Article
Engineering, Multidisciplinary
Gaochao Li, Lin Cheng, Anan Zhang, Jie Yang, Feihu Wang, Chunhui Ma
Summary: This paper investigates the application of distributed optical fiber strain sensing technology in arch dam deformation monitoring. By combining BO-LSTM with optical fiber monitoring, a three-dimensional model is established to achieve the transformation between strain and displacement. The results show that the model can accurately realize the strain-displacement transformation of arch dams.
Article
Engineering, Mechanical
Bo Xu, Zeyuan Chen, Xuan Wang, Jingwu Bu, Zhenhao Zhu, Hu Zhang, Shida Wang, Junyi Lu
Summary: This study proposes a comprehensive prediction model for the displacement of concrete arch dams considering signal residual correction. By integrating cluster analysis, LSTM, CEEMDAN, LSSVM, and PSO methods, valuable information is effectively extracted and utilized, providing technical support and new research perspectives for the health monitoring and operational management of concrete arch dams.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Bo Liu, Bowen Wei, Huokun Li, Ying Mao
Summary: Most roller compacted concrete (RCC) dams face the issue of poor cementation quality at the construction interface. To tackle the limitations of conventional models, a multipoint hybrid model considering the construction interface and its seepage is proposed for monitoring the displacement health of RCC arch dams. This model, called HIST-M, improves spatial interpretation and prediction capabilities by introducing spatial coordinates based on the hydrostatic-seasonal-time (HST) model. Coupled with the analysis model of seepage field and stress field using the finite element method, the HIST-M model takes into account the influence of construction interface and seepage. The proposed model shows remarkable improvement in long-term prediction abilities and prediction precision compared to commonly used models.
APPLIED MATHEMATICAL MODELLING
(2022)
Article
Engineering, Multidisciplinary
Chongshi Gu, Mingyuan Zhu, Yan Wu, Bo Chen, Fuqiang Zhou, Weinan Chen
Summary: Constructing an accurate dam displacement health monitoring (DHM) model is crucial to ensure the safety of the dam. However, previous studies on DHM focused on the analysis and prediction of a single measurement point, with little work on multiple measurement points, which leads to low efficiency in evaluating the overall status of dams. To address these issues, the HTcT model is proposed based on full consideration of extreme climate and engineering measures in cold regions, and the multi-output least-square support vector regression (MOLSSVR) is introduced to forecast multiple measurement points simultaneously. The proposed model outperforms popular machine learning models and provides an accurate and efficient approach for dam displacement safety monitoring in severely cold regions.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Civil
Erfeng Zhao, Chengqing Wu
Summary: The study evaluates the performance of arch dams through modeling on defined centroid deformations, identifying the space-time distribution characteristics of deformation and developing a monitoring model. A novel prediction model is introduced to balance empirical risk and generalization ability, providing technical support for long-term operation of arch dams.
ENGINEERING STRUCTURES
(2021)
Article
Engineering, Civil
Dongyang Yuan, Chongshi Gu, Xiangnan Qin, Chenfei Shao, Jing He
Summary: A new measured air temperature-based displacement health monitoring model for super high arch dams is proposed in this study, utilizing twin support vector regression and optimized using grey wolf optimizer algorithm. Experimental results demonstrate that the new model can better capture the thermal displacement variations, significantly improving predictive accuracy and showing excellent long-term predictive capability.
ENGINEERING STRUCTURES
(2022)
Article
Engineering, Civil
Mingyuan Zhu, Bo Chen, Chongshi Gu, Yan Wu, Weinan Chen
Summary: Establishing a reasonable displacement health monitoring model is crucial for ensuring the safety of super high arch dams. This study proposes a multi-output least square support vector regression model capable of evaluating and predicting multiple monitoring points. The optimal parameters of the model are determined using particle swarm optimization. The introduction of kernel principal component algorithm helps extract the main temperature components accurately and overcome multicollinearity issues. Testing with real data shows that the proposed model outperforms other models and has outstanding medium and long-term predictive capacity.
