{"title":"五种温度系数叠加的高拱坝位移预测组合模型","authors":"Bingao Chai, Shaowei Wang","doi":"10.1177/01423312241262229","DOIUrl":null,"url":null,"abstract":"The statically indeterminate characteristics of arch dams highlight the temperature deformation effect, making accurate modelling of this effect a key issue in improving the performance of displacement monitoring models. In this paper, causal interpretation ability and prediction accuracy of five kinds of temperature deformation modelling factors, including seasonal harmonic function, segmented average previous air temperature, air temperature hysteresis correction factor, principal components and shape feature clustering-based principal components of measured dam temperatures, are compared. On this basis, a combination prediction model is established using the above five causal models as submodels. The combination process is conducted by three methods of dynamic mutual information coefficient, random forest and support vector machine. Research results of the Jinping-I arch dam show that the shape feature clustering-based temperature principal components can significantly improve the accuracy and adaptability of displacement monitoring models, in which the root mean square error decreases with an average rate of 52%. The combination prediction model can effectively take the advantages of different kinds of temperature deformation modelling factors into account. Compared with the hydraulic-seasonal-time model and the best submodel, prediction accuracy of the support vector machine-based combination model is improved with an average rate of 54% and 28%, respectively.","PeriodicalId":49426,"journal":{"name":"Transactions of the Institute of Measurement and Control","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A combination model for displacement prediction of high arch dams stacking five kinds of temperature factors\",\"authors\":\"Bingao Chai, Shaowei Wang\",\"doi\":\"10.1177/01423312241262229\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The statically indeterminate characteristics of arch dams highlight the temperature deformation effect, making accurate modelling of this effect a key issue in improving the performance of displacement monitoring models. In this paper, causal interpretation ability and prediction accuracy of five kinds of temperature deformation modelling factors, including seasonal harmonic function, segmented average previous air temperature, air temperature hysteresis correction factor, principal components and shape feature clustering-based principal components of measured dam temperatures, are compared. On this basis, a combination prediction model is established using the above five causal models as submodels. The combination process is conducted by three methods of dynamic mutual information coefficient, random forest and support vector machine. Research results of the Jinping-I arch dam show that the shape feature clustering-based temperature principal components can significantly improve the accuracy and adaptability of displacement monitoring models, in which the root mean square error decreases with an average rate of 52%. The combination prediction model can effectively take the advantages of different kinds of temperature deformation modelling factors into account. Compared with the hydraulic-seasonal-time model and the best submodel, prediction accuracy of the support vector machine-based combination model is improved with an average rate of 54% and 28%, respectively.\",\"PeriodicalId\":49426,\"journal\":{\"name\":\"Transactions of the Institute of Measurement and Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions of the Institute of Measurement and Control\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1177/01423312241262229\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of the Institute of Measurement and Control","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/01423312241262229","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
A combination model for displacement prediction of high arch dams stacking five kinds of temperature factors
The statically indeterminate characteristics of arch dams highlight the temperature deformation effect, making accurate modelling of this effect a key issue in improving the performance of displacement monitoring models. In this paper, causal interpretation ability and prediction accuracy of five kinds of temperature deformation modelling factors, including seasonal harmonic function, segmented average previous air temperature, air temperature hysteresis correction factor, principal components and shape feature clustering-based principal components of measured dam temperatures, are compared. On this basis, a combination prediction model is established using the above five causal models as submodels. The combination process is conducted by three methods of dynamic mutual information coefficient, random forest and support vector machine. Research results of the Jinping-I arch dam show that the shape feature clustering-based temperature principal components can significantly improve the accuracy and adaptability of displacement monitoring models, in which the root mean square error decreases with an average rate of 52%. The combination prediction model can effectively take the advantages of different kinds of temperature deformation modelling factors into account. Compared with the hydraulic-seasonal-time model and the best submodel, prediction accuracy of the support vector machine-based combination model is improved with an average rate of 54% and 28%, respectively.
期刊介绍:
Transactions of the Institute of Measurement and Control is a fully peer-reviewed international journal. The journal covers all areas of applications in instrumentation and control. Its scope encompasses cutting-edge research and development, education and industrial applications.