Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study i...

Full description

Saved in:

Bibliographic Details
Published in:	Sustainability Vol. 12; no. 4; p. 1514
Main Authors:	Yaseen, Zaher Mundher, Ali, Zainab Hasan, Salih, Sinan Q., Al-Ansari, Nadhir
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 2020
Subjects:	Accuracy Artificial intelligence Civil engineering computer aid construction project Contractors Decision trees delay sources Genetic algorithms Geoteknik Labor productivity Literature reviews Neural networks Questionnaires random forest‐genetic algorithm Research methodology risk management Soil Mechanics
ISSN:	2071-1050, 2071-1050
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.
AbstractList	Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability. Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.
Author	Yaseen, Zaher Mundher Salih, Sinan Q. Ali, Zainab Hasan Al-Ansari, Nadhir
Author_xml	– sequence: 1 givenname: Zaher Mundher orcidid: 0000-0003-3647-7137 surname: Yaseen fullname: Yaseen, Zaher Mundher – sequence: 2 givenname: Zainab Hasan orcidid: 0000-0003-1916-8164 surname: Ali fullname: Ali, Zainab Hasan – sequence: 3 givenname: Sinan Q. surname: Salih fullname: Salih, Sinan Q. – sequence: 4 givenname: Nadhir orcidid: 0000-0002-6790-2653 surname: Al-Ansari fullname: Al-Ansari, Nadhir
BackLink	https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77758$$DView record from Swedish Publication Index
BookMark	eNptkE9LAzEQxYMoWGsvfoKAN3E12WT_HUurtlCxiPVoyGZnS-qa1CSL9Nu7ZUVFnMsMzG_eG94JOjTWAEJnlFwxVpBr39KYcJpQfoAGMcloRElCDn_Nx2jk_YZ0xRgtaDpAL0sHlVZBW4NtjR-1f8VTaOQOa4Mn1vjg2n67dHYDKni88tqsscSzXel0hccu6ForLRs8NwGaRq_BKMD3toLmFB3VsvEw-upDtLq9eZrMosXD3XwyXkSKsThESZFWCU1JzpQEIKmUUBFaxSklBU94UaaclzXPOEtYlRdQ1onKVMrrmEAh45IN0WWv6z9g25Zi6_SbdDthpRZT_TwW1q1FE1qRZVmSd_h5j2-dfW_BB7GxrTPdhyLmnOVFlnZOQ3TRU8pZ7x3U37KUiH3i4ifxDiZ_YKWD3CcXnNTNfyefD_SEnQ
CitedBy_id	crossref_primary_10_1108_JFM_09_2021_0095 crossref_primary_10_1108_IJBPA_07_2021_0098 crossref_primary_10_3390_buildings12091342 crossref_primary_10_3390_su151511848 crossref_primary_10_1007_s40030_022_00651_3 crossref_primary_10_1016_j_autcon_2025_106470 crossref_primary_10_3390_su13073975 crossref_primary_10_1108_JEDT_03_2024_0186 crossref_primary_10_1108_CI_05_2021_0103 crossref_primary_10_2478_otmcj_2023_0014 crossref_primary_10_3390_modelling6020035 crossref_primary_10_1016_j_jclepro_2022_135522 crossref_primary_10_1007_s13198_024_02653_9 crossref_primary_10_3390_joitmc8010016 crossref_primary_10_2478_cee_2021_0066 crossref_primary_10_1108_ECAM_10_2020_0873 crossref_primary_10_3390_app13085014 crossref_primary_10_1108_IJMPB_08_2023_0180 crossref_primary_10_3390_app12020844 crossref_primary_10_1016_j_jwpe_2025_107251 crossref_primary_10_1061_JCEMD4_COENG_16481 crossref_primary_10_1061_JLADAH_LADR_1276 crossref_primary_10_3390_app14093847 crossref_primary_10_1108_ECAM_04_2021_0287 crossref_primary_10_3390_buildings15030473 crossref_primary_10_3390_machines12120919 crossref_primary_10_1080_15623599_2021_1942769 crossref_primary_10_1109_ACCESS_2024_3438291 