{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:49:25Z","timestamp":1757310565927,"version":"3.41.2"},"reference-count":56,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"vor","delay-in-days":228,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Project cost prediction is one of the key elements in the civil engineering activities development. Project cost is a highly sensitive component to diverse parameters and hence it is associated with complex trends that make it difficult to be predicted and fully understood. Due to the massive advancement of soft computing (SC) and Internet of things (IoT), the main research objective of the current study was initiative. Several machine learning (ML) models including extreme learning machine (ELM), multivariate adaptive regression spline (MARS), and partial least square regression (PLS) were adopted to predict field canal cost. Several essential predictors were used to develop the prediction network \u201cthe learning process\u201d including the total length of the PVC pipeline, served area, geographical zone, construction year, and cost and duration of field canal improvement projects (FCIP) construction. Data were collected from the open source published literature. The modeling results evidenced the potential of the applied SC models in predicting the FCIP cost. In numerical magnitude evaluation, MARS model indicated the least value for the root mean square error (RMSE\u2009=\u200927422.7), mean absolute error (MAE\u2009=\u200919761.8), and mean absolute percentage error (MAPE\u2009=\u20090.05454) with Nash\u2013Sutcliffe efficiency (NSE\u2009=\u20090.94), agreement index (MD\u2009=\u20090.89), and coefficient of determination (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>\u2009=\u20090.94), with best precision of prediction using all predictors, except geographical zone parameter in which less influence on the cost construction is presented. In general, the research outcome gave an informative primary cost initiative for cost civil engineering project.<\/jats:p>","DOI":"10.1155\/2021\/8324272","type":"journal-article","created":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T18:22:48Z","timestamp":1629224568000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evaluation of Several Machine Learning Models for Field Canal Improvement Project Cost Prediction"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6309-9373","authenticated-orcid":false,"given":"Saadi","family":"Shartooh Sharqi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2079-7099","authenticated-orcid":false,"given":"Aayush","family":"Bhattarai","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_10_1_2","DOI":"10.1177\/107049659800700304"},{"doi-asserted-by":"publisher","key":"e_1_2_10_2_2","DOI":"10.1007\/978-3-030-60551-3_1"},{"doi-asserted-by":"publisher","key":"e_1_2_10_3_2","DOI":"10.2166\/wh.2020.216"},{"doi-asserted-by":"publisher","key":"e_1_2_10_4_2","DOI":"10.1007\/978-3-319-44234-1"},{"doi-asserted-by":"publisher","key":"e_1_2_10_5_2","DOI":"10.1061\/(asce)co.1943-7862.0001561"},{"doi-asserted-by":"publisher","key":"e_1_2_10_6_2","DOI":"10.1061\/(asce)co.1943-7862.0001678"},{"doi-asserted-by":"publisher","key":"e_1_2_10_7_2","DOI":"10.1016\/j.dib.2020.105688"},{"key":"e_1_2_10_8_2","first-page":"1","article-title":"Sensitivity analysis of head loss equations on the design of improved irrigation on-farm system in Egypt","volume":"2","author":"Radwan H. G.","year":"2013","journal-title":"International Journal of Advanced Research in Science"},{"key":"e_1_2_10_9_2","first-page":"1","article-title":"Challenges in cost estimating with building information modeling","author":"Sabol L.","year":"2008","journal-title":"IFMA World Work"},{"doi-asserted-by":"publisher","key":"e_1_2_10_10_2","DOI":"10.14716\/ijtech.v5i2.402"},{"doi-asserted-by":"publisher","key":"e_1_2_10_11_2","DOI":"10.3846\/1392-3730.2009.15.405-409"},{"doi-asserted-by":"publisher","key":"e_1_2_10_12_2","DOI":"10.1139\/l11-016"},{"key":"e_1_2_10_13_2","article-title":"Elements of cost estimation: a survey in Canada and the unit","volume":"37","author":"Hegazy T.","year":"1995","journal-title":"Cost Engineering"},{"key":"e_1_2_10_14_2","first-page":"41","article-title":"Investigation and evaluation of the cost estimation methods of Iraqi communication projects","volume":"5","author":"Al-Zwainy F. M. S.","year":"2015","journal-title":"International Journal of Engineering and Management Research"},{"doi-asserted-by":"publisher","key":"e_1_2_10_15_2","DOI":"10.1061\/(asce)co.1943-7862.0000668"},{"doi-asserted-by":"publisher","key":"e_1_2_10_16_2","DOI":"10.1061\/(asce)0733-9364(1998)124:3(210)"},{"doi-asserted-by":"publisher","key":"e_1_2_10_17_2","DOI":"10.1016\/j.jare.2011.01.007"},{"key":"e_1_2_10_18_2","article-title":"Modelling the parametric construction project cost estimate using fuzzy logic","volume":"2","author":"El-Sawalhi N. I.","year":"2012","journal-title":"International Journal of Emerging