Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines

Extreme learning machine: Theory and applications

Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285 · see citing works

Extreme learning machines: a survey

Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,903 · see citing works

An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels

Huang, G.-B. (2014) · Cognitive Computation · doi:10.1007/s12559-014-9255-2 · Cited by 958 · see citing works

Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models

Mata, J. (2011) · Engineering Structures · doi:10.1016/j.engstruct.2010.12.011 · Cited by 373 · see citing works

Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations

Salazar, F., Moya, R. M., Toledo, M. Á., & Oñate, E. (2015) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-015-9157-9 · Cited by 367 · see citing works

Concrete dam deformation prediction model for health monitoring based on extreme learning machine

Kang, F., Liu, J., Li, J., & Li, S. (2017) · Structural Control and Health Monitoring · doi:10.1002/stc.1997 · Cited by 254 · see citing works

Constructing statistical models for arch dam deformation

Mata, J., de Castro, A. T., & da Costa, J. S. (2013) · Structural Control and Health Monitoring · doi:10.1002/stc.1575 · Cited by 245 · see citing works

An empirical comparison of machine learning techniques for dam behaviour modelling

Salazar, F., Toledo, M. Á., Oñate, E., & Moya, R. M. (2015) · Structural Safety · doi:10.1016/j.strusafe.2015.05.001 · Cited by 228 · see citing works

Development of support vector regression identification model for prediction of dam structural behaviour

Ranković, V., Grujović, N., Divac, D., & Milivojević, N. (2014) · Structural Safety · doi:10.1016/j.strusafe.2014.02.004 · Cited by 196 · see citing works

Hydrostatic, Temperature, Time-Displacement Model for Concrete Dams

Léger, P., & Leclerc, M. (2007) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(2007)133:3(267 · Cited by 182 · see citing works

Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation

Kang, F., Li, J., Zhao, S., & Wang, Y. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.11.065 · Cited by 179 · see citing works

Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms

Kang, F., Li, J., & Dai, J. (2019) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2019.03.003 · Cited by 175 · see citing works

Statistical analysis and structural identification in concrete dam monitoring

Sortis, A. D., & Paoliani, P. (2006) · Engineering Structures · doi:10.1016/j.engstruct.2006.04.022 · Cited by 173 · see citing works

Performance improvement method of support vector machine-based model monitoring dam safety

Su, H., Chen, Z., & Wen, Z. (2015) · Structural Control and Health Monitoring · doi:10.1002/stc.1767 · Cited by 162 · see citing works

Health monitoring of concrete dams: a literature review

Bukenya, P., Moyo, P., Beushausen, H. D., & Oosthuizen, C. (2014) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-014-0079-2 · Cited by 147 · see citing works

Thermal displacements of concrete dams: Accounting for water temperature in statistical models

Tatin, M., Briffaut, M., Dufour, F., Simon, A., & Fabre, J.-P. (2015) · Engineering Structures · doi:10.1016/j.engstruct.2015.01.047 · Cited by 120 · see citing works

A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam

Bui, K.-T. T., Bui, D. T., Zou, J., Doan, C. V., & Revhaug, I. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2666-0 · Cited by 115 · see citing works

Adaptive system for dam behavior modeling based on linear regression and genetic algorithms

Stojanovic, B. S., Milivojević, M., Ivanovic, M. R., Milivojević, N., & Divac, D. (2013) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.06.019 · Cited by 110 · see citing works

Combination forecast model for concrete dam displacement considering residual correction

Wei, B., Yuan, D., Li, H., & Xu, Z. (2017) · Structural Health Monitoring · doi:10.1177/1475921717748608 · Cited by 97 · see citing works

Statistical modelling of thermal displacements for concrete dams: Influence of water temperature profile and dam thickness profile

Tatin, M., Briffaut, M., Dufour, F., Simon, A., & Fabre, J.-P. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.03.010 · Cited by 97 · see citing works