crossref_primary_10_1007_s42107_022_00514_z crossref_primary_10_3390_su14020656 crossref_primary_10_1080_10803548_2024_2444807 crossref_primary_10_1080_15623599_2025_2528914 crossref_primary_10_1155_2023_7953967 crossref_primary_10_3390_buildings13112696 crossref_primary_10_3390_met13020232 crossref_primary_10_37648_ijrst_v15i02_003 crossref_primary_10_1016_j_scs_2024_105499 crossref_primary_10_1061_JMENEA_MEENG_6639 crossref_primary_10_1155_2022_9598781 crossref_primary_10_3390_buildings12101660 crossref_primary_10_3390_buildings11020036 crossref_primary_10_3390_su152216071 crossref_primary_10_1016_j_eswa_2024_126268 crossref_primary_10_3390_buildings13010013 crossref_primary_10_3390_informatics9020043 crossref_primary_10_1007_s42107_023_00651_z crossref_primary_10_3390_su142214927 crossref_primary_10_3390_buildings14010220 crossref_primary_10_1080_09537287_2024_2320790 crossref_primary_10_3390_su13105478 crossref_primary_10_1016_j_aei_2025_103511 crossref_primary_10_1109_TEM_2021_3119064 crossref_primary_10_3390_su142416602 crossref_primary_10_3846_btp_2023_18733 crossref_primary_10_1108_USS_11_2024_0069 crossref_primary_10_3390_en17010182 crossref_primary_10_1007_s13132_024_02464_0 crossref_primary_10_1155_2022_4285328 crossref_primary_10_3390_app12115712 crossref_primary_10_1088_1757_899X_928_2_022083 crossref_primary_10_3390_su13052861 crossref_primary_10_1061_JCEMD4_COENG_12367 crossref_primary_10_1108_ECAM_11_2022_1104 crossref_primary_10_1155_2022_8789067 crossref_primary_10_1108_ECAM_09_2024_1209 crossref_primary_10_15446_dyna_v91n231_107967 crossref_primary_10_1016_j_asoc_2025_112890 crossref_primary_10_3390_asi4030052 crossref_primary_10_4018_IJABIM_384773 crossref_primary_10_1155_adce_5517481 crossref_primary_10_3390_buildings13051312 crossref_primary_10_3390_su142214789 crossref_primary_10_3390_su12219143 crossref_primary_10_1007_s42452_025_07078_y crossref_primary_10_1108_JEDT_04_2021_0189 crossref_primary_10_1080_15623599_2024_2304389 crossref_primary_10_3390_buildings12081198 crossref_primary_10_1061__ASCE_CO_1943_7862_0002253 crossref_primary_10_3390_joitmc6020026 crossref_primary_10_3390_joitmc8010045 crossref_primary_10_1016_j_jclepro_2022_134069 crossref_primary_10_3390_buildings13092346
Cites_doi	10.1080/2287108X.2015.1059920 10.1109/ACCESS.2019.2920916 10.1061/(ASCE)CO.1943-7862.0001225 10.1007/978-3-642-12035-0_30 10.1016/j.eswa.2013.12.035 10.7551/mitpress/1090.001.0001 10.1061/41020(339)75 10.3846/13923730.2014.971129 10.1016/S0164-1212(01)00088-7 10.1016/j.autcon.2009.11.016 10.1016/j.buildenv.2003.08.012 10.1061/(ASCE)0733-9364(2009)135:3(169) 10.1016/j.autcon.2014.08.006 10.1016/S0263-7863(02)00028-5 10.1061/(ASCE)CO.1943-7862.0001736 10.1016/j.aei.2016.07.001 10.1007/978-3-319-11017-2 10.1016/S0263-7863(00)00037-5 10.1061/(ASCE)CO.1943-7862.0001322 10.1145/1143844.1143874 10.1007/s11269-017-1660-3 10.1016/j.aej.2013.03.002 10.1016/S0263-7863(98)00060-X 10.1016/j.autcon.2008.11.005 10.3390/su11071953 10.1080/01431161.2011.552923 10.1080/01446193.2010.528437 