Abstract
The mechanism of dam safety monitoring model is analyzed; for the dam system comprehensive affected by multi-factor, the mapping relationship between the influence factors and the dam behavior effects domain is usually nonlinear. Synthesizing each kind of factor, 27 parameters are chosen as the main factors which affect the accuracy of the monitoring model. Taking the actual monitoring data as the evaluation factor, the dam safety monitoring model based on the random forest (RF) intelligent algorithm was built with the actual monitoring data to predict uplift pressure. At the same time, test the significance of each variable based on the RF monitoring model and calculate the importance degree of each variable for the model through the importance function. It is indicated that RF model can be relatively fast and accurately predict the uplift pressure of the dam according to the influence factors. The average prediction accuracy is more than 95%. As compared with other intelligent algorithms such as support vector machine, RF has better robustness, higher prediction accuracy, and faster convergence speed. Because of the uniformity of the calculation procedure and the universality of the prediction method, the RF model also has reasonable extrapolation for other dam safety monitoring models (such as crack opening and seepage discharge). Significance test results obtained by the two methods have shown that the impact of reservoir water level and daily rainfall on the uplift pressure is significant, and other factors’ impact on dam deformation is unstable and changes with the external environmental influence.
Similar content being viewed by others
References
Wu Z, Su H (2005) Dam health diagnosis and evaluation. Smart Mater Struct 14(3):S130
Su H, Wen Z, Wu Z (2011) Study on an intelligent inference engine in early-warning system of dam health. Water Resour Manag 25(6):1545–1563
Jeon J et al (2009) Development of dam safety management system. Adv Eng Softw 40(8):554–563
Kang F, Li J, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87(13–14):861–870
Ardito R, Maier G, Massalongo G (2008) Diagnostic analysis of concrete dams based on seasonal hydrostatic loading. Eng Struct 30(11):3176–3185
Szostak-Chrzanowski A, Chrzanowski A, Massiéra M (2005) Use of deformation monitoring results in solving geomechanical problems—case studies. Eng Geol 79(1–2):3–12
Li F, Wang Z, Liu G (2013) Towards an error correction model for dam monitoring data analysis based on cointegration theory. Struct Saf 43:12–20
Xu C, Yue D, Deng C (2012) Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis. Eng Appl Artif Intell 25(3):468–475
Stojanovic B et al (2013) Adaptive system for dam behavior modeling based on linear regression and genetic algorithms. Adv Eng Softw 65:182–190
Su H, Chen Z, Wen Z (2016) Performance improvement method of support vector machine-based model monitoring dam safety. Struct Control Health Monit 23(2):252–266
Su H, Wu Z, Wen Z (2007) Identification model for dam behavior based on wavelet network. Comput Aided Civ Infrastruct Eng 22(6):438–448
Su H et al (2015) Time-varying identification model for dam behavior considering structural reinforcement. Struct Saf 57:1–7
Huaizhi S, Jiang H, Zhongru W (2012) A study of safety evaluation and early-warning method for dam global behavior. Struct Health Monit 11(3):269–279
Hu J, Ma F (2016) Comprehensive investigation method for sudden increases of uplift pressures beneath gravity dams: case study. J Perform Constr Facil 30(5):04016023
De Sortis A, Paoliani P (2007) Statistical analysis and structural identification in concrete dam monitoring. Eng Struct 29(1):110–120
Mata J (2011) Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng Struct 33(3):903–910
Nourani V, Babakhani A (2012) Integration of artificial neural networks with radial basis function interpolation in earthfill dam seepage modeling. J Comput Civ Eng 27(2):183–195
Riquelme F et al (2011) Application of artificial neural network models to determine movements in an arch dam. In: Proceedings of the 2nd international congress on dam maintenance and rehabilitation, Zaragoza, Spain
Kao CY, Loh CH (2013) Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches. Struct Control Health Monit 20(3):282–303
Santillán D, Fraile-Ardanuy J, Toledo M (2014) Seepage prediction in arch dams by means of artificial neural networks. Water Technol Sci 3:81–96
Simon A et al (2013) Analysis and interpretation of dam measurements using artificial neural networks. In: Proceedings of the 9th ICOLD European club symposium, Venice, Italy
Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222
Ranković V et al (2014) Development of support vector regression identification model for prediction of dam structural behaviour. Struct Saf 48:33–39
Salazar F et al (2016) Interpretation of dam deformation and leakage with boosted regression trees. Eng Struct 119:230–251
Salazar F et al (2017) Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch Comput Methods Eng 24(1):1–21
Salazar F et al (2015) An empirical comparison of machine learning techniques for dam behaviour modelling. Struct Saf 56:9–17
Dong L-J, Li X-B, Kang P (2013) Prediction of rockburst classification using random forest. Trans Nonferrous Met Soc China 23(2):472–477
Chen X, Ishwaran H (2012) Random forests for genomic data analysis. Genomics 99(6):323–329
Tesfamariam S, Liu Z (2010) Earthquake induced damage classification for reinforced concrete buildings. Struct Saf 32(2):154–164
Deng H, Runger G (2013) Gene selection with guided regularized random forest. Pattern Recogn 46(12):3483–3489
Immitzer M, Atzberger C, Koukal T (2012) Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sens 4(9):2661–2693
Mihailescu DM et al (2013) Computer aided diagnosis method for steatosis rating in ultrasound images using random forests. Med Ultrason 15(3):184
Zhao T et al (2012) Predict seasonal low flows in the upper Yangtze River using random forests model. J Hydroelectr Eng 31(3):18–38
Wang Z et al (2015) Flood hazard risk assessment model based on random forest. J Hydrol 527:1130–1141
You K, Yuan-fang C, Sheng-hua G (2014) Assessment of sustainable utilization of regional water resources based on random forest. Water Resour Power 32(3):34–38
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Fang K et al (2011) A review of technologies on random forests. Stat Info Forum 26(3):32–38
Peters J et al (2007) Random forests as a tool for ecohydrological distribution modelling. Ecol Model 207(2–4):304–318
Breiman L (2002) Manual on setting up, using, and understanding random forests v3. 1. Statistics Department University of California Berkeley, CA, USA, p 1
Li X, Su H, Hu J (2017) The prediction model of dam uplift pressure based on random forest. Mater Sci Eng 229(1):012025
Saouma V, Hansen E, Rajagopalan B (2001) Statistical and 3d nonlinear finite element analysis of Schlegeis dam. In: Proceedings of the sixth ICOLD benchmark workshop on numerical analysis of dams. Salzburg, Austria
Acknowledgements
This research has been partially supported by the National Key Research and Development Program of China (SN: 2018YFC0407101, 2016YFC0401601, 2017YFC0804607), National Natural Science Foundation of China (SN: 51739003, 51579083, 51479054), Key R&D Program of Guangxi (SN: AB17195074), Open Foundation of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (SN: 20165042112, 20145027612), the Fundamental Research Funds for the Central Universities (SN: 2018B40514, 2015B25414).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, X., Wen, Z. & Su, H. An approach using random forest intelligent algorithm to construct a monitoring model for dam safety. Engineering with Computers 37, 39–56 (2021). https://doi.org/10.1007/s00366-019-00806-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00366-019-00806-0