is established in this paper according to engineering-measured data and finite element simulation results. The quantum genetic algorithm has high global search efficiency and population diversity. A mechanical parameter inversion of high-arch dams is built from the intelligent optimization of an established algorithm by applying the quantum genetic algorithm. The proposed algorithm is tested to be feasible and valid for practical engineering projects and therefore shows scientific and practical application values.

ACS Style

Yantao Zhu; Xinqiang Niu; Jimin Wang; Chongshi Gu; Erfeng Zhao; Lixian Huang; Claudio Mazzotti. Inverse Analysis of the Partitioning Deformation Modulusof High-Arch Dams Based on Quantum Genetic Algorithm. Advances in Civil Engineering 2020, 2020, 1 -12.

AMA Style

Yantao Zhu, Xinqiang Niu, Jimin Wang, Chongshi Gu, Erfeng Zhao, Lixian Huang, Claudio Mazzotti. Inverse Analysis of the Partitioning Deformation Modulusof High-Arch Dams Based on Quantum Genetic Algorithm. Advances in Civil Engineering. 2020; 2020 ():1-12.

Chicago/Turabian Style

Yantao Zhu; Xinqiang Niu; Jimin Wang; Chongshi Gu; Erfeng Zhao; Lixian Huang; Claudio Mazzotti. 2020. "Inverse Analysis of the Partitioning Deformation Modulusof High-Arch Dams Based on Quantum Genetic Algorithm." Advances in Civil Engineering 2020, no. : 1-12.

Journal article
Published: 17 August 2020 in Applied Sciences
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Accurate and reliable prediction of dam deformation (DD) is of great significance to the safe and stable operation of dams. In order to deal with the fluctuation characteristics in DD for more accurate prediction results, a new hybrid model based on a decomposition-ensemble model named VMD-SE-ER-PACF-ELM is proposed. First, the time series data are decomposed into subsequences with different frequencies and an error sequence (ER) by variational mode decomposition (VMD), and then the secondary decomposition method is introduced into the prediction of ER. In these two decomposition processes, the sample entropy (SE) method is innovatively utilized to determine the decomposition modulus. Then, the input variables of the subsequences are selected by partial autocorrelation analysis (PACF). Finally, the parameter-optimization-based extreme learning machine (ELM) models are used to predict the subsequences, and the outputs are reconstructed to obtain the final prediction results. The case analysis shows that the VMD-SE-ER-PACF-ELM model has strong prediction ability for DD. The model is then compared with other nonlinear and time series models, and its performance under different prediction periods is also analyzed. The results show that the proposed model is able to adequately describe the original DD. It performs well in both training and testing stages. It is a preferred data-driven model for DD prediction and can provide a priori knowledge for health monitoring of dams.

ACS Style

Enhua Cao; Tengfei Bao; Chongshi Gu; Hui Li; Yongtao Liu; Shaopei Hu. A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation. Applied Sciences 2020, 10, 5700 .

AMA Style

Enhua Cao, Tengfei Bao, Chongshi Gu, Hui Li, Yongtao Liu, Shaopei Hu. A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation. Applied Sciences. 2020; 10 (16):5700.

Chicago/Turabian Style

Enhua Cao; Tengfei Bao; Chongshi Gu; Hui Li; Yongtao Liu; Shaopei Hu. 2020. "A Novel Hybrid Decomposition—Ensemble Prediction Model for Dam Deformation." Applied Sciences 10, no. 16: 5700.

Journal article
Published: 07 August 2020 in IEEE Access
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In most studies of dam’s displacement prediction based on monitoring data, emphasis was given on improving the prediction accuracy, while the model stability was merely considered. This study proposed a numerical-statistical combined model which aims to improve the model stability. The displacement was modelled within three modules: recoverable displacement (i.e., displacement induced by the external load including the water pressure and temperature), non-recoverable displacement (i.e., displacement due to the inherent variations of the materials such as the creep and fatigue of the concrete), and measurement errors (i.e., instrument error and human error). To reduce the random errors and increase the model stability, we used the numerical simulation to constrain the coefficients of explanatory variables for the recoverable displacement. The non-recoverable displacement was estimated by empirical equations, and the measurement errors were given by Gaussian distributions. The randomness of coefficients in the model among all monitoring points are constrained further by random coefficient model. We adopted the root mean square error (RMSE) at varying time and the change ratio of the coefficients (CRC) to evaluate the model stability. Results indicated that the proposed model not only has better prediction accuracy but also has better model stability compared with the statistical model and coordinates-included statistical model proposed in previous studies.