10.1061/(ASCE)CP.1943-5487.0000197 10.1023/A:1010933404324 10.1016/S0263-7863(99)00072-1 10.1016/j.autcon.2008.04.002 10.5345/JKIBC.2011.11.5.523 10.1198/jasa.2004.s339 10.1061/(ASCE)ME.1943-5479.0000096 10.1061/(ASCE)0733-9364(2004)130:6(869) 10.1061/(ASCE)0733-9364(2006)132:12(1242) 10.1061/(ASCE)0733-9364(2006)132:5(491) 10.1061/(ASCE)0742-597X(2010)26:2(105) 10.1061/(ASCE)BE.1943-5592.0001103 10.1109/ICARA.2000.4804025 10.5194/nhess-13-2815-2013 10.1061/(ASCE)CO.1943-7862.0001006 10.28991/cej-0309151 10.1016/j.ijproman.2005.11.010 10.1016/j.ijproman.2006.11.007 10.1007/s12205-014-1128-4 10.3390/w11030502 10.5130/ajceb.v10i1/2.1592 10.3390/sym11050677
ContentType	Journal Article
Copyright	2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 4U- ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS ADTPV AOWAS D8T ZZAVC
DOI	10.3390/su12041514
DatabaseName	CrossRef University Readers ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China SwePub SwePub Articles SWEPUB Freely available online SwePub Articles full text
DatabaseTitle	CrossRef Publicly Available Content Database University Readers ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Economics Environmental Sciences
EISSN	2071-1050
ExternalDocumentID	oai_DiVA_org_ltu_77758 10_3390_su12041514
GroupedDBID	29Q 2WC 4P2 5VS 7XC 8FE 8FH A8Z AAHBH AAYXX ACHQT ADBBV ADMLS AENEX AFFHD AFKRA AFMMW ALMA_UNASSIGNED_HOLDINGS BCNDV BENPR CCPQU CITATION E3Z ECGQY ESTFP FRS GX1 KQ8 ML. MODMG M~E OK1 P2P PHGZM PHGZT PIMPY PROAC TR2 4U- ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS 2XV ADTPV AOWAS C1A D8T IAO IEP IPNFZ ISR ITC RIG ZZAVC
ID	FETCH-LOGICAL-c332t-596d516083caee06aaed01d261094549b644bf474353d89ebf5c7c64f20e9a2b3
IEDL.DBID	BENPR
ISICitedReferencesCount	99
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000522460200232&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2071-1050
IngestDate	Tue Nov 04 15:49:39 EST 2025 Mon Oct 20 03:00:49 EDT 2025 Sat Nov 29 07:14:10 EST 2025 Tue Nov 18 21:25:51 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	4
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c332t-596d516083caee06aaed01d261094549b644bf474353d89ebf5c7c64f20e9a2b3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-3647-7137 0000-0002-6790-2653 0000-0003-1916-8164
OpenAccessLink	https://www.proquest.com/docview/2443897635?pq-origsite=%requestingapplication%
PQID	2443897635
PQPubID	2032327
ParticipantIDs	swepub_primary_oai_DiVA_org_ltu_77758 proquest_journals_2443897635 crossref_primary_10_3390_su12041514 crossref_citationtrail_10_3390_su12041514
PublicationCentury	2000
PublicationDate	2020
PublicationDateYYYYMMDD	2020-01-01
PublicationDate_xml	– year: 2020 text: 2020
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Sustainability
PublicationYear	2020
