Research

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30 pages, 11810 KiB

Open AccessArticle

A Modeling of Human Reliability Analysis on Dam Failure Caused by Extreme Weather

by Huiwen Wang, Dandan Li, Taozhen Sheng, Jinbao Sheng, Peiran Jing and Dawei Zhang

Appl. Sci. 2023, 13(23), 12968; https://doi.org/10.3390/app132312968 - 04 Dec 2023

Viewed by 842

Human factors are introduced into the dam risk analysis method to improve the existing dam risk management theory. This study constructs the path of human factor failure in dam collapse, explores the failure pattern of each node, and obtains the performance shaping factors [...] Read more.

Human factors are introduced into the dam risk analysis method to improve the existing dam risk management theory. This study constructs the path of human factor failure in dam collapse, explores the failure pattern of each node, and obtains the performance shaping factors (PSFs) therein. The resulting model was combined with a Bayesian network, and sensitivity analysis was performed using entropy reduction. The study obtained a human factor failure pathway consisting of four components: monitoring and awareness, state diagnosis, plan formulation and operation execution. Additionally, a PSFs set contains five factors: operator, technology, organization, environment, and task. Operator factors in a BN (Bayesian network) are the most sensitive, while the deeper causes are failures in organizational and managerial factors. The results show that the model can depict the relationship between the factors, explicitly measure the failure probability quantitatively, and identify the causes of high impact for risk control. Governments should improve the significance of the human factor in the dam project, constantly strengthen the safety culture of the organization’s communications, and enhance the psychological quality and professional skills of management personnel through training. This study provides valuable guidelines for the human reliability analysis on dam failure, which has implications for the theoretical research and engineering practice of reservoir dam safety and management. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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12 pages, 1380 KiB

Open AccessArticle

Multi-Point Deformation Prediction Model for Concrete Dams Based on Spatial Feature Vector

by Zhuoxun Chen and Xiaosheng Liu

Appl. Sci. 2023, 13(20), 11212; https://doi.org/10.3390/app132011212 - 12 Oct 2023

Viewed by 641

Abstract

Deformation can effectively reflect the structural state of concrete dams and, thus, establishing na accurate concrete dam deformation prediction model is important for dam health monitoring and early warning strategies. To address the problem that the spatial coordinates introduced in the traditional multi-point [...] Read more.

Deformation can effectively reflect the structural state of concrete dams and, thus, establishing na accurate concrete dam deformation prediction model is important for dam health monitoring and early warning strategies. To address the problem that the spatial coordinates introduced in the traditional multi-point deformation prediction model of dams not being able to accurately and efficiently reflect the spatial correlation of multiple-measuring points, a 2D-1D-CNN model is proposed which expresses the spatial correlation between each measuring point through spatial feature vectors, replacing the spatial coordinates in the traditional multi-point model. First, the spatial feature vector is extracted from the historical spatio-temporal panel series of deformation values of measuring points via a Two-Dimensional Convolutional Neural Network (2D-CNN); second, the vector is combined with the environmental impact factor of dam deformation to form the final input factor of fused spatial features; and, thirdly, this vector is combined with the environmental impact factors of dam deformation to form the final input factor of fused spatial features, and the non-linear linkage between the factors and the measured displacement values is constructed by the efficient feature processing capability of a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the prediction results. Finally, the actual monitoring data of a concrete dam in China are used as an example to verify the validity of the model. The results show that the proposed model outperforms the other models in most cases, respectively, which verifies the effectiveness of the proposed model in this paper. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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24 pages, 5469 KiB

Open AccessArticle

Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods

by Chuan Lin, Yun Zou, Xiaohe Lai, Xiangyu Wang and Yan Su

Appl. Sci. 2023, 13(19), 10827; https://doi.org/10.3390/app131910827 - 29 Sep 2023

Viewed by 562

Abstract

The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction [...] Read more.

