Service-Life Study of Polycarbonate Outdoors Using Python with Incomplete Data
The deterioration of polycarbonate (PC) depends on various environmental factors. Meanwhile, the complexity of the related weathering processes inhibits the prediction of service life based on the environmental factors. To elucidate the nonlinear correlation between PC weathering and the environment...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8909747 |
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| _version_ | 1849397748945649664 |
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| author | Jiangfeng An Duncheng Peng Xuejie Zhou Jun Wu Penghua Zheng |
| author_facet | Jiangfeng An Duncheng Peng Xuejie Zhou Jun Wu Penghua Zheng |
| author_sort | Jiangfeng An |
| collection | DOAJ |
| description | The deterioration of polycarbonate (PC) depends on various environmental factors. Meanwhile, the complexity of the related weathering processes inhibits the prediction of service life based on the environmental factors. To elucidate the nonlinear correlation between PC weathering and the environmental factors, three-year-long natural weathering tests were conducted at eight experimental stations in China. The relationship between tensile-property data of PC and environmental and pollutant data is analyzed by extra-trees and multilayer perceptron networks implemented in Python. The results indicated that (1) the degradation of PC tensile properties is mainly affected by the experimental period (76.37%), whilst the effect of the environmental or pollutant factors on the degradation is less pronounced (23.63%); (2) the classification accuracy of the trained model on the training set is 91% (91/100), and on the testing set is 72.13% (44/61); and lastly, (3) it is inferred from the error analysis of the classification results that the performance change of polycarbonate in Qionghai and Wuhan is characterized by an initial reduction followed by a slight improvement. Lastly, we show that the proposed method performs well, especially in the case of areas with incomplete data available. |
| format | Article |
| id | doaj-art-a89a2e3c4dc2478f92ae69b04796d4f4 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-a89a2e3c4dc2478f92ae69b04796d4f42025-08-20T03:38:54ZengWileyModelling and Simulation in Engineering1687-55911687-56052020-01-01202010.1155/2020/89097478909747Service-Life Study of Polycarbonate Outdoors Using Python with Incomplete DataJiangfeng An0Duncheng Peng1Xuejie Zhou2Jun Wu3Penghua Zheng4Wuhan Research Institute of Materials Protection Co., Ltd, 126 Bao Feng Erlu, Wuhan, Hubei 430030, ChinaWuhan Research Institute of Materials Protection Co., Ltd, 126 Bao Feng Erlu, Wuhan, Hubei 430030, ChinaState Key Laboratory of Special Surface Protection Materials and Applied Technology, 126 Bao Feng Erlu, Wuhan, Hubei 430030, ChinaState Key Laboratory of Special Surface Protection Materials and Applied Technology, 126 Bao Feng Erlu, Wuhan, Hubei 430030, ChinaState Key Laboratory of Special Surface Protection Materials and Applied Technology, 126 Bao Feng Erlu, Wuhan, Hubei 430030, ChinaThe deterioration of polycarbonate (PC) depends on various environmental factors. Meanwhile, the complexity of the related weathering processes inhibits the prediction of service life based on the environmental factors. To elucidate the nonlinear correlation between PC weathering and the environmental factors, three-year-long natural weathering tests were conducted at eight experimental stations in China. The relationship between tensile-property data of PC and environmental and pollutant data is analyzed by extra-trees and multilayer perceptron networks implemented in Python. The results indicated that (1) the degradation of PC tensile properties is mainly affected by the experimental period (76.37%), whilst the effect of the environmental or pollutant factors on the degradation is less pronounced (23.63%); (2) the classification accuracy of the trained model on the training set is 91% (91/100), and on the testing set is 72.13% (44/61); and lastly, (3) it is inferred from the error analysis of the classification results that the performance change of polycarbonate in Qionghai and Wuhan is characterized by an initial reduction followed by a slight improvement. Lastly, we show that the proposed method performs well, especially in the case of areas with incomplete data available.http://dx.doi.org/10.1155/2020/8909747 |
| spellingShingle | Jiangfeng An Duncheng Peng Xuejie Zhou Jun Wu Penghua Zheng Service-Life Study of Polycarbonate Outdoors Using Python with Incomplete Data Modelling and Simulation in Engineering |
| title | Service-Life Study of Polycarbonate Outdoors Using Python with Incomplete Data |
| title_full | Service-Life Study of Polycarbonate Outdoors Using Python with Incomplete Data |
| title_fullStr | Service-Life Study of Polycarbonate Outdoors Using Python with Incomplete Data |
| title_full_unstemmed | Service-Life Study of Polycarbonate Outdoors Using Python with Incomplete Data |
| title_short | Service-Life Study of Polycarbonate Outdoors Using Python with Incomplete Data |
| title_sort | service life study of polycarbonate outdoors using python with incomplete data |
| url | http://dx.doi.org/10.1155/2020/8909747 |
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