ENGINEERING STRUCTURES
(2022)
Article
Environmental Sciences
Junqiang Han, Rui Tu, Xiaochun Lu, Lihong Fan, Wenquan Zhuang, Weisheng Wang, Feng Zhao, Bayin Dalai, Gulayozov Majid Shonazarovich, Mustafo Safarov
Summary: The Sarez Dam, the highest natural dam in the world, is economically significant and requires reliable monitoring of potential deformations. This study used the Beidou high-precision deformation monitoring system to track the dam's deformations. The results revealed displacement deformations, with horizontal deformation towards the lake center and vertical deformation showing subsidence. Earthquakes were found to significantly impact the dam's deformation.
Article
Energy & Fuels
John K. Thomas, Hancy Rohan Crasta, K. Kausthubha, Chavan Gowda, Ashwath Rao
Summary: This paper introduces a method using LSTM technology to predict battery life, which can measure the battery life by monitoring battery voltage, load voltage, temperature, and charge-discharge cycles.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Computer Science, Artificial Intelligence
Qiubing Ren, Heng Li, Mingchao Li, Juntao Zhang, Ting Kong
Summary: This paper presents an online monitoring model for dam displacement behavior that can continuously track the behavior and improve prediction performance through multi-scale residual error correction. The proposed model outperforms benchmark models in displacement prediction and accurately tracks the dynamic variations of displacement data.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Multidisciplinary
Qiubing Ren, Mingchao Li, Shuo Bai, Yang Shen
Summary: This study proposed a dam multiple-point displacement monitoring model based on the SVR algorithm, which can analyze and predict displacements at multiple measurement points simultaneously. By introducing weight vectors to separate common and individual information and considering spatiotemporal correlations, the CMOSVR-based model showed better monitoring performance and higher adaptability to various scenarios compared to conventional single-point monitoring models.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Article
Construction & Building Technology
Qiubing Ren, Mingchao Li, Ting Kong, Jie Ma
Summary: This paper presents an online model based on sequential learning for real-time monitoring of dam displacement behavior. The proposed model effectively captures the complex nonlinear mapping from environmental variables to displacements through sequential learning and data fusion techniques. The verification using real monitoring data shows that the proposed model can achieve satisfactory prediction accuracy with low computational cost.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Public, Environmental & Occupational Health
Patrick Mbullo Owuor, Diana Ross Awuor, Emily Mwende Ngave, Sera L. Young
Summary: This study explored the experiences and expectations of displaced and nondisplaced women in Makueni County, Kenya, where a dam construction project is being carried out. The study found that both displaced and nondisplaced women experienced negative impacts in economic, health, social, and environmental domains. However, the displaced women perceived worse outcomes in terms of economic and social consequences. Older and married women in both groups had the worst experiences and negative perceptions about the social wellbeing consequences.
SOCIAL SCIENCE & MEDICINE
(2023)
Article
Engineering, Civil
Enhua Cao, Tengfei Bao, Hui Li, Xiang Xie, Rongyao Yuan, Shaopei Hu, Wenjun Wang
Summary: Developing a deformation prediction model based on monitoring data is crucial for establishing a safety monitoring system for super high arch dams. This study proposes a hybrid deep learning model for deformation prediction and a feature selection method. The results show that the model has the best performance and applicability in different zones and operating conditions, providing reliable a priori knowledge for the construction of safety monitoring system for super high arch dams.