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Shin (ref_24) 2011; 11 Bilal (ref_29) 2016; 30 (ref_9) 2000; 18 ref_10 ref_54 Breiman (ref_37) 2001; 45 ref_52 Yaseen (ref_32) 2019; 7 Rogalska (ref_48) 2008; 18 Helmer (ref_53) 2002; 60 ref_16 Chou (ref_25) 2012; 27 Kokkaew (ref_19) 2014; 18 Hammad (ref_15) 2008; 2 Rezaie (ref_14) 2007; 190 Fugar (ref_7) 2010; 10 Long (ref_47) 2009; 18 Ismail (ref_51) 2013; 1 Gondia (ref_11) 2020; 146 Asadi (ref_30) 2015; 4 Naghibi (ref_40) 2017; 31 Heravi (ref_27) 2015; 141 ref_23 ref_20 Aibinu (ref_5) 2002; 20 Ruppert (ref_38) 2004; 99 Assaf (ref_3) 2006; 24 Senouci (ref_46) 2004; 130 Liaw (ref_42) 2002; 2 Tabassum (ref_57) 2014; 4 Alipour (ref_41) 2017; 22 Mahamid (ref_12) 2011; 28 ref_36 ref_35 Xia (ref_50) 2017; 143 ref_34 Smeeton (ref_55) 1985; 41 Hassan (ref_31) 2018; 4 Aziz (ref_8) 2013; 52 Chan (ref_13) 2004; 39 Ali (ref_17) 2010; 26 Kandil (ref_45) 2006; 132 Chao (ref_22) 2009; 135 Chou (ref_49) 2014; 41 Catani (ref_39) 2013; 13 Dursun (ref_18) 2011; 29 Apanaviciene (ref_26) 2015; 21 Vorakulpipat (ref_1) 2010; 19 ref_44 ref_43 Pontius (ref_56) 2011; 32 Elazouni (ref_21) 2006; 132 Chan (ref_2) 2001; 19 Chou (ref_33) 2014; 48 Sambasivan (ref_4) 2007; 25 Odeh (ref_6) 2002; 20 Gerassis (ref_28) 2016; 143
References_xml	– volume: 4 start-page: 115 year: 2015 ident: ref_30 article-title: A machine learning approach for predicting delays in construction logistics publication-title: Int. J. Adv. Logist. doi: 10.1080/2287108X.2015.1059920 – volume: 7 start-page: 74471 year: 2019 ident: ref_32 article-title: Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: Case study in tropical region: Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: Case study in tropical region publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2920916 – volume: 143 start-page: 4016093 year: 2016 ident: ref_28 article-title: Bayesian decision tool for the analysis of occupational accidents in the construction of embankments publication-title: J. Constr. Eng. Manag. doi: 10.1061/(ASCE)CO.1943-7862.0001225 – ident: ref_23 doi: 10.1007/978-3-642-12035-0_30 – volume: 41 start-page: 3955 year: 2014 ident: ref_49 article-title: Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2013.12.035 – ident: ref_43 doi: 10.7551/mitpress/1090.001.0001 – ident: ref_16 doi: 10.1061/41020(339)75 – volume: 41 start-page: 795 year: 1985 ident: ref_55 article-title: Early history of the kappa statistic publication-title: Biometrics – volume: 21 start-page: 290 year: 2015 ident: ref_26 article-title: modelling the effectiveness of ppp road infrastructure projects by applying random forests publication-title: J. Civ. Eng. Manag. doi: 10.3846/13923730.2014.971129 – volume: 60 start-page: 165 year: 2002 ident: ref_53 article-title: Automated discovery of concise predictive rules for intrusion detection publication-title: J. Syst. Softw. doi: 10.1016/S0164-1212(01)00088-7 – volume: 19 start-page: 142 year: 2010 ident: ref_1 article-title: Value creating construction virtual teams: A case study in the construction sector publication-title: Autom. Constr. doi: 10.1016/j.autcon.2009.11.016 – volume: 4 start-page: 124 year: 2014 ident: ref_57 article-title: A genetic algorithm analysis towards optimization solutions publication-title: Int. J. Digit. Inf. Wirel. Commun. – volume: 39 start-page: 339 year: 2004 ident: ref_13 