Sökning: onr:"swepub:oai:DiVA.org:ltu-77620" > A Hybrid Intelligen...
Fältnamn | Indikatorer | Metadata |
---|---|---|
000 | 05095naa a2200517 4500 | |
001 | oai:DiVA.org:ltu-77620 | |
003 | SwePub | |
008 | 200203s2020 | |||||||||||000 ||eng| | |
024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-776202 URI |
024 | 7 | a https://doi.org/10.3390/su120310632 DOI |
040 | a (SwePub)ltu | |
041 | a engb eng | |
042 | 9 SwePub | |
072 | 7 | a ref2 swepub-contenttype |
072 | 7 | a art2 swepub-publicationtype |
100 | 1 | 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 |
245 | 1 0 | a A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers |
264 | c 2020-02-03 | |
264 | 1 | a 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 | 7 | a TEKNIK OCH TEKNOLOGIERx Samhällsbyggnadsteknikx Geoteknik0 (SwePub)201062 hsv//swe |
650 | 7 | a 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 | |
700 | 1 | a Shirzadi, Ataollahu Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran4 aut |
700 | 1 | a Amini, Atau Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran4 aut |
700 | 1 | 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 |
700 | 1 | a Al-Ansari, Nadhir,d 1947-u Luleå tekniska universitet,Geoteknologi4 aut0 (Swepub:ltu)nadhir |
700 | 1 | a Hamidi, Shahriaru Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran4 aut |
700 | 1 | a Singh, Sushant K.u Department of Health, Insurance & Life Sciences, Data & Analytics, Virtusa Corporation, Irvington, NJ, USA4 aut |
700 | 1 | a Pham, Binh Thaiu Institute of Research and Development, Duy Tan University, Da Nang, Vietnam4 aut |
700 | 1 | a Ahmad, Baharin Binu Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia4 aut |
700 | 1 | a Ghazvinei, Pezhman Tahereiu Department of Civil Engineering, Technical and Engineering College, Ale Taha University, Tehran, Iran4 aut |
710 | 2 | 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 |
773 | 0 | t Sustainabilityd Switzerland : MDPIg 12:3, s. 1-24q 12:3<1-24x 2071-1050 |
856 | 4 | u https://ltu.diva-portal.org/smash/get/diva2:1390920/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print |
856 | 4 | u https://www.mdpi.com/2071-1050/12/3/1063/pdf |
856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77620 |
856 | 4 8 | u https://doi.org/10.3390/su12031063 |
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
Kopiera och spara länken för att återkomma till aktuell vy