Early detection of anomalies in dam performance: A methodology based on boosted regression trees

Salazar, F., Toledo, M. Á., González, J. M., & Oñate, E. (2017) · Structural Control and Health Monitoring · doi:10.1002/stc.2012 · Cited by 94 · see citing works

Statistical modeling for deformation analysis of concrete arch dams with influential horizontal cracks

Hu, J. B., & Wu, S. (2018) · Structural Health Monitoring · doi:10.1177/1475921718760309 · Cited by 81 · see citing works

Time-varying identification model for dam behavior considering structural reinforcement

Su, H., Wen, Z., Sun, X., & Yang, M. (2015) · Structural Safety · doi:10.1016/j.strusafe.2015.07.002 · Cited by 79 · see citing works

Application of advanced statistical methods for extracting long-term trends in static monitoring data from an arch dam

Loh, C., Chen, C., & Hsu, T. (2011) · Structural Health Monitoring · doi:10.1177/1475921710395807 · Cited by 76 · see citing works

Extreme learning machine with kernel model based on deep learning

Ding, S., Guo, L., & Hou, Y. (2016) · Neural Computing and Applications · doi:10.1007/s00521-015-2170-y · Cited by 72 · see citing works

Towards an Error Correction Model for dam monitoring data analysis based on Cointegration Theory

Li, F., Wang, Z., & Liu, G. (2013) · Structural Safety · doi:10.1016/j.strusafe.2013.02.005 · Cited by 70 · see citing works

Fast detection of impact location using kernel extreme learning machine

Fu, H., Vong, C., Wong, P.-K., & Yang, Z. (2014) · Neural Computing and Applications · doi:10.1007/s00521-014-1568-2 · Cited by 67 · see citing works

Time–frequency analysis for concrete dam safety control: Correlation between the daily variation of structural response and air temperature

Mata, J., de Castro, A. T., & da Costa, J. S. (2013) · Engineering Structures · doi:10.1016/j.engstruct.2012.12.013 · Cited by 64 · see citing works

Modified hybrid forecast model considering chaotic residual errors for dam deformation

Wei, B., Yuan, D., Xu, Z., & Li, L. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2188 · Cited by 64 · see citing works

Modelling of dam behaviour based on neuro-fuzzy identification

Ranković, V., Grujović, N., Divac, D., Milivojević, N., & Novaković, A. (2012) · Engineering Structures · doi:10.1016/j.engstruct.2011.11.011 · Cited by 60 · see citing works

A self-tuning system for dam behavior modeling based on evolving artificial neural networks

Stojanovic, B. S., Milivojević, M., Milivojević, N., & Antonijevic, D. B. (2016) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2016.02.010 · Cited by 57 · see citing works

Integration of Artificial Neural Networks with Radial Basis Function Interpolation in Earthfill Dam Seepage Modeling

Nourani, V., & Babakhani, A. (2012) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000200 · Cited by 56 · see citing works

Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis

Das, P. P., Bisoi, R., & Dash, P. K. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.10.053 · Cited by 52 · see citing works

Assessment of long-term coordinate time series using hydrostatic-season-time model for rock-fill embankment dam

Gamse, S., & Oberguggenberger, M. (2016) · Structural Control and Health Monitoring · doi:10.1002/stc.1859 · Cited by 48 · see citing works

A statistical model of deformation during the construction of a concrete face rockfill dam

Shi, Y., Yang, J., Wu, J., & He, J. (2017) · Structural Control and Health Monitoring · doi:10.1002/stc.2074 · Cited by 41 · see citing works

Anomaly identification of foundation uplift pressures of gravity dams based on DTW and LOF

Hu, J. B., Ma, F., & Wu, S. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2153 · Cited by 37 · see citing works

Modeling method for predicting seepage of RCC dams considering time-varying and lag effect

Wei, B., Gu, M., Li, H., Xiong, W., & Xu, Z. (2017) · Structural Control and Health Monitoring · doi:10.1002/stc.2081 · Cited by 34 · see citing works