article-title: Developing a benchmark model for project construction time performance in Hong Kong publication-title: Build. Environ. doi: 10.1016/j.buildenv.2003.08.012 – volume: 135 start-page: 169 year: 2009 ident: ref_22 article-title: Estimating Project S-Curves Using Polynomial Function and Neural Networks publication-title: J. Constr. Eng. Manag. doi: 10.1061/(ASCE)0733-9364(2009)135:3(169) – volume: 48 start-page: 88 year: 2014 ident: ref_33 article-title: Hybrid computational model for predicting bridge scour depth near piers and abutments publication-title: Autom. Constr. doi: 10.1016/j.autcon.2014.08.006 – volume: 20 start-page: 593 year: 2002 ident: ref_5 article-title: The effects of construction delays on project delivery in Nigerian construction industry publication-title: Int. J. Proj. Manag. doi: 10.1016/S0263-7863(02)00028-5 – volume: 146 start-page: 04019085 year: 2020 ident: ref_11 article-title: Machine Learning Algorithms for Construction Projects Delay Risk Prediction publication-title: J. Constr. Eng. Manag. doi: 10.1061/(ASCE)CO.1943-7862.0001736 – volume: 30 start-page: 500 year: 2016 ident: ref_29 article-title: Big Data in the construction industry: A review of present status, opportunities, and future trends publication-title: Adv. Eng. Inform. doi: 10.1016/j.aei.2016.07.001 – ident: ref_44 doi: 10.1007/978-3-319-11017-2 – ident: ref_52 – volume: 20 start-page: 67 year: 2002 ident: ref_6 article-title: Causes of construction delay: traditional contracts publication-title: Int. J. Proj. Manag. doi: 10.1016/S0263-7863(00)00037-5 – volume: 143 start-page: 04017030 year: 2017 ident: ref_50 article-title: Assessment of Stakeholder-Related Risks in Construction Projects: Integrated Analyses of Risk Attributes and Stakeholder Influences publication-title: J. Constr. Eng. Manag. doi: 10.1061/(ASCE)CO.1943-7862.0001322 – volume: 2 start-page: 250 year: 2008 ident: ref_15 article-title: Prediction model for construction cost and duration in Jordan publication-title: Jordan J. Civ. Eng. – ident: ref_54 doi: 10.1145/1143844.1143874 – volume: 31 start-page: 2761 year: 2017 ident: ref_40 article-title: Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping publication-title: Water Resour. Manag. doi: 10.1007/s11269-017-1660-3 – volume: 52 start-page: 387 year: 2013 ident: ref_8 article-title: Ranking of delay factors in construction projects after Egyptian revolution publication-title: Alex. Eng. J. doi: 10.1016/j.aej.2013.03.002 – volume: 18 start-page: 51 year: 2000 ident: ref_9 article-title: Construction delay: a quantitative analysis publication-title: Int. J. Proj. Manag. doi: 10.1016/S0263-7863(98)00060-X – volume: 18 start-page: 499 year: 2009 ident: ref_47 article-title: A genetic algorithm-based method for scheduling repetitive construction projects publication-title: Autom. Constr. doi: 10.1016/j.autcon.2008.11.005 – ident: ref_34 doi: 10.3390/su11071953 – volume: 32 start-page: 4407 year: 2011 ident: ref_56 article-title: Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2011.552923 – volume: 