Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A multisource data‐driven monitoring model for assessing concrete dam behavior
The pivotal role of dam infrastructure necessitates continuous health monitoring, which results in extensive sets of data. Most monitoring data‐based models in dam engineering concentrate on predicting dam behavior. However, little attention has been systematically paid to the processing of extensive monitoring data, modeling of comprehensive dam behavior, and assessment of overall dam operation status. Here, we propose a novel monitoring model comprising three main aspects: a multidimensional data mining method, a multipoint response prediction method, and a multilayer data fusion‐based assessment method. Utilizing monitoring data from a mega concrete arch dam, we evaluate and discuss the effects of data mining, modeling accuracy for dam behavior, robustness against data pollution, and sensitivity to anomalies. Comparisons with classical benchmarks demonstrate the performance of the proposed model for the dam.
A multisource data‐driven monitoring model for assessing concrete dam behavior
The pivotal role of dam infrastructure necessitates continuous health monitoring, which results in extensive sets of data. Most monitoring data‐based models in dam engineering concentrate on predicting dam behavior. However, little attention has been systematically paid to the processing of extensive monitoring data, modeling of comprehensive dam behavior, and assessment of overall dam operation status. Here, we propose a novel monitoring model comprising three main aspects: a multidimensional data mining method, a multipoint response prediction method, and a multilayer data fusion‐based assessment method. Utilizing monitoring data from a mega concrete arch dam, we evaluate and discuss the effects of data mining, modeling accuracy for dam behavior, robustness against data pollution, and sensitivity to anomalies. Comparisons with classical benchmarks demonstrate the performance of the proposed model for the dam.
A multisource data‐driven monitoring model for assessing concrete dam behavior
Yao, Kefu (Autor:in) / Wen, Zhiping (Autor:in) / Shao, Chenfei (Autor:in) / Yang, Jiaquan (Autor:in) / Su, Huaizhi (Autor:in)
Computer‐Aided Civil and Infrastructure Engineering ; 39 ; 3595-3609
01.12.2024
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Assessing the Classification Accuracy of Multisource Remote Sensing Data
| Online Contents | 1994