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

Abstract

This study investigates the application of established open-source machine learning tools, specifically CatBoost, XGBoost, LightGBM, and TensorFlow, which are based on Forest and Radial Basis Function Networks, to predict and analyze the structural behavior of concrete arch dams. Utilizing the Karun-I dam as a case study, the research assesses the performance of various machine learning frameworks. The results demonstrate that Random Forest-based methods achieve superior prediction accuracy and computational efficiency in comparison to Radial Basis Function Networks. Additionally, the analysis emphasizes the critical influence of lake levels as the primary factor impacting dam displacement, as revealed through feature importance evaluation. Overall, this research underscores the promising potential of machine learning in enhancing structural health monitoring for large dams, offering significant insights that contribute to the improvement of safety measures and operational efficiency in dam management.

Keywords

Long-life operation / Arch dams / Radial Basis Function Networks (RBFN) / Random Forest (RF) / CatBoost / XGBoost / LightGBM / TensorFlow

References

[1] Alegre, A., & Oliveira, S. (2018). Damdamage 1 . 0: A matlab 3 dfe program for non-linear analysis of arch dams. https://api.semanticscholar.org/CorpusID:203614189.

[2] BaharanK, MirzabozorgH, BabadiAM. Structural health monitoring of arch dams with deep learning: A comparative study of recurrent neural networks in daily, bi-monthly, and monthly predictions. Journal of Civil Structural Health Monitoring, 2025.

[3] BianchiM, BremenR. Health monitoring of arch dams- Recent developments. Restoration of Buildings and Monuments, 2001, 7: 271-284.

[4] BreimanL. Random forests. Machine Learning, 2001, 45(1): 5-32.

[5] ChenB, TianyiH, HuangZ, FangC. A spatio-temporal clustering and diagnosis method for concrete arch dams using deformation monitoring data. Structural Health Monitoring, 2019, 18(5–6): 1355-1371.

[6] Ferreira, C., & Barros, H. (2020) Nonlinear structural behaviour of an arch dam under static and dynamic loads. XI International Conference on Structural Dynamics, URL https://api.semanticscholar.org/CorpusID:229626266.

[7] Friedman, N., & Labbi, A. (2020). Solving engineering problems with machine learning - introduction to the special theme. ERCIM News, 2020, https://api.semanticscholar.org/CorpusID:220604235.

[8] HastingsJS, MitchellOS. How financial literacy and impatience shape retirement wealth and investment behaviors. Journal of Pension Economics and Finance, 2020, 19(1): 1-20.

[9] HuaizhiS, ChenZ, WenZ. Performance improvement method of support vector machine-based model monitoring dam safety. Structural Control and Health Monitoring, 2016, 23: 252-266.

[10] JiangH, MaF. Comparison of hierarchical clustering based deformation prediction models for high arch dams during the initial operation period. Journal of Civil Structural Health Monitoring, 2021, 11: 897-914.

[11] JiangH, SuhuaW. Statistical modeling for deformation analysis of concrete arch dams with influential horizontal cracks. Structural Health Monitoring, 2019, 18(2): 546-562.

[12] KangF, LiJ. Displacement model for concrete dam safety monitoring via Gaussian process regression considering extreme air temperature. Journal of Structural Engineering, 2020, 146105019001.

[13] KangF, LiJ, ZhaoS, WangY. Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation. Engineering Structures, 2019, 180: 642-653.

[14] KangF, LiuJ, LiJ, LiS. Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Structural Control and Health Monitoring, 2017, 24. e1997

[15] KangF, LiuX, LiJ. Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines. Structural Health Monitoring, 2020, 19(4): 987-1002.

[16] LiB, YangJ, DexiuH. Dam monitoring data analysis methods: A literature review. Structural Control and Health Monitoring, 2020, 27: 1-14.

[17] LightGBM Contributors. (2024). Lightgbm documentation. https://lightgbm.readthedocs.io/en/stable/. Open-source gradient boosting library documentation.