KSCE JOURNAL OF CIVIL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Bo Liu, Huokun Li, Gang Wang, Wei Huang, Pengzhen Wu, Yuekang Li
Summary: Vibration-based dam safety monitoring methods have become crucial for assessing dam safety. This study developed a dynamic material parameter inversion framework for arch dams using modal parameters and deep learning. A determined-order stochastic subspace identification method was proposed to effectively identify the modal parameters. The sensitivity of the dynamic elastic modulus (DEM) in different regions to the modal parameters was analyzed, and a Bayesian optimized multi-output long short-term memory neural network was used to establish a nonlinear mapping relationship between the DEM and modal parameters. The proposed method was proven effective and accurate for high arch dams, advancing the application of deep learning technology in hydraulic engineering.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Multidisciplinary
Hao Ding, Jin-Ting Wang, Li-Qiao Lu, Jian-Wen Pan
Summary: This article investigates the optimization of the toroidal tuned liquid column damper (TTLCD) for suppressing harmonic vibration. The closed-form solution for the TTLCD-structure system is derived, and based on this solution, optimization is carried out to obtain design tables with optimum parameters. The study also performs a parametric investigation of key parameters affecting the damping properties of the TTLCD-structure system, revealing how to produce optimal damping effects. Additionally, a design example of the TTLCD is illustrated and compared to a traditional TLCD.
ENGINEERING OPTIMIZATION
(2022)
Article
Engineering, Mechanical
Guang-Heng Luo, Jian-Wen Pan, Jin-Ting Wang, Feng Jin
Summary: The improved SASW method, based on theoretical derivation, efficiently processes data for Rayleigh waves to obtain travel time in 3-D Rayleigh wave tomography. This method demonstrates good discriminating ability in quantitatively detecting voids of different locations, depths, and sizes.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Civil
Meng-Zhong Zhang, Lei Zhang, Xiang-Chao Wang, Wei Su, Yi-Xiang Qiu, Jin-Ting Wang, Chu-Han Zhang
Summary: This paper proposes a practical framework for the seismic analysis of dams based on a source-to-structure simulation. The framework combines physics-based numerical simulations to generate site-specific ground motions and finite element methods to simulate the dam's dynamic response. The study demonstrates the utility of the proposed simulation framework for accurately predicting seismic responses in dams.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2023)
Article
Engineering, Civil
Hao Ding, Okyay Altay, Jin-Ting Wang, Anupam Das, Tian-Yu Zhou
Summary: This article analyzes the dynamic response of toroidal-tuned liquid column dampers (TTLCDs) under unidirectional and bidirectional excitation scenarios. The results reveal that the liquid responses of TTLCDs excited unidirectionally provide a good prediction for those excited bidirectionally, and the bidirectional response of TTLCDs can be simplified as the linear superposition of the response in two principal directions.
JOURNAL OF EARTHQUAKE ENGINEERING
(2023)
Article
Engineering, Civil
Xiang-Chao Wang, Jin-Ting Wang
Summary: The traditional spectral matching methods fail to provide ground motions with site-related physical backgrounds. This study introduces a physics-based spectral matching (PBSM) method to generate fully site-related broadband ground motions that are compatible to the target spectrum. The method constructs a 3D numerical model and uses adjoint simulations to calculate strain Green's tensors, reducing the variable space dimension and allowing for optimization algorithms to find the physics-based and spectrum-matched ground motions. The method has been applied to the Xiluodu dam in China, showing promising results.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2023)
Article
Engineering, Mechanical
Hao Ding, Tian-Yu Zhou, Jin-Ting Wang, Okyay Altay, Jian Zhang
Summary: In this study, a new method is proposed to harvest energy from mechanical vibrations of structures by combining the tuned liquid column damper (TLCD) and the Savonius type hydrokinetic turbine (STHT) in a green power production way. The hydrokinetic energy is converted into electrical energy during the vibration control process. Shaking table tests are performed to investigate the starting performance and energy conversion efficiency, and an optimized circuit configuration is revealed for power maximization. The feasibility and characteristics of the approach in energy harvesting are demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Marine
Hao Ding, Wei Wang, Jun-Feng Liu, Jin-Ting Wang, Zhi-Ji Le, Jian Zhang, Guang-Ming Yu
Summary: This study experimentally investigates the impact of size effects of the toroidal tuned liquid column damper (TTLCD) on control efficiency using the real-time hybrid simulation technique. The results show that the effects of experimental scale on control efficiency and liquid response are insignificant, and a larger length ratio leads to stronger performance. The TTLCD is found to be an efficient additional damping device in monopile wind turbines.
