{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T01:13:46Z","timestamp":1772500426930,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T00:00:00Z","timestamp":1530835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1709216"],"award-info":[{"award-number":["U1709216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51608478"],"award-info":[{"award-number":["51608478"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51522811"],"award-info":[{"award-number":["51522811"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51478429"],"award-info":[{"award-number":["51478429"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["50908202"],"award-info":[{"award-number":["50908202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ15E080008"],"award-info":[{"award-number":["LQ15E080008"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2015XZZX004-28"],"award-info":[{"award-number":["2015XZZX004-28"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Key Laboratory of Space Structures","award":["2018"],"award-info":[{"award-number":["2018"]}]},{"name":"Hangzhou Major Science and Technology Plan Project","award":["No. 20172016A06"],"award-info":[{"award-number":["No. 20172016A06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Techniques based on the elasto-magnetic (EM) effect have been receiving increasing attention for their significant advantages in cable stress\/force monitoring of in-service structures. Variations in ambient temperature affect the magnetic behaviors of steel components, causing errors in the sensor and measurement system results. Therefore, temperature compensation is essential. In this paper, the effect of temperature on the force monitoring of steel cables using smart elasto-magneto-electric (EME) sensors was investigated experimentally. A back propagation (BP) neural network method is proposed to obtain a direct readout of the applied force in the engineering environment, involving less computational complexity. On the basis of the data measured in the experiment, an improved BP neural network model was established. The test result shows that, over a temperature range of approximately \u221210 \u00b0C to 60 \u00b0C, the maximum relative error in the force measurement is within \u00b10.9%. A polynomial fitting method was also implemented for comparison. It is concluded that the method based on a BP neural network can be more reliable, effective and robust, and can be extended to temperature compensation of other similar sensors.<\/jats:p>","DOI":"10.3390\/s18072176","type":"journal-article","created":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T10:55:44Z","timestamp":1530874544000},"page":"2176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Temperature Compensation of Elasto-Magneto-Electric (EME) Sensors in Cable Force Monitoring Using BP Neural Network"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4611-8728","authenticated-orcid":false,"given":"Ru","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University City College, Hangzhou 310015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanfeng","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University City College, Hangzhou 310015, China"},{"name":"College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"He","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University City College, Hangzhou 310015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Deng, Y., Liu, Y., and Chen, S. 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