[18] LiuB, WeiB, LiH, MaoY. Multipoint hybrid model for RCC arch dam displacement health monitoring considering construction interface and its seepage. Applied Mathematical Modelling, 2022, 110: 674-697.

[19] LiuW, PanJ, RenY, ZhigangW, WangJ. Coupling prediction model for long-term displacements of arch dams based on long short-term memory network. Structural Control and Health Monitoring, 2020, 27: 1-15.

[20] LiuX, KangF, MaC, LiH. Concrete arch dam behavior prediction using kernel-extreme learning machines considering thermal effect. Journal of Civil Structural Health Monitoring, 2021, 11: 283-299.

[21] MataJ, de CastroAT, da CostaJS. Constructing statistical models for arch dam deformation. Structural Control and Health Monitoring, 2014, 21: 423-437.

[22] MataJ, MirandaF, AntunesA, RomaoX, SantosJP. Characterization of relative movements between blocks observed in a concrete dam and definition of thresholds for novelty identification based on machine learning models. Water, 2023, 15297.

[23] OliveiraS, AlegreA, CarvalhoE, MendesP, ProenccaJ. Seismic and structural health monitoring systems for large dams: Theoretical, computational and practical innovations. Bulletin of Earthquake Engineering, 2022, 20(9): 4483-4512.

[24] PanJ, LiuW, LiuC, WangJ. Convolutional neural network-based spatiotemporal prediction for deformation behavior of arch dams. Expert Systems With Applications, 2023, 232. 120835

[25] PrakashG, SadhuA, NarasimhanS, BreheJ-M. Initial service life data towards structural health monitoring of a concrete arch dam. Structural Control and Health Monitoring, 2018, 25. e2036

[26] RenQ, LiM, ShenY. A new interval prediction method for displacement behavior of concrete dams based on gradient boosted quantile regression. Structural Control and Health Monitoring, 2023, 29. e2859

[27] RenQ, LiM, ShenY. A new interval prediction method for displacement behavior of concrete dams based on gradient boosted quantile regression. Structural Control and Health Monitoring, 2024.

[28] RenQ, LiM, BaiS, ShenY. A multiple-point monitoring model for concrete dam displacements based on correlated multiple-output support vector regression. Structural Health Monitoring, 2022, 21(6): 2768-2785.

[29] RenQ, LiM, KongR, ShenY, ShengliD. A hybrid approach for interval prediction of concrete dam displacements under uncertain conditions. Engineering with Computers, 2023, 39: 1285-1303.

[30] RenQ, LiM, LiH, ShenY. A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism. Advanced Engineering Informatics, 2021, 50. 101407

[31] RenQ, LiM, LiH, SongL, SiW, LiuH. A robust prediction model for displacement of concrete dams subjected to irregular water-level fluctuations. Computer-Aided Civil and Infrastructure Engineering, 2021, 36: 577-601.

[32] RenQ, LiM, SongL, LiuH. An optimized combination prediction model for concrete dam deformation considering quantitative evaluation and hysteresis correction. Advanced Engineering Informatics, 2020, 46. 101154

[33] SalazarF, MoranR, ToledoMA, OnateE. Data-based models for the prediction of dam behaviour: A review and some methodological considerations. Archives of Computational Methods in Engineering, 2017, 24(1): 1-21.

[34] SalazarF, ToledoMA, GonzalezJM, OnateE. Early detection of anomalies in dam performance: A methodology based on boosted regression trees. Structural Control and Health Monitoring, 2017, 24. Article e2012

[35] SevimB, AltunisikAC, BayraktarA. Construction stages analyses using time dependent material properties of concrete arch dams. Computers and Concrete, 2014, 14: 599-612.

[36] Shah, C. (2020). Machine learning introduction and regression. https://api.semanticscholar.org/CorpusID:225946488.