The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam deformation in the short term can better interpret the dam’s structural health status. Aiming to capture the short-term variation trends of dam deformation, a multi-step displacement prediction model of concrete dams is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the k-harmonic means (KHM) algorithm, and the error minimized extreme learning machine (EM-ELM) algorithm. The model can be divided into three stages: (1) The CEEMDAN algorithm is adopted to decompose dam displacement series into different signals according to their timing characteristics. Moreover, the sample entropy (SE) method is used to remove the noise contained in the decomposed signals. (2) The KHM clustering algorithm is employed to cluster the denoised data with similar characteristics. Furthermore, the sparrow search algorithm (SSA) is utilized to optimize the KHM algorithm to avoid the local optimal problem. (3) A multi-step prediction model to capture the short-term variation of dam displacement is established based on the clustered data. Engineering examples show that the model has good prediction performance and strong robustness, demonstrating the feasibility of applying the proposed model to the multi-step forecasting of dam displacement. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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15 pages, 26440 KiB

Open AccessArticle

Concrete Dam Deformation Prediction Model Research Based on SSA–LSTM

by Jiedeerbieke Madiniyeti, Yang Chao, Tongchun Li, Huijun Qi and Fei Wang

Appl. Sci. 2023, 13(13), 7375; https://doi.org/10.3390/app13137375 - 21 Jun 2023

Cited by 4 | Viewed by 908

Abstract

In the context of dam deformation monitoring, the prediction task is essentially a time series prediction problem that involves non-stationarity and complex influencing factors. To enhance the accuracy of predictions and address the challenges posed by high randomness and parameter selection in LSTM [...] Read more.

In the context of dam deformation monitoring, the prediction task is essentially a time series prediction problem that involves non-stationarity and complex influencing factors. To enhance the accuracy of predictions and address the challenges posed by high randomness and parameter selection in LSTM models, a novel approach called sparrow search algorithm–long short-term memory (SSA–LSTM) has been proposed for predicting the deformation of concrete dams. SSA–LSTM combines the SSA optimization algorithm with LSTM to automatically optimize the model’s parameters, thereby enhancing the prediction performance. Firstly, a concrete dam was used as an example to preprocess the historical monitoring data by cleaning, normalizing, and denoising, and due to the specificity of the data structure, multi-level denoising of abnormal data was performed. Second, some of the data were used to train the model, and the hyperparameters of the long and short-term memory neural network model (LSTM) were optimized by the SSA algorithm to better match the input data with the network structure. Finally, high-precision prediction of concrete dam deformation was carried out. The proposed model in this study significantly improves the prediction accuracy in dam deformation forecasting and demonstrates effectiveness in long-term time series deformation prediction. The model provides a reliable and efficient approach for evaluating the long-term stability of dam structures, offering valuable insights for engineering practices and decision-making. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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22 pages, 14748 KiB

Open AccessArticle

A Partitioned Rigid-Element and Interface-Element Method for Rock-Slope-Stability Analysis

by Taozhen Sheng, Tongchun Li, Xiaoqing Liu and Huijun Qi

Appl. Sci. 2023, 13(12), 7301; https://doi.org/10.3390/app13127301 - 19 Jun 2023

Cited by 1 | Viewed by 895

Abstract

The stability analysis of rock slopes has been a prominent topic in the field of rock mechanics, primarily due to the widespread occurrence of discontinuous structural planes in rock masses. Based on this complex characteristic of rock slopes, this paper proposes a novel [...] Read more.

The stability analysis of rock slopes has been a prominent topic in the field of rock mechanics, primarily due to the widespread occurrence of discontinuous structural planes in rock masses. Based on this complex characteristic of rock slopes, this paper proposes a novel numerical method, the Partitioned-Rigid-Element and Interface-Element (PRE-IE) method. In the PRE-IE method, the structure is modeled as several rigid bodies and discontinuous structural planes, which are, respectively, divided into partitioned rigid elements and interface elements. Taking the contact force of node pairs and the displacement of the rigid body centroid as mixed variables, according to the principle of minimum potential energy, the governing equations of PRE-IE can be established using the Lagrange multiplier method and then solved using the nonlinear contact iterative method and the incremental method. A classic case study demonstrates that using the failure of all contact node pairs as the criterion for slope failure is appropriate. This criterion is objective and avoids the potential impact of personal bias on safety factor calculations. Two numerical examples of differently shaped slopes are provided to verify the correctness and validity of the PRE-IE method. By comparing the safety factor calculated using the PRE-IE method with those obtained from other different methods, as well as comparing the computational time, it is shown that the PRE-IE method, in combination with the SRM, can accurately and efficiently analyze the stability problems of rock slopes. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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23 pages, 11205 KiB