ACS Style

Yating Hu; Chongshi Gu; Zhenzhu Meng; Chenfei Shao. Improve the Model Stability of Dam’s Displacement Prediction Using a Numerical-Statistical Combined Model. IEEE Access 2020, 8, 147482 -147493.

AMA Style

Yating Hu, Chongshi Gu, Zhenzhu Meng, Chenfei Shao. Improve the Model Stability of Dam’s Displacement Prediction Using a Numerical-Statistical Combined Model. IEEE Access. 2020; 8 (99):147482-147493.

Chicago/Turabian Style

Yating Hu; Chongshi Gu; Zhenzhu Meng; Chenfei Shao. 2020. "Improve the Model Stability of Dam’s Displacement Prediction Using a Numerical-Statistical Combined Model." IEEE Access 8, no. 99: 147482-147493.

Journal article
Published: 18 July 2020 in Measurement
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The magnitude of leakage in the dam body and its foundation can be used as an important indicator in dam risk management. This study presents a data mining and monitoring framework for safety control of the dam leakage flow. First, the influencing factors in dam leakage flow are investigated. Second, a kernel extreme learning machine (KELM) is trained to predict dam leakage, where the parameters are optimized adaptively by parallel multi-population Jaya algorithm. Finally, a novel global sensitivity analysis is proposed to evaluate the relative importance of each input variable based on the KELM. Monitoring data of leakage flow from the concrete face rockfill dam in a pumped-storage power station is used as a vehicle for post-possessing. The simulated results of the case study reveal that KELM achieves a satisfactory prediction of the leakage flow. It is found that the water level fluctuation and rainfall have a significant impact on leakage magnitude. The sensitivity analysis provides a useful qualitative metric of dam leakage, which is of great value for dam safety monitoring and operation.

ACS Style

Siyu Chen; Chongshi Gu; Chaoning Lin; Yao Wang; Mohammad Amin Hariri-Ardebili. Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine. Measurement 2020, 166, 108161 .

AMA Style

Siyu Chen, Chongshi Gu, Chaoning Lin, Yao Wang, Mohammad Amin Hariri-Ardebili. Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine. Measurement. 2020; 166 ():108161.

Chicago/Turabian Style

Siyu Chen; Chongshi Gu; Chaoning Lin; Yao Wang; Mohammad Amin Hariri-Ardebili. 2020. "Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine." Measurement 166, no. : 108161.

Research article
Published: 01 July 2020 in Structural Control and Health Monitoring
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The mathematical monitoring model‐based interpretation of recorded quantities, especially of displacements, is essential for the structural health diagnosis of concrete dams. In practice, dam displacements are frequently interpreted and predicted by the hydraulic, seasonal, and time model, which considers the thermal deformation effect of a dam body by the periodic harmonic factor. The main purpose of this paper is to replace this factor with the measured temperatures of a dam body. This approach is conducted by performing a time series shape feature‐based spatial clustering method for the temperature field of a dam body in the first step. The principal components are then extracted from each cluster and used as the temperature factors in the monitoring model. An engineering example of the Jinping‐I arch dam demonstrates the good performance of the proposed clustering method and established monitoring models. By comparing the shape feature clustering‐based temperature principal component factor with the periodic harmonic factor, it can be concluded that the proposed models can describe the thermal deformation effect of concrete dams more reasonably: in the presented case study, especially for the dam parts of one half of the dam height above the foundation plane.

ACS Style

ShaoWei Wang; Cong Xu; Chongshi Gu; Huaizhi Su; Kun Hu; Qun Xia. Displacement monitoring model of concrete dams using the shape feature clustering‐based temperature principal component factor. Structural Control and Health Monitoring 2020, 27, 1 .

AMA Style

ShaoWei Wang, Cong Xu, Chongshi Gu, Huaizhi Su, Kun Hu, Qun Xia. Displacement monitoring model of concrete dams using the shape feature clustering‐based temperature principal component factor. Structural Control and Health Monitoring. 2020; 27 (10):1.

Chicago/Turabian Style

ShaoWei Wang; Cong Xu; Chongshi Gu; Huaizhi Su; Kun Hu; Qun Xia. 2020. "Displacement monitoring model of concrete dams using the shape feature clustering‐based temperature principal component factor." Structural Control and Health Monitoring 27, no. 10: 1.

Journal article
Published: 05 May 2020 in Mathematical Problems in Engineering
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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.

ACS Style

Zhongwen Shi; Chongshi Gu; Erfeng Zhao; Bo Xu. A Novel Seepage Safety Monitoring Model of CFRD with Slab Cracks Using Monitoring Data. Mathematical Problems in Engineering 2020, 2020, 1 -13.