Article
Chemistry, Physical
Huite Wu, Jianwen Pan, Jinting Wang
Summary: This paper proposes a new composite C-S-Hs model to more realistically represent the layered feature of C-S-H. The larger interlayer space in the model allows for sufficient water adsorption and arrangement of C-S-H cells. The random arrangement of multiple C-S-H cells and additional water molecule adsorption significantly affect the composite C-S-Hs system. Uniaxial tension tests with different strain rates show that the mechanical properties of the composite C-S-Hs model are closer to those of cementitious materials on the macroscopic scale. The simulation results highlight the impact of strain rate and interlayer spacing on the characteristics of the multiple C-S-Hs model.
CHEMICAL PHYSICS LETTERS
(2023)
Article
Engineering, Civil
Hao Ding, Okyay Altay, Jin-Ting Wang
Summary: This study investigates the efficiency of toroidal tuned liquid column dampers (TTLCDs) in controlling the lateral vibration of monopile supported offshore wind turbines. The TTLCDs are capable of simultaneously matching the natural frequencies of the wind turbines in both fore-aft and side-side directions. A mathematical model of the TTLCDs inside the nacelle is derived and a simulation interface between MATLAB/Simulink and FAST is established. A design procedure for TTLCDs in monopile wind turbines is provided. Numerical calculations are performed considering different wind velocities, wave-wind misalignment angles and working conditions, taking into account fatigue and ultimate load cases. A parametric analysis is conducted on the flow resistance coefficient of TTLCDs. The results demonstrate that TTLCDs can effectively control lateral vibrations and improve the structural performance of monopile wind turbines against wind and wave loads.
ENGINEERING STRUCTURES
(2023)
Article
Engineering, Multidisciplinary
Guang-Heng Luo, Jin-Ting Wang, Jian-Wen Pan
Summary: This study aims to apply the surface wave transmission (SWT) method, previously used for concrete crack detection, to concrete crack monitoring by proposing a U-shaped sensor array configuration with multi-input and multi-output properties. The scale of conventional SWT is extended from 1-D to 2-D based on the proposed sensor array, facilitating broad utilization in concrete crack monitoring. Simulations are carried out to analyze the feasibility of SWT in concrete crack monitoring by investigating the influence of different signal-to-noise ratios and heterogeneous concrete properties. The results demonstrate the potential of the proposed sensor array in concrete crack monitoring and its potential application in practical engineering.
Article
Engineering, Multidisciplinary
Changwei Liu, Jianwen Pan, Jinting Wang
Summary: An LSTM-based anomaly detection model is proposed in this paper for the deformation of arch dams. By combining real-time deformation prediction and control limit determination, the model can accurately predict displacement changes of the dam and send alarms in case of abnormal conditions.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Multidisciplinary Sciences
Xiang-Chao Wang, Jin-Ting Wang, Chu-Han Zhang
Summary: This study introduces a full-scenario analysis method for evaluating the maximum credible earthquake hazard for a specific fault by considering all uncertainties of potential future earthquakes. The method is applied in seismic hazard analysis at the Xiluodu dam in China, showing potential application value in earthquake engineering.
NATURE COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jianwen Pan, Wenju Liu, Changwei Liu, Jinting Wang
Summary: This paper proposes a spatiotemporal deformation field behavior model based on a convolutional neural network (CNN) for arch dams. The model incorporates climatological data, water pressure, and constraint fields to accurately predict deformation. Compared to traditional hydraulic-seasonal-time (HST) and finite element models, the CNN-based model achieves higher prediction accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