Structural Health Monitoring with dependence on non-harmonic periodic hidden covariates

Nguyen, L. H., & Goulet, J.-A. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.03.080 · Cited by 8 · see citing works

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Ren, Q., Li, M., Li, H., & Shen, Y. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101407 · Cited by 142 · see citing works

The role of artificial intelligence and digital technologies in dam engineering: Narrative review and outlook

Hariri‐Ardebili, M. A., Mahdavi, G., Nuss, L. K., & Lall, U. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106813 · Cited by 124 · see citing works

Recent advancement of concrete dam health monitoring technology: A systematic literature review

Prakash, G., Dugalam, R., Barbosh, M., & Sadhu, A. (2022) · Structures · doi:10.1016/j.istruc.2022.08.021 · Cited by 113 · see citing works

Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review

Keshmiry, A., Hassani, S., Mousavi, M., & Dackermann, U. (2023) · Buildings · doi:10.3390/buildings13040918 · Cited by 96 · see citing works

Sustainable small-scale hydropower solutions in Central Asian countries for local and cross-border energy/water supply

Azimov, U., & Авезова, Н. Р. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112726 · Cited by 96 · see citing works

Multi-sensor real-time monitoring of dam behavior using self-adaptive online sequential learning

Ren, Q., Li, M., Kong, T., & Ma, J. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104365 · Cited by 88 · see citing works

Prediction of arch dam deformation via correlated multi-target stacking

Chen, S., Gu, C., Lin, C., & Hariri‐Ardebili, M. A. (2020) · Applied Mathematical Modelling · doi:10.1016/j.apm.2020.10.028 · Cited by 81 · see citing works

Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis

Guo, J., He, Q., Zhen, D., Gu, F., & Ball, A. D. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108969 · Cited by 73 · see citing works

A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations

Ren, Q., Li, M., Li, H., Song, L., Si, W., & Liu, H. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12654 · Cited by 66 · see citing works

DP-GMM clustering-based ensemble learning prediction methodology for dam deformation considering spatiotemporal differentiation

Chen, W., Wang, X., Cai, Z., Liu, C., Zhu, Y., & Lin, W. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106964 · Cited by 63 · see citing works

Performance-improved TSVR-based DHM model of super high arch dams using measured air temperature

Yuan, D., Gu, C., Qin, X., Shao, C., & He, J. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.113400 · Cited by 61 · see citing works

Multi-parameter inverse analysis of concrete dams using kernel extreme learning machines-based response surface model

Kang, F., Liu, X., Li, J., & Li, H. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2022.113999 · Cited by 61 · see citing works

A hybrid model of bald eagle search and relevance vector machine for dam safety monitoring using long-term temperature

Yu, X., Li, J., & Kang, F. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101863 · Cited by 59 · see citing works

A deep learning method for predicting the displacement of concrete arch dams considering the effect of cracks

Xu, B., Chen, Z., Su, H., & Zhang, H. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102574 · Cited by 58 · see citing works

Displacement observation data-based structural health monitoring of concrete dams: A state-of-art review

Wang, S., Gu, C., Liu, Y., Gu, H., Xu, B., & Wu, B. (2024) · Structures · doi:10.1016/j.istruc.2024.107072 · Cited by 58 · see citing works

A high-performance displacement prediction model of concrete dams integrating signal processing and multiple machine learning techniques

Yuan, D., Gu, C., Wei, B., Qin, X., & Xu, W. (2022) · Applied Mathematical Modelling · doi:10.1016/j.apm.2022.07.032 · Cited by 56 · see citing works

Detection and quantification of temperature sensor drift using probabilistic neural networks

Pereira, M., & Glišić, B. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118884 · Cited by 56 · see citing works

Data-driven deformation prediction model for super high arch dams based on a hybrid deep learning approach and feature selection