AMA Style

Zhongwen Shi, Chongshi Gu, Erfeng Zhao, Bo Xu. A Novel Seepage Safety Monitoring Model of CFRD with Slab Cracks Using Monitoring Data. Mathematical Problems in Engineering. 2020; 2020 ():1-13.

Chicago/Turabian Style

Zhongwen Shi; Chongshi Gu; Erfeng Zhao; Bo Xu. 2020. "A Novel Seepage Safety Monitoring Model of CFRD with Slab Cracks Using Monitoring Data." Mathematical Problems in Engineering 2020, no. : 1-13.

Journal article
Published: 27 April 2020 in Mathematical Problems in Engineering
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The dam reliability study is essential for dam operation safety, regarding the complexity in dam failure causes. The assessment of the dam reliability is now mainly probabilistic or nonprobabilistic. The probabilistic method is usually applied to the cases with sufficient knowledge on dam parameters, while the nonprobabilistic method is suitable for the cases with insufficient knowledge on dam parameters. Since a dam can contain multiple parameters, information abundancy can vary among those parameters, and neither the probabilistic method nor the nonprobabilistic method alone is enough for dam reliability assessment. In this paper, the probabilistic method and nonprobabilistic method are modified based on the adjusted first-order second-moment method and the interval analysis method to suit the dam reliability assessment. Based on characterization on these two methods and the research of the fusion method, the secondary performance function of the dam is constructed, and the construction method of the risk assessment model for dam is proposed. Combined with a case study, this paper contributes to the safe operation of the dam.

ACS Style

Yantao Zhu; Xinqiang Niu; Jimin Wang; Chongshi Gu; Qiang Sun; Bo Li; Lixian Huang. A Risk Assessment Model for Dam Combining the Probabilistic and the Nonprobabilistic Methods. Mathematical Problems in Engineering 2020, 2020, 1 -12.

AMA Style

Yantao Zhu, Xinqiang Niu, Jimin Wang, Chongshi Gu, Qiang Sun, Bo Li, Lixian Huang. A Risk Assessment Model for Dam Combining the Probabilistic and the Nonprobabilistic Methods. Mathematical Problems in Engineering. 2020; 2020 ():1-12.

Chicago/Turabian Style

Yantao Zhu; Xinqiang Niu; Jimin Wang; Chongshi Gu; Qiang Sun; Bo Li; Lixian Huang. 2020. "A Risk Assessment Model for Dam Combining the Probabilistic and the Nonprobabilistic Methods." Mathematical Problems in Engineering 2020, no. : 1-12.

Research article
Published: 22 April 2020 in Advances in Civil Engineering
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Both numerical simulations and data-driven methods have been applied in dam’s displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and temporal correlations among all monitoring points into account, we took the first step toward integrating the finite element method into a data-driven model. As the data-driven method, we selected the random coefficient model, which can make each explanatory variable coefficient of all monitoring points following one or several normal distributions. In this way, explanatory variables are constrained. Another contribution of the proposed model is that the actual elastic modulus at each monitoring point can be back-calculated. Moreover, with a Lagrange polynomial interpolation, we can obtain the distribution field of elastic modulus, rather than gaining one value for the whole dam in previous studies. The proposed model was validated by a case study of the concrete arch dam in Jinping-I hydropower station. It has a better prediction precision than the random coefficient model without the finite element method.

ACS Style

Chenfei Shao; Chongshi Gu; Zhenzhu Meng; Yating Hu. Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction. Advances in Civil Engineering 2020, 2020, 1 -16.

AMA Style

Chenfei Shao, Chongshi Gu, Zhenzhu Meng, Yating Hu. Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction. Advances in Civil Engineering. 2020; 2020 ():1-16.

Chicago/Turabian Style

Chenfei Shao; Chongshi Gu; Zhenzhu Meng; Yating Hu. 2020. "Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction." Advances in Civil Engineering 2020, no. : 1-16.

Journal article
Published: 19 April 2020 in Construction and Building Materials
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Complicated fracture modes of fully-graded concrete are investigated at mesoscale with the proposed numerical method in this study. A generation method for two kinds of meso-structures, which gets the randomness of the appearance and distribution of aggregates involved, is proposed based on the mesoscopic features of fully-graded concrete. Cohesive elements are globally inserted to the mesoscale models to capture the fracture propagation with batch embedding technique, and the description and visualization of main cracking mode of fractures are realized based on relative energy proportions. Uniaxial compressive and tensile experiments are conducted with 2D and 3D models to validate the proposed method for simulating fracturing behavior, and the numerical outputs show high consistency with the experimental results and demonstrate that the proposed method is feasible to characterize the fracturing features of fully-graded concrete at mesoscale eventually.

