Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database
Resumen:
There are often considerable differences between the planned schedule for a construction project and what later develops during actual construction. This paper introduces an innovativeapproach that uses MarkovChain models to support predictions during earned value analyses. A statistical model was developed to predict possible deviations in a project schedule and the future progress of a project. This model, based on Markov chains, uses data from the past to adjust future predictions. A case study was built from a database of 90 housing cooperative construction projects and was validated in 12 more projects. A cross validation of three interactions was also carried out, obtaininganerror of 2.38% inthe prediction offuture progressandanerror of 4.29% intheprediction of construction timing.Theinnovative prediction model presented in this paper contributes to the management body of knowledge by introducing a new tool for the management and control of construction timing. The method presented improves construction management because it predicts future deviations in scheduleswithreducederrorsanddeterminestotaldeviationfromaconstructionschedulewithgreatprecision.Thisallowsbettercontroloverwork timing and represents important input in determining strategies and future actions.
2017 | |
Agencia Nacional de Investigación e Innovación | |
Prediction Schedule Earned schedule Earned value management Cost and schedule Ingeniería y Tecnología Ingeniería Civil Ingeniería de la Construcción |
|
Español | |
Agencia Nacional de Investigación e Innovación | |
REDI | |
http://hdl.handle.net/20.500.12381/213 | |
Acceso abierto | |
Reconocimiento 4.0 Internacional. (CC BY) |
_version_ | 1814959257609568256 |
---|---|
author | Rudeli, Natalia |
author2 | Santilli, Adrián Puente, I. Viles, Elisabeth |
author2_role | author author author |
author_facet | Rudeli, Natalia Santilli, Adrián Puente, I. Viles, Elisabeth |
author_role | author |
bitstream.checksum.fl_str_mv | 2d97768b1a25a7df5a347bb58fd2d77f a850c302e43367fb537bbaee2cf86674 |
bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 |
bitstream.url.fl_str_mv | https://redi.anii.org.uy/jspui/bitstream/20.500.12381/213/2/license.txt https://redi.anii.org.uy/jspui/bitstream/20.500.12381/213/1/Rudeli%20et%20al.%20%282017%29.pdf |
collection | REDI |
dc.creator.none.fl_str_mv | Rudeli, Natalia Santilli, Adrián Puente, I. Viles, Elisabeth |
dc.date.accessioned.none.fl_str_mv | 2019-12-24T14:52:50Z |
dc.date.available.none.fl_str_mv | 2019-12-24T14:52:50Z |
dc.date.issued.none.fl_str_mv | 2017-08-08 |