29 start-page: 97 year: 2011 ident: ref_18 article-title: Time–cost relationship of building projects: statistical adequacy of categorization with respect to project location publication-title: Constr. Manag. Econ. doi: 10.1080/01446193.2010.528437 – volume: 27 start-page: 51 year: 2012 ident: ref_25 article-title: Predicting disputes in public-private partnership projects: Classification and ensemble models publication-title: J. Comput. Civ. Eng. doi: 10.1061/(ASCE)CP.1943-5487.0000197 – volume: 45 start-page: 5 year: 2001 ident: ref_37 article-title: Random Forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 19 start-page: 223 year: 2001 ident: ref_2 article-title: Time-cost relationship of public sector projects in Malaysia publication-title: Int. J. Proj. Manag. doi: 10.1016/S0263-7863(99)00072-1 – volume: 18 start-page: 24 year: 2008 ident: ref_48 article-title: Time/cost optimization using hybrid evolutionary algorithm in construction project scheduling publication-title: Autom. Constr. doi: 10.1016/j.autcon.2008.04.002 – volume: 11 start-page: 523 year: 2011 ident: ref_24 article-title: Formwork System Selection Model for Tall Building Construction Using the Adaboost Algorithm publication-title: J. Korea Inst. Build. Constr. doi: 10.5345/JKIBC.2011.11.5.523 – volume: 99 start-page: 567 year: 2004 ident: ref_38 article-title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction publication-title: J. Am. Stat. Assoc. doi: 10.1198/jasa.2004.s339 – volume: 28 start-page: 300 year: 2011 ident: ref_12 article-title: Causes of delay in road construction projects publication-title: J. Manag. Eng. doi: 10.1061/(ASCE)ME.1943-5479.0000096 – volume: 130 start-page: 869 year: 2004 ident: ref_46 article-title: Use of Genetic Algorithms in Resource Scheduling of Construction Projects publication-title: J. Constr. Eng. Manag. doi: 10.1061/(ASCE)0733-9364(2004)130:6(869) – volume: 132 start-page: 1242 year: 2006 ident: ref_21 article-title: Classifying Construction Contractors Using Unsupervised-Learning Neural Networks publication-title: J. Constr. Eng. Manag. doi: 10.1061/(ASCE)0733-9364(2006)132:12(1242) – volume: 132 start-page: 491 year: 2006 ident: ref_45 article-title: Parallel Genetic Algorithms for Optimizing Resource Utilization in Large-Scale Construction Projects publication-title: J. Constr. Eng. Manag. doi: 10.1061/(ASCE)0733-9364(2006)132:5(491) – volume: 1 start-page: 2289 year: 2013 ident: ref_51 article-title: Expert opinion on risk level for factors affecting time and cost overrun along the project lifecycle in Malaysian construction projects publication-title: Int. J. Constr. Technol. Manag. – volume: 26 start-page: 105 year: 2010 ident: ref_17 article-title: Statistical Analysis on the Cost and Duration of Public Building Projects publication-title: J. Manag. Eng. doi: 10.1061/(ASCE)0742-597X(2010)26:2(105) – volume: 22 start-page: 04017076 year: 2017 ident: ref_41 article-title: Load-Capacity Rating of Bridge Populations through Machine Learning: Application of Decision Trees and Random Forests