Wu, Y., Kang, F., Zhu, S., & Li, J. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.119483 · Cited by 56 · see citing works

Health assessment of dams under various environmental conditions using structural health monitoring techniques: a state-of-art review

Sivasuriyan, A., Vijayan, D. S., Munusami, R., & Devarajan, P. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16749-3 · Cited by 55 · see citing works

A separate modeling approach to noisy displacement prediction of concrete dams via improved deep learning with frequency division

Li, M., Ren, Q., Li, M., Fang, X., Xiao, L., & Li, H. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102367 · Cited by 55 · see citing works

Multipoint hybrid model for RCC arch dam displacement health monitoring considering construction interface and its seepage

Liu, B., Wei, B., Li, H., & Mao, Y. (2022) · Applied Mathematical Modelling · doi:10.1016/j.apm.2022.06.023 · Cited by 44 · see citing works

A hybrid approach for interval prediction of concrete dam displacements under uncertain conditions

Ren, Q., Li, M., Kong, R., Shen, Y., & Du, S. (2021) · Engineering With Computers · doi:10.1007/s00366-021-01515-3 · Cited by 44 · see citing works

Towards online monitoring of concrete dam displacement subject to time-varying environments: An improved sequential learning approach

Ren, Q., Li, H., Li, M., Zhang, J., & Kong, T. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101881 · Cited by 43 · see citing works

Centroid deformation-based nonlinear safety monitoring model for arch dam performance evaluation

Zhao, E., & Wu, C. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.112652 · Cited by 43 · see citing works

Displacement behavior interpretation and prediction model of concrete gravity dams located in cold area

Yuan, D., Gu, C., Wei, B., Qin, X., & Gu, H. (2022) · Structural Health Monitoring · doi:10.1177/14759217221122368 · Cited by 41 · see citing works

Multi-output displacement health monitoring model for concrete gravity dam in severely cold region based on clustering of measured dam temperature field

Gu, C., Zhu, M., Wu, Y. K., Chen, B., Zhou, F., & Chen, W. (2023) · Structural Health Monitoring · doi:10.1177/14759217221142006 · Cited by 41 · see citing works

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural network

Cao, W., & Li, J. (2022) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-022-0855-8 · Cited by 40 · see citing works

Multi-block synchronous prediction of concrete dam displacements using MIMO machine learning paradigm

Ren, Q., Li, H., Zheng, X., Li, M., Xiao, L., & Kong, T. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101855 · Cited by 40 · see citing works

Machine learning-aided PSDM for dams with stochastic ground motions

Hariri‐Ardebili, M. A., Chen, S., & Mahdavi, G. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101615 · Cited by 39 · see citing works

A multipoint prediction model for nonlinear displacement of concrete dam

Yao, K., Wen, Z., Yang, L., Chen, J., Hou, H., & Su, H. (2022) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12911 · Cited by 39 · see citing works

A Coupled Model for Dam Foundation Seepage Behavior Monitoring and Forecasting Based on Variational Mode Decomposition and Improved Temporal Convolutional Network

Zhu, Y., Zhang, Z., Gu, C., Li, Y., Zhang, K., & Xie, M. (2023) · Structural Control and Health Monitoring · doi:10.1155/2023/3879096 · Cited by 37 · see citing works

Prediction of long-term maximum settlement deformation of concrete face rockfill dams using hybrid support vector regression optimized with HHO algorithm

Li, Y., Yin, Q., Zhang, Y., & Qiu, W. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00641-w · Cited by 36 · see citing works

A new interval prediction method for displacement behavior of concrete dams based on gradient boosted quantile regression

Ren, Q., Li, M., & Shen, Y. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2859 · Cited by 35 · see citing works

A methodology for dam parameter identification combining machine learning, multi-objective optimization and multiple decision criteria

Li, W., & Wu, Z. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109476 · Cited by 35 · see citing works

Comprehensive evaluation method for structural behavior of concrete dams in cold regions