Open AccessArticle

An Adaptive Degree of Freedom Condensation Algorithm for Simulating Transient Temperature, Applied to an Asphalt-Concrete Core Wall

by Li Yuan, Tongchun Li, Hongen Li, Fang Wang and Huijun Qi

Appl. Sci. 2023, 13(3), 1456; https://doi.org/10.3390/app13031456 - 22 Jan 2023

Cited by 1 | Viewed by 925

Abstract

To solve the problem of the high cost of transient temperature simulation in the whole construction process of an asphalt-concrete core wall, a novel adaptive degree of freedom condensation algorithm for simulating transient temperature is proposed. This method establishes the judgment criterion of [...] Read more.

To solve the problem of the high cost of transient temperature simulation in the whole construction process of an asphalt-concrete core wall, a novel adaptive degree of freedom condensation algorithm for simulating transient temperature is proposed. This method establishes the judgment criterion of degree of freedom condensation based on the error estimator of mesh and the artificial energy added by degree of freedom condensation. In this method, the transformation matrix between the master and slave degrees of freedom is constructed based on the shape function interpolation relationship between the initial coarse mesh and the multi-level refined mesh. In the transient calculation process, this method can automatically identify the positions where temperature distribution and value are stable and condense the considered slave degrees of freedom to master degrees of freedom through the transformation matrix at any time to reduce the unnecessary degrees of freedom. In this paper, three numerical examples show that the proposed method can effectively reduce the cost of matrix factorization and the solving the equation in the finite element method at the cost of small precision loss in the long-term transient temperature simulation. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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12 pages, 588 KiB

Open AccessArticle

Assessment of Carbon Emissions at the Logistics and Transportation Stage of Prefabricated Buildings

by Yichen Zhang, Tian Peng, Chao Yuan and Yang Ping

Appl. Sci. 2023, 13(1), 552; https://doi.org/10.3390/app13010552 - 30 Dec 2022

Cited by 6 | Viewed by 2248

Abstract

As regards the carbon emission levels of the logistics stage of prefabricated buildings, this study aims to fill the gap in scientific and unified carbon emission calculation models and standards. Thus, the calculation boundary for carbon emissions was first defined in this study. [...] Read more.

As regards the carbon emission levels of the logistics stage of prefabricated buildings, this study aims to fill the gap in scientific and unified carbon emission calculation models and standards. Thus, the calculation boundary for carbon emissions was first defined in this study. Secondly, various carbon emission factors related to China’s energy consumption characteristics were summarized. Subsequently, a carbon emission calculation model for the logistics stage was established, based on the carbon emission factor method. Finally, taking a project as an example, the carbon emission level at the transportation stage was calculated using the proposed model. The effect of full-load rates on the carbon emission levels of prefabricated components was also evaluated. The results demonstrate that the full-load rate has a significant effect on carbon emissions during the transportation stage (54.32% reduction in carbon emissions at 100% full load). Therefore, increasing the full-load rate can reduce carbon emissions from transportation, as long as the loading requirements are met. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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18 pages, 3825 KiB

Open AccessArticle

Reliability Analysis of Concrete Gravity Dams Based on Least Squares Support Vector Machines with an Improved Particle Swarm Optimization Algorithm

by Shida Wang, Bo Xu, Zhenhao Zhu, Jing Li and Junyi Lu

Appl. Sci. 2022, 12(23), 12315; https://doi.org/10.3390/app122312315 - 01 Dec 2022

Cited by 4 | Viewed by 1258

Abstract

A reliability analysis method based on least squares support vector machines with an improved particle swarm optimization algorithm (IPSO-LSSVM) is proposed to calculate the reliability of concrete gravity dams when explicit nonlinear limit-state functions are difficult to obtain accurately. First, the main failure [...] Read more.