ACS Style

Xiangnan Qin; Chongshi Gu; Chenfei Shao; Xiao Fu; Luis Vallejo; Yue Chen. Numerical analysis of fracturing behavior in fully-graded concrete with oversized aggregates from mesoscopic perspective. Construction and Building Materials 2020, 253, 119184 .

AMA Style

Xiangnan Qin, Chongshi Gu, Chenfei Shao, Xiao Fu, Luis Vallejo, Yue Chen. Numerical analysis of fracturing behavior in fully-graded concrete with oversized aggregates from mesoscopic perspective. Construction and Building Materials. 2020; 253 ():119184.

Chicago/Turabian Style

Xiangnan Qin; Chongshi Gu; Chenfei Shao; Xiao Fu; Luis Vallejo; Yue Chen. 2020. "Numerical analysis of fracturing behavior in fully-graded concrete with oversized aggregates from mesoscopic perspective." Construction and Building Materials 253, no. : 119184.

Case history
Published: 15 April 2020 in Bulletin of Engineering Geology and the Environment
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Deformation characteristics of rockfill and stress condition of slabs play important roles in the safety of concrete face rockfill dam (CFRD). In this study, an analysis based on monitored data and numerical simulation is conducted to evaluate the performance condition of Langyashan CFRD, which is built on a wide valley and has a mansard dam axis. The observational rockfill settlement is analyzed firstly, then a numerical model is established in accordance with the practical engineering. The numerical results of the rockfill settlement exhibit favorable consistency with similar projects or measured data in both construction and operation stages. Furthermore, the deflections and stress states of the face slabs are analyzed, and the effect of rockfill deformation and temperature variations are recognized as the main influence factors. The potential area of the face slabs vulnerable to cracks is predicted based on the stress state. The numerical results provide a reference for safety monitoring and inspection in the future operation.

ACS Style

Xiangnan Qin; Chongshi Gu; Chenfei Shao; Yue Chen; Luis Vallejo; Erfeng Zhao. Safety evaluation with observational data and numerical analysis of Langyashan reinforced concrete face rockfill dam. Bulletin of Engineering Geology and the Environment 2020, 79, 3497 -3515.

AMA Style

Xiangnan Qin, Chongshi Gu, Chenfei Shao, Yue Chen, Luis Vallejo, Erfeng Zhao. Safety evaluation with observational data and numerical analysis of Langyashan reinforced concrete face rockfill dam. Bulletin of Engineering Geology and the Environment. 2020; 79 (7):3497-3515.

Chicago/Turabian Style

Xiangnan Qin; Chongshi Gu; Chenfei Shao; Yue Chen; Luis Vallejo; Erfeng Zhao. 2020. "Safety evaluation with observational data and numerical analysis of Langyashan reinforced concrete face rockfill dam." Bulletin of Engineering Geology and the Environment 79, no. 7: 3497-3515.

Journal article
Published: 13 April 2020 in Mathematical Problems in Engineering
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A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

ACS Style

Yijun Chen; Chongshi Gu; Chenfei Shao; Hao Gu; Dongjian Zheng; Zhongru Wu; Xiao Fu. An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction. Mathematical Problems in Engineering 2020, 2020, 1 -23.

AMA Style

Yijun Chen, Chongshi Gu, Chenfei Shao, Hao Gu, Dongjian Zheng, Zhongru Wu, Xiao Fu. An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction. Mathematical Problems in Engineering. 2020; 2020 ():1-23.

Chicago/Turabian Style

Yijun Chen; Chongshi Gu; Chenfei Shao; Hao Gu; Dongjian Zheng; Zhongru Wu; Xiao Fu. 2020. "An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction." Mathematical Problems in Engineering 2020, no. : 1-23.

Journal article
Published: 25 February 2020 in International Journal of Environmental Research and Public Health
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Dams are important water-resisting structures prone to failure, causing huge economic and environmental losses. Traditionally, a dam failure is identified using the failure mode and effect analysis. This approach analyzes both the dam failure path (the specific effect chain of the failure mode) and the damage degree, by identifying and sorting the severity caused by the dam failure path. However, this analysis can be misleading since the relationship among the failure paths is not considered. To account for this, the DEMATEL method is used to modify the evaluation result of the severity of the failure consequence, caused by the dam failure path. Based on the fuzzy mathematics and VIKOR method, a dam failure path identification method is established, and then the dam failure paths are identified and sorted for a case study: gravity dam located at the junction of Yibin County (China). According to results, the two top initial failure paths were insufficient design of upstream anti-seepage (R6) or defective water-tight screen and corrosion (R7).