Fu, X., Zhao, G., Wang, M., Wang, J., Xu, Y., & Gu, C. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2022.115435 · Cited by 34 · see citing works

Optimized deformation monitoring models of concrete dam considering the uncertainty of upstream and downstream surface temperatures

Bowen, W., Zhaoxing, L., & Yuan, D. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.115950 · Cited by 33 · see citing works

Physical data-driven modeling of deformation mechanism constraints on earth-rock dams based on deep feature knowledge distillation and finite element method

Tian, J., Luo, Y., Lü, X., Li, Y., & Chen, J. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.117899 · Cited by 33 · see citing works

Prediction model for high arch dam stress during the operation period using LightGBM with MSSA and SHAP

Li, B., Ning, J., Yang, S., & Zhang, L. (2024) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2024.103635 · Cited by 32 · see citing works

A multiple-point monitoring model for concrete dam displacements based on correlated multiple-output support vector regression

Ren, Q., Li, M., Bai, S., & Shen, Y. (2022) · Structural Health Monitoring · doi:10.1177/14759217211069639 · Cited by 32 · see citing works

Concrete arch dam behavior prediction using kernel-extreme learning machines considering thermal effect

Liu, X., Kang, F., Ma, C., & Li, H. (2021) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00452-x · Cited by 30 · see citing works

TPE-xgboost for explainable predictions of concrete compressive strength considering compositions, and mechanical and microstructure properties of testing samples

Akber, M. Z., Anwar, G. A., Chan, W. K., & Lee, H. H. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.139398 · Cited by 29 · see citing works

Hybrid artificial intelligence-based inference models for accurately predicting dam body displacements: A case study of the Fei Tsui dam

Cheng, M., Cao, M.-T., & Huang, I.-F. (2021) · Structural Health Monitoring · doi:10.1177/14759217211044116 · Cited by 26 · see citing works

Bayesian incremental learning paradigm for online monitoring of dam behavior considering global uncertainty

Ren, Q., Li, H., Li, M., Kong, T., & Guo, R. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110411 · Cited by 26 · see citing works

Deep transfer learning-based time-varying model for deformation monitoring of high earth-rock dams

Tian, J., Chen, C., Lü, X., Li, Y., & Chen, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109310 · Cited by 24 · see citing works

A self-matching model for online anomaly recognition of safety monitoring data in dams

Zhang, F., Lü, X., Li, Y., Gao, Z., Zhang, H., & Huang, H. (2022) · Structural Health Monitoring · doi:10.1177/14759217221074603 · Cited by 24 · see citing works

Coupling sPCA‐Based Statistical Modeling With Deep Residual Networks Considering Thermal Effect for Deformation Forecasting in High Dams

Liu, B., Liu, F., & Song, F. (2025) · Structural Control and Health Monitoring · doi:10.1155/stc/6688960 · Cited by 23 · see citing works

Dam deformation early warning model based on cluster analysis and spatiotemporal data fusion

Lei, W., Wang, J., Ji, T., & Li, P. (2022) · Measurement · doi:10.1016/j.measurement.2022.112109 · Cited by 23 · see citing works

A multisource data‐driven monitoring model for assessing concrete dam behavior

Yao, K., Wen, Z., Shao, C., Yang, J.-Q., & Su, H. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13232 · Cited by 22 · see citing works

A novel deep learning multi-step prediction model for dam displacement using Chrono-initialized LSTM and sequence-to-sequence framework

Su, Y. P., Fu, J., Lin, C., Lai, X., Zhiming, Z., Lin, Y., & He, Q. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126624 · Cited by 22 · see citing works

Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators

Kuok, S., Yuen, K., Roberts, S., & Girolami, M. (2020) · Structural Health Monitoring · doi:10.1177/1475921720916923 · Cited by 22 · see citing works

Damage quantification using transfer component analysis combined with Gaussian process regression