Technology and Advanced Engineering"},{"unstructured":"Ahiaga-DagbuiD. D. TokedeO. SmithS. D. andWamuziriS. A neuro-fuzzy hybrid model for predicting final cost of water infrastructure projects Proceedings of the 29th Annual ARCOM Conference September 2013 Cambridge UK SD Smith DD Ahiaga-Dagbui 181\u2013190.","key":"e_1_2_10_19_2"},{"key":"e_1_2_10_20_2","first-page":"56","article-title":"Conceptual cost estimate of Libyan highway projects using artificial neural network","volume":"4","author":"Elbeltagi E.","year":"2014","journal-title":"International Journal of Engineering Research in Africa"},{"doi-asserted-by":"publisher","key":"e_1_2_10_21_2","DOI":"10.1504\/ijpom.2012.045363"},{"doi-asserted-by":"publisher","key":"e_1_2_10_22_2","DOI":"10.1061\/(asce)co.1943-7862.0000479"},{"unstructured":"RoxasC. L. C.andOngpengJ. M. C. An artificial neural network approach to structural cost estimation of building projects in the Philippines Proceedings of the DLSU Research Congress February 2014 Manila Philippines.","key":"e_1_2_10_23_2"},{"doi-asserted-by":"publisher","key":"e_1_2_10_24_2","DOI":"10.1155\/2017\/2450370"},{"doi-asserted-by":"publisher","key":"e_1_2_10_25_2","DOI":"10.1108\/bepam-11-2017-0111"},{"doi-asserted-by":"publisher","key":"e_1_2_10_26_2","DOI":"10.1061\/(asce)cp.1943-5487.0000054"},{"doi-asserted-by":"publisher","key":"e_1_2_10_27_2","DOI":"10.1007\/s00521-020-05006-2"},{"doi-asserted-by":"publisher","key":"e_1_2_10_28_2","DOI":"10.1109\/TEM.2020.2972078"},{"doi-asserted-by":"publisher","key":"e_1_2_10_29_2","DOI":"10.1007\/s42452-020-03497-1"},{"doi-asserted-by":"publisher","key":"e_1_2_10_30_2","DOI":"10.1207\/s15327906mbr2603_7"},{"doi-asserted-by":"publisher","key":"e_1_2_10_31_2","DOI":"10.1007\/s13042-011-0019-y"},{"doi-asserted-by":"publisher","key":"e_1_2_10_32_2","DOI":"10.1109\/TSMCB.2011.2168604"},{"doi-asserted-by":"publisher","key":"e_1_2_10_33_2","DOI":"10.1016\/j.neucom.2010.12.042"},{"doi-asserted-by":"publisher","key":"e_1_2_10_34_2","DOI":"10.1016\/j.gsf.2014.10.003"},{"doi-asserted-by":"publisher","key":"e_1_2_10_35_2","DOI":"10.1109\/TNN.2006.880583"},{"doi-asserted-by":"publisher","key":"e_1_2_10_36_2","DOI":"10.3390\/en11123415"},{"doi-asserted-by":"publisher","key":"e_1_2_10_37_2","DOI":"10.1109\/TNNLS.2016.2607757"},{"doi-asserted-by":"publisher","key":"e_1_2_10_38_2","DOI":"10.1016\/j.neunet.2014.10.001"},{"doi-asserted-by":"publisher","key":"e_1_2_10_39_2","DOI":"10.1016\/j.rser.2017.05.249"},{"doi-asserted-by":"publisher","key":"e_1_2_10_40_2","DOI":"10.1016\/j.rser.2019.01.014"},{"doi-asserted-by":"publisher","key":"e_1_2_10_41_2","DOI":"10.1214\/aos\/1176347963"},{"doi-asserted-by":"publisher","key":"e_1_2_10_42_2","DOI":"10.1007\/978-3-319-04849-9_40"},{"doi-asserted-by":"publisher","key":"e_1_2_10_43_2","DOI":"10.1016\/j.energy.2021.120090"},{"doi-asserted-by":"publisher","key":"e_1_2_10_44_2","DOI":"10.1016\/j.soildyn.2020.106097"},{"doi-asserted-by":"publisher","key":"e_1_2_10_45_2","DOI":"10.1016\/j.chemolab.2004.02.007"},{"doi-asserted-by":"publisher","key":"e_1_2_10_46_2","DOI":"10.1016\/j.engstruct.2019.05.048"},{"doi-asserted-by":"publisher","key":"e_1_2_10_47_2","DOI":"10.1007\/s10064-020-01730-0"},{"doi-asserted-by":"publisher","key":"e_1_2_10_48_2","DOI":"10.1016\/j.atmosres.2016.10.004"},{"key":"e_1_2_10_49_2","first-page":"792","article-title":"Partial least square regression (PLS regression)","volume":"6","author":"Abdi H.","year":"2003","journal-title":"Encyclopedia of Social Science Research Methods"},{"doi-asserted-by":"publisher","key":"e_1_2_10_50_2","DOI":"10.1007\/978-1-62703-059-5_23"},{"doi-asserted-by":"publisher","key":"e_1_2_10_51_2","DOI":"10.1002\/(sici)1097-0266(199902)20:2<195::aid-smj13>3.0.co;2-7"},{"doi-asserted-by":"publisher","key":"e_1_2_10_52_2","DOI":"10.1016\/j.pursup.2015.04.005"},{"doi-asserted-by":"publisher","key":"e_1_2_10_53_2","DOI":"10.1111\/deci.12445"},{"doi-asserted-by":"publisher","key":"e_1_2_10_54_2","DOI":"10.1111\/bjet.12890"},{"key":"e_1_2_10_55_2","article-title":"Probability and Statistics for Engineers and Scientist","author":"Walpole R.","year":"2011","journal-title":"Pearson"},{"doi-asserted-by":"publisher","key":"e_1_2_10_56_2","DOI":"10.1016\/j.jhydrol.2009.08.003"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/8324272.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/8324272.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/8324272","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T22:25:34Z","timestamp":1723242334000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/8324272"}},"subtitle":[],"editor":[{"given":"Mostafa","family":"Al-Emran","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/8324272"],"URL":"https:\/\/doi.org\/10.1155\/2021\/8324272","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"type":"print","value":"1076-2787"},{"type":"electronic","value":"1099-0526"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-06-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8324272"}}