dc.description.abstract.none.fl_txt_mv | There are often considerable differences between the planned schedule for a construction project and what later develops during actual construction. This paper introduces an innovativeapproach that uses MarkovChain models to support predictions during earned value analyses. A statistical model was developed to predict possible deviations in a project schedule and the future progress of a project. This model, based on Markov chains, uses data from the past to adjust future predictions. A case study was built from a database of 90 housing cooperative construction projects and was validated in 12 more projects. A cross validation of three interactions was also carried out, obtaininganerror of 2.38% inthe prediction offuture progressandanerror of 4.29% intheprediction of construction timing.Theinnovative prediction model presented in this paper contributes to the management body of knowledge by introducing a new tool for the management and control of construction timing. The method presented improves construction management because it predicts future deviations in scheduleswithreducederrorsanddeterminestotaldeviationfromaconstructionschedulewithgreatprecision.Thisallowsbettercontroloverwork timing and represents important input in determining strategies and future actions. |
dc.description.sponsorship.none.fl_txt_mv | Agencia Nacional de Investigación e Innovación |
dc.identifier.anii.es.fl_str_mv | POS_EXT_2016 _1_134047 |
dc.identifier.uri.none.fl_str_mv | http://hdl.handle.net/20.500.12381/213 |
dc.language.iso.none.fl_str_mv | spa |
dc.publisher.es.fl_str_mv | American Society of Civil Engineers |
dc.rights.license.none.fl_str_mv | Reconocimiento 4.0 Internacional. (CC BY) |
dc.rights.none.fl_str_mv | Acceso abierto info:eu-repo/semantics/openAccess |
dc.source.es.fl_str_mv | Journal of Construction Engineering and Management. 2017; 143(11) |
dc.source.none.fl_str_mv | reponame:REDI instname:Agencia Nacional de Investigación e Innovación instacron:Agencia Nacional de Investigación e Innovación |
dc.subject.anii.es.fl_str_mv | Ingeniería y Tecnología Ingeniería Civil Ingeniería de la Construcción |
dc.subject.es.fl_str_mv | Prediction Schedule Earned schedule Earned value management Cost and schedule |
dc.title.none.fl_str_mv | Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database |
dc.type.es.fl_str_mv | Artículo |
dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
dc.type.version.es.fl_str_mv | Publicado |
dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
description | There are often considerable differences between the planned schedule for a construction project and what later develops during actual construction. This paper introduces an innovativeapproach that uses MarkovChain models to support predictions during earned value analyses. A statistical model was developed to predict possible deviations in a project schedule and the future progress of a project. This model, based on Markov chains, uses data from the past to