Application of Wavelet Neural Network in Building Settlement Prediction
Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But t...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2020-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/58/e3sconf_isceg2020_03014.pdf |
| _version_ | 1829482045931257856 |
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| author | Zhang Ruijie |
| author_facet | Zhang Ruijie |
| author_sort | Zhang Ruijie |
| collection | DOAJ |
| description | Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential. |
| first_indexed | 2024-12-14T21:40:07Z |
| format | Article |
| id | doaj.art-b8e6047bb1c543219473930f907b6e6a |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-14T21:40:07Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-b8e6047bb1c543219473930f907b6e6a2022-12-21T22:46:29ZengEDP SciencesE3S Web of Conferences2267-12422020-01-011980301410.1051/e3sconf/202019803014e3sconf_isceg2020_03014Application of Wavelet Neural Network in Building Settlement PredictionZhang Ruijie0845 Rue Sherbrooke Ouest, Civil Engineering, McGill University, MontrealDeformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/58/e3sconf_isceg2020_03014.pdf |
| spellingShingle | Zhang Ruijie Application of Wavelet Neural Network in Building Settlement Prediction E3S Web of Conferences |
| title | Application of Wavelet Neural Network in Building Settlement Prediction |
| title_full | Application of Wavelet Neural Network in Building Settlement Prediction |
| title_fullStr | Application of Wavelet Neural Network in Building Settlement Prediction |
| title_full_unstemmed | Application of Wavelet Neural Network in Building Settlement Prediction |
| title_short | Application of Wavelet Neural Network in Building Settlement Prediction |
| title_sort | application of wavelet neural network in building settlement prediction |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/58/e3sconf_isceg2020_03014.pdf |
| work_keys_str_mv | AT zhangruijie applicationofwaveletneuralnetworkinbuildingsettlementprediction |