publication-title: J. Bridg. Eng. doi: 10.1061/(ASCE)BE.1943-5592.0001103 – ident: ref_20 doi: 10.1109/ICARA.2000.4804025 – volume: 13 start-page: 2815 year: 2013 ident: ref_39 article-title: Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues publication-title: Nat. Hazards Earth Syst. Sci. doi: 10.5194/nhess-13-2815-2013 – volume: 190 start-page: 1492 year: 2007 ident: ref_14 article-title: Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties publication-title: Appl. Math. Comput. – volume: 141 start-page: 04015032 year: 2015 ident: ref_27 article-title: Applying Artificial Neural Networks for Measuring and Predicting Construction-Labor Productivity publication-title: J. Constr. Eng. Manag. doi: 10.1061/(ASCE)CO.1943-7862.0001006 – volume: 4 start-page: 993 year: 2018 ident: ref_31 article-title: Evaluation of Legislation Adequacy in Managing Time and Quality Performance in Iraqi Construction Projects- a Bayesian Decision Tree Approach publication-title: Civ. Eng. J. doi: 10.28991/cej-0309151 – volume: 24 start-page: 349 year: 2006 ident: ref_3 article-title: Causes of delay in large construction projects publication-title: Int. J. Proj. Manag. doi: 10.1016/j.ijproman.2005.11.010 – volume: 25 start-page: 517 year: 2007 ident: ref_4 article-title: Causes and effects of delays in Malaysian construction industry publication-title: Int. J. Proj. Manag. doi: 10.1016/j.ijproman.2006.11.007 – volume: 18 start-page: 1599 year: 2014 ident: ref_19 article-title: Completion delay risk management: A dynamic risk insurance approach publication-title: KSCE J. Civ. Eng. doi: 10.1007/s12205-014-1128-4 – ident: ref_36 – volume: 2 start-page: 18 year: 2002 ident: ref_42 article-title: Classification and regression by randomforest publication-title: R News – ident: ref_35 doi: 10.3390/w11030502 – volume: 10 start-page: 103 year: 2010 ident: ref_7 article-title: Delays in Building Construction Projects in Ghana publication-title: Australas. J. Constr. Econ. Build. doi: 10.5130/ajceb.v10i1/2.1592 – ident: ref_10 doi: 10.3390/sym11050677
SSID	ssj0000331916
Score	2.5472949
Snippet	Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities....
SourceID	swepub proquest crossref
SourceType	Open Access Repository Aggregation Database Enrichment Source Index Database
StartPage	1514
SubjectTerms	Accuracy Artificial intelligence Civil engineering computer aid construction project Contractors Decision trees delay sources Genetic algorithms Geoteknik Labor productivity Literature reviews Neural networks Questionnaires random forest‐genetic algorithm Research methodology risk management Soil Mechanics
Title	Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model
URI	https://www.proquest.com/docview/2443897635 https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77758
Volume	12