[37] TensorFlow Developers. (2024). Tensorflow. https://www.tensorflow.org/. Open-source machine learning framework.

[38] WangS, CongX, ChongshiG, HuaizhiS, BangbinW. Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams. Transactions of the Institute of Measurement and Control, 2021, 43(15): 3347-3359.

[39] WangS, CongX, ChongshiG, HuaizhiS, KunH, XiaQ. Displacement monitoring model of concrete dams using the shape feature clustering-based temperature principal component factor. Structural Control and Health Monitoring, 2020, 27. e2603

[40] WangS, CongX, LiuY, BangbinW. Mixed-coefficient panel model for evaluating the overall deformation behavior of high arch dams using the spatial clustering. Structural Control and Health Monitoring, 2021, 28. e2809

[41] WangS, CongX, LiuY, BoX. Zonal intelligent inversion of viscoelastic parameters of high arch dams using an hest statistical model. Journal of Civil Structural Health Monitoring, 2021, 12: 207-223.

[42] WangS, CongX, LiuY, BoX. Zonal intelligent inversion of viscoelastic parameters of high arch dams using an hest statistical model. Journal of Civil Structural Health Monitoring, 2022, 12: 207-223.

[43] WangS, CongX, LiuY, HaoG, BoX, KunH. Spatial association-considered real-time risk rate assessment of high arch dams using observed displacement and combination prediction model. Structures, 2023, 53: 1108-1121.

[44] WangS, SuiX, LiuY, HaoG, BoX, XiaQ. Prediction and interpretation of the deformation behaviour of high arch dams based on a measured temperature field. Journal of Civil Structural Health Monitoring, 2023, 13: 661-675.

[45] WangS, YingliX, ChongshiG, BaoT, XiaQ, KunH. Hysteretic effect considered monitoring model for interpreting abnormal deformation behavior of arch dams: A case study. Structural Control and Health Monitoring, 2019, 26. e2417

[46] WangS, YingliX, ChongshiG, XiaQ, KunH. Two spatial association–considered mathematical models for diagnosing the long-term balanced relationship and short-term fluctuation of the deformation behaviour of high concrete arch dams. Structural Health Monitoring, 2020, 19(5): 1421-1439.

[47] WeiB, LiuB, YuanD, MaoY, YaoS. Spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series. Engineering Structures, 2021, 228. 111488

[48] WenZ, FanZ, HuaizhiS. An appso–svm approach building the monitoring model of dam safety. Soft Computing, 2022, 26: 11451-11459.

[49] WenZ, ZhouR, HuaizhiS. Mr and stacked grus neural network combined model and its application for deformation prediction of concrete dam. Expert Systems With Applications, 2022, 201. 117272

[50] XGBoost Contributors. (2024). Xgboost documentation. https://xgboost.readthedocs.io/en/latest/. Open-source gradient boosting library documentation.

[51] Yandex.(2024). Catboost. https://catboost.ai/. Open-source gradient boosting library.

[52] YangG, HaoG, ChenX, ZhaoK, QiaoD, ChenX. Hybrid hydraulic-seasonal-time model for predicting the deformation behaviour of high concrete dams during the operational period. Structural Control and Health Monitoring, 2021, 28. e2685

[53] YuanD, ChongshiG, QinX, ShaoC, HeJ. Performance-improved TSVR-based DHM model of super high arch dams using measured air temperature. Engineering Structures, 2022, 250. 113400

[54] ZhaoE, ChengqingW. Centroid deformation-based nonlinear safety monitoring model for arch dam performance evaluation. Engineering Structures, 2021, 243. 112652

[55] ZhuM, ChenB, ChongshiG, YanW, ChenW. Optimized multi-output lssvr displacement monitoring model for super high arch dams based on dimensionality reduction of measured dam temperature field. Engineering Structures, 2022, 268. 114686

RIGHTS & PERMISSIONS

The Author(s)