adjust future predictions. A case study was built from a database of 90 housing cooperative construction projects and was validated in 12 more projects. A cross validation of three interactions was also carried out, obtaininganerror of 2.38% inthe prediction offuture progressandanerror of 4.29% intheprediction of construction timing.Theinnovative prediction model presented in this paper contributes to the management body of knowledge by introducing a new tool for the management and control of construction timing. The method presented improves construction management because it predicts future deviations in scheduleswithreducederrorsanddeterminestotaldeviationfromaconstructionschedulewithgreatprecision.Thisallowsbettercontroloverwork timing and represents important input in determining strategies and future actions. |
eu_rights_str_mv | openAccess |
format | article |
id | REDI_aa66c7cfa52e7ce55128632423c0235a |
identifier_str_mv | POS_EXT_2016 _1_134047 |
instacron_str | Agencia Nacional de Investigación e Innovación |
institution | Agencia Nacional de Investigación e Innovación |
instname_str | Agencia Nacional de Investigación e Innovación |
language | spa |
network_acronym_str | REDI |
network_name_str | REDI |
oai_identifier_str | oai:redi.anii.org.uy:20.500.12381/213 |
publishDate | 2017 |
reponame_str | REDI |
repository.mail.fl_str_mv | jmaldini@anii.org.uy |
repository.name.fl_str_mv | REDI - Agencia Nacional de Investigación e Innovación |
repository_id_str | 9421 |
rights_invalid_str_mv | Reconocimiento 4.0 Internacional. (CC BY) Acceso abierto |
spelling | Reconocimiento 4.0 Internacional. (CC BY)Acceso abiertoinfo:eu-repo/semantics/openAccess2019-12-24T14:52:50Z2019-12-24T14:52:50Z2017-08-08http://hdl.handle.net/20.500.12381/213POS_EXT_2016 _1_134047There are often considerable differences between the planned schedule for a construction project and what later develops during actual construction. This paper introduces an innovativeapproach that uses MarkovChain models to support predictions during earned value analyses. A statistical model was developed to predict possible deviations in a project schedule and the future progress of a project. This model, based on Markov chains, uses data from the past to adjust future predictions. A case study was built from a database of 90 housing cooperative construction projects and was validated in 12 more projects. A cross validation of three interactions was also carried out, obtaininganerror of 2.38% inthe prediction offuture progressandanerror of 4.29% intheprediction of construction timing.Theinnovative prediction model presented in this paper contributes to the management body of knowledge by introducing a new tool for the management and control of construction timing. The method presented improves construction management because it predicts future deviations in scheduleswithreducederrorsanddeterminestotaldeviationfromaconstructionschedulewithgreatprecision.Thisallowsbettercontroloverwork timing and represents important input in determining strategies and future actions.Agencia Nacional de Investigación e InnovaciónspaAmerican Society of Civil EngineersJournal of Construction Engineering and Management. 