Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test Database
The quality of in situ data is key to calculating resistance factor of bored piles. However, it is difficult to summarize accuracy data due to various uncertainties in engineering. This paper employs the Bayesian method and mathematical statistics theory to put forward an estimation method for updat...
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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: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/2763863 |
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| author | Zhijun Xu Ranran Zhang Liang Fan Xing Han Fang Yuan Mingfang Du |
| author_facet | Zhijun Xu Ranran Zhang Liang Fan Xing Han Fang Yuan Mingfang Du |
| author_sort | Zhijun Xu |
| collection | DOAJ |
| description | The quality of in situ data is key to calculating resistance factor of bored piles. However, it is difficult to summarize accuracy data due to various uncertainties in engineering. This paper employs the Bayesian method and mathematical statistics theory to put forward an estimation method for updating in situ data. A testing database (33 tests in noncohesive soils and 53 tests in cohesive soils) of bored piles is summarized. The model factor of bored piles is quantified as the ratio of the measured capacity to the calculated capacity. The proposed method is used to classify summarized data into three categories, which are “good data,” “general data,” and “bad data.” The “bad data” are discarded because of bad contribution to calculation, and Bayesian theory is incorporated into updating the model factor statistics. Three methods are used to calculate the reliability index and resistance factor of bored piles, and the results show that the reliability index and resistance factor are sensitive to the quality of data. Finally, the available values of resistance factors are proposed based on resistance factor design for bridge design specification, which can offer references to revision relevant specifications. The proposed method can be used to update other geotechnical data. |
| format | Article |
| id | doaj-art-fc09907129674872b9a666b261d74288 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-fc09907129674872b9a666b261d742882025-02-03T06:06:34ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/27638632763863Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test DatabaseZhijun Xu0Ranran Zhang1Liang Fan2Xing Han3Fang Yuan4Mingfang Du5College of Civil Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Civil Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Civil Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Civil Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Civil Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Civil Engineering, Henan University of Technology, Zhengzhou 450001, ChinaThe quality of in situ data is key to calculating resistance factor of bored piles. However, it is difficult to summarize accuracy data due to various uncertainties in engineering. This paper employs the Bayesian method and mathematical statistics theory to put forward an estimation method for updating in situ data. A testing database (33 tests in noncohesive soils and 53 tests in cohesive soils) of bored piles is summarized. The model factor of bored piles is quantified as the ratio of the measured capacity to the calculated capacity. The proposed method is used to classify summarized data into three categories, which are “good data,” “general data,” and “bad data.” The “bad data” are discarded because of bad contribution to calculation, and Bayesian theory is incorporated into updating the model factor statistics. Three methods are used to calculate the reliability index and resistance factor of bored piles, and the results show that the reliability index and resistance factor are sensitive to the quality of data. Finally, the available values of resistance factors are proposed based on resistance factor design for bridge design specification, which can offer references to revision relevant specifications. The proposed method can be used to update other geotechnical data.http://dx.doi.org/10.1155/2020/2763863 |
| spellingShingle | Zhijun Xu Ranran Zhang Liang Fan Xing Han Fang Yuan Mingfang Du Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test Database Advances in Civil Engineering |
| title | Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test Database |
| title_full | Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test Database |
| title_fullStr | Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test Database |
| title_full_unstemmed | Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test Database |
| title_short | Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test Database |
| title_sort | bayesian estimation of resistance factor for bored piles based on load test database |
| url | http://dx.doi.org/10.1155/2020/2763863 |
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