Yano, M. O., da Silva, S., Figueiredo, E., & Villani, L. G. (2022) · Structural Health Monitoring · doi:10.1177/14759217221094500 · Cited by 22 · see citing works

Improved extreme learning machine-based dam deformation prediction considering the physical and hysteresis characteristics of the deformation sequence

Cai, Z., Yu, J., Chen, W., Wang, J., Wang, X., & Guo, H. G. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00603-2 · Cited by 22 · see citing works

Missing data imputation method for dams based on multi-head attention mechanisms and generative adversarial networks

Wang, X., Bao, T., Gong, J., Zhang, J., & Xu, Y. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.120350 · Cited by 20 · see citing works

Concrete dam deformation prediction model considering the time delay of monitoring variables

Cao, X., Sheng, J., Jiang, C., Yuan, D., & Zhang, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-92806-y · Cited by 19 · see citing works

A Combination Model for Displacement Interval Prediction of Concrete Dams Based on Residual Estimation

Yang, X., Xiang, Y., Shen, G., & Sun, M. (2022) · Sustainability · doi:10.3390/su142316025 · Cited by 19 · see citing works

A High-Robust Displacement Prediction Model for Super-High Arch Dams Integrating Wavelet De-Noising and Improved Random Forest

Gu, C., Wu, B., & Chen, Y. (2023) · Water · doi:10.3390/w15071271 · Cited by 19 · see citing works

A hybrid monitoring model of rockfill dams considering the spatial variability of rockfill materials and a method for determining the monitoring indexes

Li, R., Yang, J., Pengli, Z., Wang, J., Ma, C., Chao, C., Cheng, L., Jian’e, W., & Zhou, M. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00562-8 · Cited by 18 · see citing works

A similarity-aware ensemble method for displacement prediction of concrete dams based on temporal division and fully Bayesian learning

Liu, R., Ren, Q., Li, M., Ji, X., Liu, T., & Liu, H. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102921 · Cited by 18 · see citing works

Interval prediction method for dynamic zoning of displacements in super-high arch dam under significant water level fluctuations

Xu, B., Chen, Z., Su, H., Zhang, H., & Zhu, Z. (2024) · Structural Health Monitoring · doi:10.1177/14759217241282738 · Cited by 17 · see citing works

Deformation prediction model for concrete dams considering the effect of solar radiation

Liu, M., Qi, Y., & Su, H. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103252 · Cited by 16 · see citing works

SSA optimized back propagation neural network model for dam displacement monitoring based on long-term temperature data

Yu, X., Li, J., & Kang, F. (2022) · European Journal of Environmental and Civil engineering · doi:10.1080/19648189.2022.2090445 · Cited by 15 · see citing works

Prediction intervals for concrete face sandy gravel dam settlement using Kalman filter-based kernel extreme learning machine

Zhou, T., Wei, Y., Jie, Y., & Zhang, Y. (2024) · Measurement · doi:10.1016/j.measurement.2024.115094 · Cited by 15 · see citing works

A New Hybrid Monitoring Model for Displacement of the Concrete Dam

Gu, C., Cui, X., Gu, H., & Yang, M. (2023) · Sustainability · doi:10.3390/su15129609 · Cited by 14 · see citing works

Rapid postearthquake modelling method for deformation monitoring models of high arch dams based on metalearning and graph attention

Tian, J., Luo, Y., Huang, H., Chen, J., & Li, Y. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102925 · Cited by 12 · see citing works

Noise adaptive spatiotemporal neural networks for deformation prediction of high rockfill dams

Wang, Z., Ma, G., Ai, Z., Ding, Q., & Zhou, W. H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128836 · Cited by 11 · see citing works

GCN-Former-BiLSTM: An enhanced LSTM model using transformer and multiscale spatio-temporal graph neural network for high arch dam displacement prediction

Ma, Y., Bao, T., Li, Y., Wu, Z., Zhao, M., & Liu, X. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.121395 · Cited by 11 · see citing works