A reliability analysis method based on least squares support vector machines with an improved particle swarm optimization algorithm (IPSO-LSSVM) is proposed to calculate the reliability of concrete gravity dams when explicit nonlinear limit-state functions are difficult to obtain accurately. First, the main failure modes of concrete gravity dams and their influencing factors are determined. Second, Latin hypercube sampling is used to create samples. A finite element calculation batch program of concrete gravity dams is written to calculate the safety indexes of each sample. Third, based on the samples, the IPSO-LSSVM model is established to replace the finite element calculation. Finally, the failure probability of concrete gravity dams is obtained by using the Monte Carlo (MC) method. The case study for a typical concrete gravity dam in the Yunnan Province of China shows that the dam is reliable because the failure probability is 8.87 × 10⁻⁵. The proposed reliability analysis method is efficient and feasible for calculating the reliability of concrete gravity dams. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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25 pages, 8382 KiB

Open AccessArticle

Time Series Prediction of Dam Deformation Using a Hybrid STL–CNN–GRU Model Based on Sparrow Search Algorithm Optimization

by Chuan Lin, Kailiang Weng, Youlong Lin, Ting Zhang, Qiang He and Yan Su

Appl. Sci. 2022, 12(23), 11951; https://doi.org/10.3390/app122311951 - 23 Nov 2022

Cited by 6 | Viewed by 1467

Abstract

During its long service life, an arch dam affected by a combination of factors exhibits a typical time-varying characteristic in terms of its structure and material properties, and the deformation in the dam structure can directly and reliably reflect the health and service [...] Read more.

During its long service life, an arch dam affected by a combination of factors exhibits a typical time-varying characteristic in terms of its structure and material properties, and the deformation in the dam structure can directly and reliably reflect the health and service status of dams. Therefore, an accurate deformation prediction is an important part of dam safety monitoring. However, due to multiple factors, dam deformation data often tend to be highly volatile, and most existing deformation estimation techniques employ a single algorithm, which may not effectively capture the potential change process. A hybrid model for dam deformation prediction has been proposed to overcome this problem. First, dam deformation data are decomposed into three components by seasonal and trend decomposition using loess. Second, a convolutional neural network–gated recurrent unit (GRU) hybrid model, which optimizes hyperparameters using the sparrow search algorithm, is used to capture the nonlinear relationships that exist in each component. Finally, the final prediction result of dam deformation is the comprehensive output of multiple submodules. The deformation monitoring data (period: 2009–2019) of a parabolic variable-thickness double-curved arch dam located in China are considered as the survey target. The test results indicate that the proposed model is suitable for short-term and long-term prediction and outperforms other models in terms of higher robustness to abnormal sequences than other conventional models (R² differs by 5.50% and 7.87%, respectively, in short-term and long-term predictions for different measurement points, while other models differ by 9.78% to reach 15.71%, respectively). Among the models studied, the GRU shows better robustness to abnormal series than the LSTM with good prediction accuracy, fewer parameters, and a simpler structure. Hence, the GRU can be employed for dam deformation prediction in practical engineering. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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Review

Jump to: Research

17 pages, 1732 KiB

Open AccessReview

A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces

by Dong Chen, Ben Huang and Fei Kang

Appl. Sci. 2023, 13(6), 3564; https://doi.org/10.3390/app13063564 - 10 Mar 2023

Cited by 7 | Viewed by 3031

Abstract

Cracks seriously endanger the safe and stable operation of dams. It is important to detect surface cracks in a timely and accurate manner to ensure the safety and serviceability of a dam. The above-water crack detection technology of dams has been widely studied, [...] Read more.

Cracks seriously endanger the safe and stable operation of dams. It is important to detect surface cracks in a timely and accurate manner to ensure the safety and serviceability of a dam. The above-water crack detection technology of dams has been widely studied, but due to the complex underwater environment, above-water crack detection technology on dam surfaces cannot be directly applied to underwater crack detection. To adapt to the underwater detection environment and improve the efficiency and accuracy of underwater crack detection, many methods have been proposed for underwater crack detection, including sensor detection and image detection. This paper presents a systematic overview of the development and application practices of existing underwater crack detection technologies for concrete dams, focusing on methods that use underwater robots as underwater mobile carriers to acquire images that are combined with digital image processing algorithms to identify, locate, and quantify underwater cracks in dams. This method has been widely used for underwater crack detection on dam surfaces with the advantages of being non-contact, non-destructive, having high efficiency, and wide applicability. Finally, this paper looks further forward to the development trends and research challenges of detection technologies for underwater cracks on concrete dam surfaces, which will help researchers to complete further studies on underwater crack detection. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Concrete Dam)

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