WOSCitedRecordID	wos000522460200232&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources (ISSN International Center) customDbUrl: eissn: 2071-1050 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331916 issn: 2071-1050 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2071-1050 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331916 issn: 2071-1050 databaseCode: BENPR dateStart: 20090301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2071-1050 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331916 issn: 2071-1050 databaseCode: PIMPY dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JS8QwFH7oKOjFXRw3AurBQ7Ft0iUncRnRg0MRFb1Y0iaRwWFGp6Pgxd_uS5vOjCBePBZCEvq95C1Jvg9gX8U65trVjmCaOgytxOEIhpNxz0d_K4WbiVJsImq344cHntiCW2GvVdZ7YrlRy35uauRH6IbQtxr6tOPXN8eoRpnTVSuhMQ0zhqmMNWDmtNVObkZVFpeiiXlhxUtKMb9HfHES6LU89tMTTYSXk5ShpZu5WPzvBJdgwQaY5KSyiGWYUr0VmKvfHxcrsN4av23DhnZxF6vwlAzMqY1BivQ1uekUL-RcdcUn6fSIEfasqWZJUpVvClJeOCCCXH6ah1_loBUjBbmaoPokRnCtuwZ3F63bs0vHyi84OaX-0Al4KAMvxBgtF0q5oRBKup70DUE7w7Qyw1Aq0wxDkIDKmKtMB3mUh0z7ruLCz-g6NHr9ntoAEsUhp7HIZKB8psKca0H9CP-V9KRisWjCYQ1FmltuciOR0U0xRzGwpWPYmrA3avtaMXL82mq7Rim1q7JIxxA14aBCedSFodk-79yfpP3Bc9odvqdRhKnU5t_dbMG8b_LvsiSzDQ3EQe3AbP4x7BSDXWuMuzB9_dXCr-TqOnn8Bhpv7dU
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LThsxFL2ioRJsKKVFhAK11LLoYtQZ2_PwokKoASUColFFK7qp6xnbVUSUQCZQ5af6jVzPIwlS1R2L7i2PPPfMfXnuOQDvTWITYX3rKW6ZxxElnkBjeJkIKMZbrfxMlWITcb-fXF2JdAX-NLMw7rfKxieWjlqPc9cj_4hhCGOro087urn1nGqUu11tJDQqWJyZ2W8s2YpPvQ7a95DS05PLz12vVhXwcsbo1AtFpMMgwtQjV8b4kVJG-4GmjnecY7WUYYaQWY6RNWQ6ESazYR7nEbfUN0LRjOG-z2CVI9iTFqymvYv0-7yr4zOEdBBVPKiMCR_xhIfGKBnwx5FvKZ1dpigtw9rpi__thWzCRp1Ak-MK8S9hxYy2YK2Zry62YPtkMbuHC2vnVbyCH-nE3Uo5JJKxJV8GxTXpmKGakcGIOOHShkqXpFV7qiDlDxVEke7MDbaVD60YN0hvicqUOEG54Wv4-iTn3obWaDwyO0DiJBIsUZkODeUmyoVVjMZoGx1owxPVhg-N6WVec687CZChxBrMwUQuYNKGd_O1NxXjyF9X7TWokLXXKeQCEm04rFA138LRiHcG347lePJLDqd3Mo6xVNz99zZvYa17eXEuz3v9szewTl2voWw_7UELbWL24Xl-Px0Uk4P6QyDw86mh9gBYG0c3
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LTxsxEB5BgoBLW6CoaSm1RDn0sGLX3pcPCEUNERElWlW0ohdc79pGUaMEsmmr_DV-HeN9JEGquHHofeTVej7Py55vAD7q2MTcuMaRvmGOjyhxOCrDSblH0d8q6aayGDYR9fvx1RVPVuC-7oWxzyprm1gYajXObI38CN0Q-lZLn3ZkqmcRSad7cnvn2AlS9qa1HqdRQuRcz_5i-pYf9zqo60NKu6eXn8-casKAkzFGp07AQxV4IYYhmdTaDaXUyvUUtRzkPmZOKUYLqfHRywZMxVynJsiiLPQNdTWXNGW47io0IyvRgGbSu0h-zCs8LkN4e2HJicoYdxFbuAHoMT3_sRdcCm2X6UoLF9d9-T9vzit4UQXWpF2ehC1Y0aNt2Kj7rvNt2D1d9PShYGXU8h24Tib2tsoilIwN-TrIf5GOHsoZGYyIHWhaU-ySpCxb5aR4aEEkOZvZhrfioyUTB-ktUZwSO2hu-Bq-Pct_70JjNB7pN0CiOOQslqkKNPV1mHEjGY1QT8pT2o9lCz7VMBBZxcluR4MMBeZmFjJiAZkWHMxlb0smkn9K7dUIEZU1ysUCHi04LBE2X8LSi3cG39tiPLkRw-lvEUWYQr59epkPsI74El96_fN3sEltCaKoSu1BA1Wi38Na9mc6yCf71Zkg8PO5kfYAGWFP9w
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Prediction+of+Risk+Delay+in+Construction+Projects+Using+a+Hybrid+Artificial+Intelligence+Model&rft.jtitle=Sustainability&rft.au=Yaseen%2C+Zaher+Mundher&rft.au=Ali%2C+Zainab+Hasan&rft.au=Salih%2C+Sinan+Q.&rft.au=Al-Ansari%2C+Nadhir&rft.date=2020&rft.issn=2071-1050&rft.eissn=2071-1050&rft.volume=12&rft.issue=4&rft_id=info:doi/10.3390%2Fsu12041514&rft.externalDocID=oai_DiVA_org_ltu_77758
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2071-1050&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2071-1050&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2071-1050&client=summon