A new early warning method for dam displacement behavior based on non-normal distribution function

Jiang, Z., & Chen, H. (2022) · Water Science and Engineering · doi:10.1016/j.wse.2022.04.001 · Cited by 11 · see citing works

Deformation prediction model of concrete face rockfill dams based on an improved random forest model

Li, Y., Yin, Q., Zhang, Y., & Zhou, H. (2023) · Water Science and Engineering · doi:10.1016/j.wse.2023.09.005 · Cited by 11 · see citing works

Effect of environmental factors on modal identification of a hydroelectric dam’s hollow-gravity concrete block

A., Y. V. A., Araújo, I. G., Villalba-Morales, J. D., & Aracayo, L. A. S. (2024) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-024-00828-3 · Cited by 10 · see citing works

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Time series forecasting and anomaly detection of bridge deformation based on PS-InSAR technique and seasonal-trend decomposition

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Comparative Performance Analysis of Machine Learning Models for Compressive Strength Prediction in Concrete Mix Design

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Improved Hydrostatic-Season-Time Model for Dam Monitoring: Inclusion of a Thermal Analytical Solution

Belmokre, A., Santillán, D., & Mihoubi, M. K. (2020) · Lecture notes in civil engineering · doi:10.1007/978-981-15-9121-1_5 · Cited by 7 · see citing works

Adaptive multi-point hybrid model for displacement prediction of concrete dams considering sequence cross-relatedness

Ren, Q., Liu, R., Li, M., Qi, Z., Tan, D., & Xu, Z. (2025) · Measurement · doi:10.1016/j.measurement.2025.118522 · Cited by 6 · see citing works

MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect

Liu, H., Li, D., & Ding, Y. (2023) · Applied Sciences · doi:10.3390/app13148538 · Cited by 6 · see citing works

On the multi‐parameters identification of concrete dams: A novel stochastic inverse approach

Lin, C., Du, X., Chen, S., Li, T., Zhou, X., & van Gelder, P. H. A. J. M. (2024) · International Journal for Numerical and Analytical Methods in Geomechanics · doi:10.1002/nag.3812 · Cited by 6 · see citing works

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Predictive Control Method of Reaming up in the Raise Boring Process Using Kernel Based Extreme Learning Machine

Jing, G., Yan, W. M., & Hu, F. (2023) · Processes · doi:10.3390/pr11010277 · Cited by 5 · see citing works

Predictive modeling of concrete arch dam behavior: evaluating the efficacy of Random Forest and Radial Basis Function Networks

Babadi, A. M., Mirzabozorg, H., & Baharan, K. (2025) · AI in Civil Engineering · doi:10.1007/s43503-025-00071-9 · Cited by 4 · see citing works

Enhanced Prediction and Evaluation of Hydraulic Concrete Compressive Strength Using Multiple Soft Computing and Metaheuristic Optimization Algorithms

Li, T., Hu, X., Li, T., Liao, J., Mei, L., Tian, H., & Gu, J. (2024) · Buildings · doi:10.3390/buildings14113461 · Cited by 4 · see citing works

Stress Prediction Model of Super-High Arch Dams during Their Initial Operation Stages

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Attention-enhanced spatiotemporal deep learning-based automatic warning model with uncertainty estimation in dam safety

Liu, H., Su, H., Kookalani, S., Cao, M., Lei, Y., & Wu, W. (2025) · Structural Health Monitoring · doi:10.1177/14759217251358537 · Cited by 3 · see citing works

Acoustic Emission-Based Structural Health Monitoring for Thermal Protection Systems: Impact of Number of Receiver Channels and Temperature Sensitivity of Lamb Waves

Bano, S., Iqbal, M., Pelt, H. V., & Wild, G. (2025) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp63728.2025.11144971 · Cited by 3 · see citing works

Machine learning–enhanced structural health monitoring of the steel–glass exedra under year-round environmental loading

