The ever-increasing dam heights and the enormous number of existing dams pose significant challenges to dam safety construction and management. For earth and rockfill dams, the deformation control problem becomes more and more prominent. Compared with the study based on monitoring data of single monitoring point, the constructed plane deformation based on the monitoring data can reflect the deformation and mechanical characteristics of the dam more effectively. Therefore, this paper proposes an approach for dam crest plane deformation construction and crack analysis considering monitoring points weights, using the PBG high core wall rockfill dam as a case study. First, the individual monitoring point Hydrostatic–Season–Time (HST) model of dam crest settlement was analyzed based on statistical method. Then, evaluated the quality of monitoring data using outlier rate, maximum deformation magnitude, and deformation trend consistency, and assigns weights to each monitoring point accordingly. Finally, by considering each monitoring point weights, the mapping relationship between monitoring point coordinates and HST model parameters is fitted, and dam crest plane settlement HST model was established, and the deformation characteristics and crack analysis are conducted. This article proposes a new method for expanding independent monitoring point deformation data to plane deformation, improving the utilization rate and reliability of monitoring data and providing technical support for enhancing the long-term safety and stability of dams.
SchmittRJPRosaL. Dams for hydropower and irrigation: trends, challenges, and alternatives. Renew Sustain Energy Rev2024; 199: 114439.
2.
MaHChiF.Major technologies for safe construction of high earth-rockfill dams. Engineering2016; 2(4): 236–258.
3.
MaHChiF.Technical progress on researches for the safety of high concrete-faced rockfill dams. Engineering2016; 2(3): 332–339.
4.
GB/T22385-2025:2025. Specification for acceptance of dam safety monitoring system.
5.
WangDAnNHuJ, et al. Multiscale modelling reveals new insights into the deformation mechanism of rockfill dams. Comput Geotech2025; 188: 107573.
6.
ShiYYangJWuJ, et al. A statistical model of deformation during the construction of a concrete face rockfill dam. Struct Control Health Monit2018; 25(2): e2074.
7.
LiuSHeWSunY, et al. Analysis of the behavior of a high earth-core rockfill dam considering particle breakage. Comput Geotech2023; 157: 105320.
8.
XiaoPZhaoRLiD, et al. As-built inventory and deformation analysis of a high rockfill dam under construction with terrestrial laser scanning. Sensors2022; 22(2): 521.
9.
DingCXueKZhouC.Case study on long-term deformation monitoring and numerical simulation of layered rock slopes on both sides of Wudongde dam reservoir area. Sci Rep2024, 14(1): 6909.
10.
ZhouXChiSJiaY.Wetting deformation of core-wall rockfill dams. Int J Geomech2019; 19(8): 04019084.
11.
WuEZhuJWangJ, et al. Investigation on the incremental creep model of rockfill material for dam building. Comput Geotech2024; 174: 106636.
12.
RenQLiHLiM, et al. Towards online monitoring of concrete dam displacement subject to time-varying environments: an improved sequential learning approach. Adv Eng Inform2023; 55: 101881.
13.
ZhouXChiSWangM, et al. Study on wetting deformation characteristics of coarse granular materials and its simulation in core-wall rockfill dams. Int J Numer Anal Methods Geomech2020; 44(6): 851–873.
14.
LiYBaoTGaoZ, et al. A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques. Struct Health Monit2022; 21(3): 770–787.
15.
YaoKWenZShaoC, et al. A multisource data-driven monitoring model for assessing concrete dam behavior. Comput Aided Civ Infrastruct Eng2024; 39(23): 3595–3609.
16.
Hariri-ArdebiliMAMahdaviGNussLK, et al. The role of artificial intelligence and digital technologies in dam engineering: narrative review and outlook. Eng Appl Artif Intell2023; 126, 106813.
17.
ChenJ.The nonlinear parameter estimation of aging deformation of concrete dam. Dam Observation and Geotechnical Testing, 1980, 2: 3–13
18.
GuCWangYGuH, et al. A combined safety monitoring model for high concrete dams. Appl Sci2022; 12(23): 12103.
19.
LiXWenZSuH.An approach using random forest intelligent algorithm to construct a monitoring model for dam safety. Eng Comput2021; 37(1): 39–56.
20.
YangDGuCZhuY, et al. A concrete dam deformation prediction method based on LSTM with attention mechanism. IEEE Access2020; 8: 185177–185186.
21.
HosainMTJimJRMridhaMF, et al. Explainable AI approaches in deep learning: advancements, applications and challenges. Comput Electr Eng2024; 117: 109246.
22.
YangGGuHChenX, et al. Hybrid hydraulic-seasonal-time model for predicting the deformation behaviour of high concrete dams during the operational period. Struct Control Health Monit2021; 28(3): e2685.