ACS Style

Yantao Zhu; Xinqiang Niu; Chongshi Gu; Dashan Yang; Qiang Sun; E. Fernandez Rodriguez. Using the DEMATEL-VIKOR Method in Dam Failure Path Identification. International Journal of Environmental Research and Public Health 2020, 17, 1480 .

AMA Style

Yantao Zhu, Xinqiang Niu, Chongshi Gu, Dashan Yang, Qiang Sun, E. Fernandez Rodriguez. Using the DEMATEL-VIKOR Method in Dam Failure Path Identification. International Journal of Environmental Research and Public Health. 2020; 17 (5):1480.

Chicago/Turabian Style

Yantao Zhu; Xinqiang Niu; Chongshi Gu; Dashan Yang; Qiang Sun; E. Fernandez Rodriguez. 2020. "Using the DEMATEL-VIKOR Method in Dam Failure Path Identification." International Journal of Environmental Research and Public Health 17, no. 5: 1480.

Original article
Published: 11 January 2020 in Engineering with Computers
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The observation data of dam displacement can reflect the dam’s actual service behavior intuitively. Therefore, the establishment of a precise data-driven model to realize accurate and reliable safety monitoring of dam deformation is necessary. This study proposes a novel probabilistic prediction approach for concrete dam displacement based on optimized relevance vector machine (ORVM). A practical optimization framework for parameters estimation using the parallel Jaya algorithm (PJA) is developed, and various simple kernel/multi-kernel functions of relevance vector machine (RVM) are tested to obtain the optimal selection. The proposed model is tested on radial displacement measurements of a concrete arch dam to mine the effect of hydrostatic, seasonal and irreversible time components on dam deformation. Four algorithms, including support vector regression (SVR), radial basis function neural network (RBF-NN), extreme learning machine (ELM) and the HST-based multiple linear regression (HST-MLR), are used for comparison with the ORVM model. The simulation results demonstrate that the proposed multi-kernel ORVM model has the best performance for predicting the displacement out of range of the used measurements dataset. Meanwhile, the ORVM model has the advantages of probabilistic output and can provide reasonable confidence interval (CI) for dam safety monitoring. This study lays the foundation for the application of RVM in the field of dam health monitoring.

ACS Style

Siyu Chen; Chongshi Gu; Chaoning Lin; Kang Zhang; Yantao Zhu. Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement. Engineering with Computers 2020, 1 -17.

AMA Style

Siyu Chen, Chongshi Gu, Chaoning Lin, Kang Zhang, Yantao Zhu. Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement. Engineering with Computers. 2020; ():1-17.

Chicago/Turabian Style

Siyu Chen; Chongshi Gu; Chaoning Lin; Kang Zhang; Yantao Zhu. 2020. "Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement." Engineering with Computers , no. : 1-17.

Journal article
Published: 02 January 2020 in International Journal of Environmental Research and Public Health
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As an important feature, deformation analysis is of great significance to ensure the safety and stability of arch dam operation. In this paper, Jinping-I arch dam with a height of 305 m, which is the highest dam in the world, is taken as the research object. The deformation data representation method is analyzed, and the processing method of deformation spatiotemporal data is discussed. A deformation hybrid model is established, in which the hydraulic component is calculated by the finite element method, and other components are still calculated by the statistical model method. Since the relationship among the measuring points is not taken into account and the overall situation cannot be fully reflected in the hybrid model, a spatiotemporal hybrid model is proposed. The measured values and coordinates of all the typical points with pendulums of the arch dam are included in one spatiotemporal hybrid model, which is feasible, convenient, and accurate. The model can predict the deformation of any position on the arch dam. This is of great significance for real-time monitoring of deformation and stability of Jinping-I arch dam and ensuring its operation safety.

ACS Style

Chongshi Gu; Xiao Fu; Chenfei Shao; Zhongwen Shi; Huaizhi Su. Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study. International Journal of Environmental Research and Public Health 2020, 17, 319 .

AMA Style

Chongshi Gu, Xiao Fu, Chenfei Shao, Zhongwen Shi, Huaizhi Su. Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study. International Journal of Environmental Research and Public Health. 2020; 17 (1):319.

Chicago/Turabian Style

Chongshi Gu; Xiao Fu; Chenfei Shao; Zhongwen Shi; Huaizhi Su. 2020. "Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study." International Journal of Environmental Research and Public Health 17, no. 1: 319.