Crognale, M., Rinaldi, C., Ciambella, J., & Gattulli, V. (2026) · Journal of low frequency noise, vibration and active control · doi:10.1177/14613484261421404 · Cited by 3 · see citing works

An interpretable data-driven method for dam displacement modeling and its application to a high-altitude concrete dam

Yao, K., Yang, J.-Q., & Su, H. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.121222 · Cited by 2 · see citing works

A Multi-Input Multi-Output Considering Correlation and Hysteresis Prediction Method for Gravity Dam Displacement with Interpretative Functions

Xu, B., Yao, Y., Wang, X., Sun, L., Ou, B., & Zhang, Y. (2025) · Applied Sciences · doi:10.3390/app15137096 · Cited by 1 · see citing works

Application of neural network model incorporating attention mechanism in concrete temperature prediction

Zhang, D., Gu, D., Zhang, Q., Xiao, P., Zhang, Z., & Sun, Q. (2025) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2574341 · Cited by 1 · see citing works

Research on a dam real-time strong-earthquake monitoring, warning, and response system based on polling commands

Zhang, W., Zhao, Q., Jiang, Y., Bai, Y., Zhao, S., Sun, W. J., Zhang, Y., Yu, H., & Gu, W. (2025) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-025-01032-7 · Cited by 1 · see citing works

Mixed Monitoring Model of Concrete Arch Dam on Residual Sequence CorrectionPreprint

Wang, Z., Fu, S., Chen, D., Chi, H., Bin, O., & Yang, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4699602/v1 · Cited by 1 · see citing works

Predominant Research Themes in Using Machine Learning in Structural Health Monitoring

Akber, M. Z., & Zhang, X. (2023) · Civil-comp conferences · doi:10.4203/ccc.3.13.4

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Karras, A., Theodorakopoulos, L., Karras, C., & Antonopoulou, H. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825772

Development of Intelligent Analysis and Visualization Software Tools for Environmental Monitoring Data

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Investigating the Impact of Waterhead, Time and Temperature on Dam Displacement: Application of Computer Aided Models

Abdullah, M. (2025) · AUIQ technical engineering science. · doi:10.70645/3078-3437.1028

A comparative study of supervised feature selection methods in dam displacement prediction

Zhong, S., & Xu, B. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae2568

A hybrid prediction model for the displacement of concrete dams considering temperature hysteresis effects

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An Intelligent Temporal Framework for Interval Prediction of Concrete Dam Deformation

Han, F., Gu, C., Liu, P., & Cui, X. (2026) · Informatics · doi:10.3390/informatics13090148

Spatiotemporal graph convolutional networks for super-high arch dam displacement prediction

Wu, Y., Kang, F., Chen, J., Zhu, S., & Zhou, D. (2026) · Applied Mathematical Modelling · doi:10.1016/j.apm.2026.117365

Interpretable deep learning for dam structural response prediction using low-rank denoising reconstruction

Lu, Z., & Liu, H.-P. (2026) · Measurement · doi:10.1016/j.measurement.2026.123667

Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation

Kang, F., Li, J., Zhao, S., & Wang, Y. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.11.065 · Cited by 179 · see citing works

Displacement Model for Concrete Dam Safety Monitoring via Gaussian Process Regression Considering Extreme Air Temperature

Kang, F., & Li, J. (2019) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0002467 · Cited by 88 · see citing works

Concrete Dam Behavior Prediction Using Multivariate Adaptive Regression Splines with Measured Air Temperature

Kang, F., Liu, X., & Li, J. (2019) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-019-04095-z · Cited by 17 · see citing works

Concrete dam deformation prediction model for health monitoring based on extreme learning machine

Kang, F., Liu, J., Li, J., & Li, S. (2017) · Structural Control and Health Monitoring · doi:10.1002/stc.1997 · Cited by 254 · see citing works

A hybrid prediction model for the displacement of concrete dams considering temperature hysteresis effects

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