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European Journal of Environmental and Civil Engineering Volume 25, 2021 - Issue 9
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Original Articles

Artificial neural network prediction models of heavy metal polluted soil resistivity

Ya Chua Institute of Geotechnical Engineering, Jiangsu Key Laboratory of Urban Underground Engineering and Environmental Safety, Southeast University, Nanjing, Jiangsu, China;b Department of Civil Architectural and Environment Engineering, Missouri University of Science and Technology, Rolla, MO, USAView further author information
,
Songyu Liua Institute of Geotechnical Engineering, Jiangsu Key Laboratory of Urban Underground Engineering and Environmental Safety, Southeast University, Nanjing, Jiangsu, ChinaCorrespondence[email protected]
View further author information
,
Guojun Caia Institute of Geotechnical Engineering, Jiangsu Key Laboratory of Urban Underground Engineering and Environmental Safety, Southeast University, Nanjing, Jiangsu, ChinaView further author information
&
Hanliang Bianc Department of Civil and Architectural Engineering, Henan University, Kaifeng, ChinaView further author information
Pages 1570-1590 | Received 16 Aug 2015, Accepted 18 Feb 2019, Published online: 05 Apr 2019

Citations (7)

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Read on this site (2)

Gholamreza Pazouki & Arash Pourghorban. (2022) Using a hybrid artificial intelligence method for estimating the compressive strength of recycled aggregate self-compacting concrete. European Journal of Environmental and Civil Engineering 26:12, pages 5569-5593.
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Ahmad Bazoobandi, Samad Emamgholizadeh & Hadi Ghorbani. (2022) Estimating the amount of cadmium and lead in the polluted soil using artificial intelligence models. European Journal of Environmental and Civil Engineering 26:3, pages 933-951.
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Articles from other publishers (5)

Deliang Ma, Yifei Xie, Tianyang Lan, Jianmei Chang & Huaiping Feng. (2022) Local moisture monitoring of unsaturated triaxial silty sample by a four-probe method based on van der Pauw configuration. Acta Geotechnica 18:5, pages 2813-2825.
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Abir Sarkar & Debjit Bhowmik. 2023. Recent Trends in Civil Engineering. Recent Trends in Civil Engineering 515 539 .
Gholamreza Pazouki. (2022) Fly ash-based geopolymer concrete's compressive strength estimation by applying artificial intelligence methods. Measurement 203, pages 111916.
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Neda Halalsheh, Odey Alshboul, Ali Shehadeh, Rabia Emhamed Al Mamlook, Amani Al-Othman, Muhammad Tawalbeh, Ali Saeed Almuflih & Charalambos Papelis. (2022) Breakthrough Curves Prediction of Selenite Adsorption on Chemically Modified Zeolite Using Boosted Decision Tree Algorithms for Water Treatment Applications. Water 14:16, pages 2519.
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Ahmed M. Ebid. (2020) 35 Years of (AI) in Geotechnical Engineering: State of the Art. Geotechnical and Geological Engineering 39:2, pages 637-690.
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