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WFRF:(Shahabi Himan)
 

Sökning: WFRF:(Shahabi Himan) > A Hybrid Intelligen...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00005095naa a2200517 4500
001oai:DiVA.org:ltu-77620
003SwePub
008200203s2020 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-776202 URI
0247 a https://doi.org/10.3390/su120310632 DOI
040 a (SwePub)ltu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Bui, Dieu Tienu Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam4 aut
2451 0a A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers
264 c 2020-02-03
264 1a Switzerland :b MDPI,c 2020
338 a electronic2 rdacarrier
500 a Validerad;2020;Nivå 2;2020-02-04 (johcin)
520 a Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.
650 7a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Geoteknik0 (SwePub)201062 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Civil Engineeringx Geotechnical Engineering0 (SwePub)201062 hsv//eng
653 a scour depth
653 a complex piers
653 a pile cap
653 a machine learning algorithms
653 a ensemble models
653 a Soil Mechanics
653 a Geoteknik
7001 a Shirzadi, Ataollahu Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran4 aut
7001 a Amini, Atau Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran4 aut
7001 a Shahabi, Himanu Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj , Iran. Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan4 aut
7001 a Al-Ansari, Nadhir,d 1947-u Luleå tekniska universitet,Geoteknologi4 aut0 (Swepub:ltu)nadhir
7001 a Hamidi, Shahriaru Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran4 aut
7001 a Singh, Sushant K.u Department of Health, Insurance & Life Sciences, Data & Analytics, Virtusa Corporation, Irvington, NJ, USA4 aut
7001 a Pham, Binh Thaiu Institute of Research and Development, Duy Tan University, Da Nang, Vietnam4 aut
7001 a Ahmad, Baharin Binu Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia4 aut
7001 a Ghazvinei, Pezhman Tahereiu Department of Civil Engineering, Technical and Engineering College, Ale Taha University, Tehran, Iran4 aut
7102 a Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnamb Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran4 org
7730 t Sustainabilityd Switzerland : MDPIg 12:3, s. 1-24q 12:3<1-24x 2071-1050
8564 u https://ltu.diva-portal.org/smash/get/diva2:1390920/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 u https://www.mdpi.com/2071-1050/12/3/1063/pdf
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77620
8564 8u https://doi.org/10.3390/su12031063

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