23.
ShaoCGuCYangM, et al. A novel model of dam displacement based on panel data. Struct Control Health Monit2018; 25(1): e2037.
24.
YangZDaiWChenB, et al. The application of Kriging’s space-time auto-regressive model in deformation modeling. Sci Surv Mapp2018; 43: 6.
25.
RenQLiHZhengX, et al. Multi-block synchronous prediction of concrete dam displacements using MIMO machine learning paradigm. Adv Eng Inform2023; 55: 101855.
26.
ScaioniMMarsellaMCrosettoM, et al. Geodetic and remote-sensing sensors for dam deformation monitoring. Sensors2018; 18(11): 3682.
27.
SunZMahmoodianMSidiqA, et al. Optimal sensor placement for structural health monitoring: a comprehensive review. J Sens Actuat Netw2025; 14(2): 22.
28.
ZhengSShaoCGuC, et al. An automatic data process line identification method for dam safety monitoring data outlier detection. Struct Control Health Monit2022; 29(7): e2948.
29.
ZhangHJingYChenJ, et al. Characteristics and causes of crest cracking on a high core-wall rockfill dam: a case study. Eng Geol2022; 297: 106488.
30.
RongZPangRXuB, et al. Dam safety monitoring data anomaly recognition using multiple-point model with local outlier factor. Autom Constr2024; 159: 105290.
31.
ChenCLuXLiJ, et al. A novel settlement forecasting model for rockfill dams based on physical causes. Bull Eng Geol Environ2021; 80(10): 7973–7988.
32.
TianJLuoYLuX, et al. Physical data-driven modeling of deformation mechanism constraints on earth-rock dams based on deep feature knowledge distillation and finite element method. Eng Struct2024; 307: 117899.
33.
LégerPLeclercM.Hydrostatic, temperature, time-displacement model for concrete dams. J Eng Mech2007; 133(3): 267–277.
34.
HuJWuS.Statistical modeling for deformation analysis of concrete arch dams with influential horizontal cracks. Struct Health Monit2019; 18(2): 546–562.
35.
LiuXLiZSunL, et al. A critical review of statistical model of dam monitoring data. J Build Eng2023; 80: 108106.
36.
AiZMaGZhangG, et al. Multi-source monitoring data filtering assisted deformation analysis model updating of ultra-high rockfill dam. Comput Geotech2024; 171: 106323.
37.
LinCWengKLinY, et al. Time series prediction of dam deformation using a hybrid STL–CNN–GRU model based on sparrow search algorithm optimization. Appl Sci2022; 12(23): 11951.
38.
LiYBaoTGongJ, et al. The prediction of dam displacement time series using STL, extra-trees, and stacked LSTM neural network. IEEE Access2020; 8: 94440–94452.
39.
LiBYangJHuD.Dam monitoring data analysis methods: a literature review. Struct Control Health Monit2020; 27(3): e2501.
40.
ZiemerJSteinGWickerC, et al. Enhancing the prediction of dam deformations: a novel data-driven approach. Remote Sens2025; 17(6): 1026.
41.
PeiLChenJZhouJ, et al. A fractal prediction method for safety monitoring deformation of core rockfill dams. Math Probl Eng2021; 2021(1): 6655657.
42.
ZhangHWeiWXueS.Analysis on the variation of temperature and precipitation in Dingxi based on R/S and Mann-Kendall test. Res Soil Water Conserv2015; 22(6): 183–189.
43.
DiakoulakiDMavrotasGPapayannakisL.Determining objective weights in multiple criteria problems: the critic method. Comput Oper Res1995; 22(7): 763–770.
44.
ZhouXCaiWHeQ, et al. Construction and analysis of slope plane deformation of high CFRD based on statistical analysis of multi-points monitoring data. Eng Rep2025; 7(3): e70030.
45.
Erdogan ErtenGYavuzMDeutschCV. Combination of machine learning and kriging for spatial estimation of geological attributes. Nat Resour Res2022; 31(1): 191–213.
46.
GuoHZhaoWWangS, et al. Subspot background noise removal method based on Bezier surface fitting. Acta Photonica Sinica2024; 53(8): 0801003.
47.
OstroffCJudgeTA. Perspectives on organizational fit. Psychology Press, New York, 2007.
48.
ZhouXChiSJiaY, et al. A new wetting deformation simulation method based on changes in mechanical properties. Comput Geotech2020; 117: 103261.
49.
ZhouXHeJChiS, et al. Study on collapse settlement and cracks of core wall rockfill dams under wetting deformation. Int J Numer Anal Methods Geomech2023; 47(1): 106–128.