2017; 143(11)reponame:REDIinstname:Agencia Nacional de Investigación e Innovacióninstacron:Agencia Nacional de Investigación e InnovaciónPredictionScheduleEarned scheduleEarned value managementCost and scheduleIngeniería y TecnologíaIngeniería CivilIngeniería de la ConstrucciónStatistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction DatabaseArtículoPublicadoinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleUniversidad de NavarraRudeli, NataliaSantilli, AdriánPuente, I.Viles, ElisabethLICENSElicense.txtlicense.txttext/plain; charset=utf-84746https://redi.anii.org.uy/jspui/bitstream/20.500.12381/213/2/license.txt2d97768b1a25a7df5a347bb58fd2d77fMD52ORIGINALRudeli et al. (2017).pdfRudeli et al. (2017).pdfRudeli et al. (2017)application/pdf259596https://redi.anii.org.uy/jspui/bitstream/20.500.12381/213/1/Rudeli%20et%20al.%20%282017%29.pdfa850c302e43367fb537bbaee2cf86674MD5120.500.12381/2132020-09-18 11:57:52.308oai:redi.anii.org.uy:20.500.12381/213PHA+QWNlcHRhbmRvIGxhIGNlc2nDs24gZGUgZGVyZWNob3MgZWwgdXN1YXJpbyBERUNMQVJBIHF1ZSBvc3RlbnRhIGxhIGNvbmRpY2nDs24gZGUgYXV0b3IgZW4gZWwgc2VudGlkbyBxdWUgb3RvcmdhIGxhIGxlZ2lzbGFjacOzbiB2aWdlbnRlIHNvYnJlICBwcm9waWVkYWQgaW50ZWxlY3R1YWwgZGUgbGEgb2JyYSBvcmlnaW5hbCBxdWUgZXN0w6EgZW52aWFuZG8gKOKAnGxhIG9icmHigJ0pLiBFbiBjYXNvIGRlIHNlciBjb3RpdHVsYXIsIGVsIGF1dG9yIGRlY2xhcmEgcXVlIGN1ZW50YSBjb24gZWwgIGNvbnNlbnRpbWllbnRvIGRlIGxvcyByZXN0YW50ZXMgdGl0dWxhcmVzIHBhcmEgaGFjZXIgbGEgcHJlc2VudGUgY2VzacOzbi4gRW4gY2FzbyBkZSBwcmV2aWEgY2VzacOzbiBkZSBsb3MgZGVyZWNob3MgZGUgZXhwbG90YWNpw7NuIHNvYnJlIGxhIG9icmEgYSB0ZXJjZXJvcywgZWwgYXV0b3IgZGVjbGFyYSBxdWUgdGllbmUgbGEgYXV0b3JpemFjacOzbiBleHByZXNhIGRlIGRpY2hvcyB0aXR1bGFyZXMgZGUgZGVyZWNob3MgYSBsb3MgZmluZXMgZGUgZXN0YSBjZXNpw7NuLCBvIGJpZW4gcXVlIGhhIGNvbnNlcnZhZG8gbGEgZmFjdWx0YWQgZGUgY2VkZXIgZXN0b3MgZGVyZWNob3MgZW4gbGEgZm9ybWEgcHJldmlzdGEgZW4gbGEgcHJlc2VudGUgY2VzacOzbi48L3A+DQoNCjxwPkNvbiBlbCBmaW4gZGUgZGFyIGxhIG3DoXhpbWEgZGlmdXNpw7NuIGEgbGEgb2JyYSBhIHRyYXbDqXMgZGUgUkVESSwgZWwgQVVUT1IgQ0VERSBhIEFOSUksIGRlIGZvcm1hIGdyYXR1aXRhIHkgTk8gRVhDTFVTSVZBLCBjb24gY2Fyw6FjdGVyIGlycmV2b2NhYmxlIGUgaWxpbWl0YWRvIGVuIGVsIHRpZW1wbyB5IGNvbiDDoW1iaXRvIG11bmRpYWwsIGxvcyBkZXJlY2hvcyBkZSByZXByb2R1Y2Npw7NuLCBkZSBkaXN0cmlidWNpw7NuLCBkZSBjb211bmljYWNpw7NuIHDDumJsaWNhLCBpbmNsdWlkbyBlbCBkZXJlY2hvIGRlIHB1ZXN0YSBhIGRpc3Bvc2ljacOzbiBlbGVjdHLDs25pY2EsIHBhcmEgcXVlIHB1ZWRhIHNlciB1dGlsaXphZGEgZGUgZm9ybWEgbGlicmUgeSBncmF0dWl0YSBwb3IgdG9kb3MgbG9zIHF1ZSBsbyBkZXNlZW4uPC9wPg0KDQo8cD5MYSBjZXNpw7NuIHNlIHJlYWxpemEgYmFqbyBsYXMgc2lndWllbnRlcyBjb25kaWNpb25lczo8L3A+DQoNCjxwPkxhIHRpdHVsYXJpZGFkIGRlIGxhIG9icmEgc2VndWlyw6EgY29ycmVzcG9uZGllbmRvIGFsIEF1dG9yIHkgbGEgcHJlc2VudGUgY2VzacOzbiBkZSBkZXJlY2hvcyBwZXJtaXRpcsOhIGEgUkVESTo8L3A+DQoNCjx1bD4gPGxpIHZhbHVlPShhKT5UcmFuc2Zvcm1hciBsYSBvYnJhIGVuIGxhIG1lZGlkYSBlbiBxdWUgc2VhIG5lY2VzYXJpbyBwYXJhIGFkYXB0YXJsYSBhIGN1YWxxdWllciB0ZWNub2xvZ8OtYSBzdXNjZXB0aWJsZSBkZSBpbmNvcnBvcmFjacOzbiBhIEludGVybmV0OyByZWFsaXphciBsYXMgYWRhcHRhY2lvbmVzIG5lY2VzYXJpYXMgcGFyYSBoYWNlciBwb3NpYmxlIHN1IGFjY2VzbyB5IHZpc3VhbGl6YWNpw7NuIHBlcm1hbmVudGUsIGHDum4gcG9yIHBhcnRlIGRlIHBlcnNvbmFzIGNvbiBkaXNjYXBhY2lkYWQsIHJlYWxpemFyIGxhcyBtaWdyYWNpb25lcyBkZSBmb3JtYXRvcyBwYXJhIGFzZWd1cmFyIGxhIHByZXNlcnZhY2nDs24gYSBsYXJnbyBwbGF6bywgaW5jb3Jwb3JhciBsb3MgbWV0YWRhdG9zIG5lY2VzYXJpb3MgcGFyYSByZWFsaXphciBlbCByZWdpc3RybyBkZSBsYSBvYnJhLCBlIGluY29ycG9yYXIgdGFtYmnDqW4g4oCcbWFyY2FzIGRlIGFndWHigJ0gbyBjdWFscXVpZXIgb3RybyBzaXN0ZW1hIGRlIHNlZ3VyaWRhZCBvIGRlIHByb3RlY2Npw7NuIG8gZGUgaWRlbnRpZmljYWNpw7NuIGRlIHByb2NlZGVuY2lhLiBFbiBuaW5nw7puIGNhc28gZGljaGFzIG1vZGlmaWNhY2lvbmVzIGltcGxpY2Fyw6FuIGFkdWx0ZXJhY2lvbmVzIGVuIGVsIGNvbnRlbmlkbyBkZSBsYSBvYnJhLjwvbGk+IA0KPGxpIHZhbHVlPShiKT5SZXByb2R1Y2lyIGxhIG9icmEgZW4gdW4gbWVkaW8gZGlnaXRhbCBwYXJhIHN1IGluY29ycG9yYWNpw7NuIGEgc2lzdGVtYXMgZGUgYsO6c3F1ZWRhIHkgcmVjdXBlcmFjacOzbiwgaW5jbHV5ZW5kbyBlbCBkZXJlY2hvIGEgcmVwcm9kdWNpciB5IGFsbWFjZW5hcmxhIGVuIHNlcnZpZG9yZXMgdSBvdHJvcyBtZWRpb3MgZGlnaXRhbGVzIGEgbG9zIGVmZWN0b3MgZGUgc2VndXJpZGFkIHkgcHJlc2VydmFjacOzbi48L2xpPiANCjxsaSB2YWx1ZT0oYyk+UGVybWl0aXIgYSBsb3MgdXN1YXJpb3MgbGEgZGVzY2FyZ2EgZGUgY29waWFzIGVsZWN0csOzbmljYXMgZGUgbGEgb2JyYSBlbiB1biBzb3BvcnRlIGRpZ2l0YWwuPC9saT4gDQo8bGkgdmFsdWU9KGQpPlJlYWxpemFyIGxhIGNvbXVuaWNhY2nDs24gcMO6YmxpY2EgeSBwdWVzdGEgYSBkaXNwb3NpY2nDs24gZGUgbGEgb2JyYSBhY2Nlc2libGUgZGUgbW9kbyBsaWJyZSB5IGdyYXR1aXRvIGEgdHJhdsOpcyBkZSBJbnRlcm5ldC48L3VsPg0KDQo8cD5FbiB2aXJ0dWQgZGVsIGNhcsOhY3RlciBubyBleGNsdXNpdm8gZGUgbGEgY2VzacOzbiwgZWwgQXV0b3IgY29uc2VydmEgdG9kb3MgbG9zIGRlcmVjaG9zIGRlIGF1dG9yIHNvYnJlIGxhIG9icmEsIHkgcG9kcsOhIHBvbmVybGEgYSBkaXNwb3NpY2nDs24gZGVsIHDDumJsaWNvIGVuIGVzdGEgeSBlbiBwb3N0ZXJpb3JlcyB2ZXJzaW9uZXMsIGEgdHJhdsOpcyBkZSBsb3MgbWVkaW9zIHF1ZSBlc3RpbWUgb3BvcnR1bm9zLjwvcD4NCg0KPHA+RWwgQXV0b3IgZGVjbGFyYSBiYWpvIGp1cmFtZW50byBxdWUgbGEgcHJlc2VudGUgY2VzacOzbiBubyBpbmZyaW5nZSBuaW5nw7puIGRlcmVjaG8gZGUgdGVyY2Vyb3MsIHlhIHNlYW4gZGUgcHJvcGllZGFkIGluZHVzdHJpYWwsIGludGVsZWN0dWFsIG8gY3VhbHF1aWVyIG90cm8geSBnYXJhbnRpemEgcXVlIGVsIGNvbnRlbmlkbyBkZSBsYSBvYnJhIG5vIGF0ZW50YSBjb250cmEgbG9zIGRlcmVjaG9zIGFsIGhvbm9yLCBhIGxhIGludGltaWRhZCB5IGEgbGEgaW1hZ2VuIGRlIHRlcmNlcm9zLCBuaSBlcyBkaXNjcmltaW5hdG9yaW8uIFJFREkgZXN0YXLDoSBleGVudG8gZGUgbGEgcmV2aXNpw7NuIGRlbCBjb250ZW5pZG8gZGUgbGEgb2JyYSwgcXVlIGVuIHRvZG8gY2FzbyBwZXJtYW5lY2Vyw6EgYmFqbyBsYSByZXNwb25zYWJpbGlkYWQgZXhjbHVzaXZhIGRlbCBBdXRvci48L3A+DQoNCjxwPkxhIG9icmEgc2UgcG9uZHLDoSBhIGRpc3Bvc2ljacOzbiBkZSBsb3MgdXN1YXJpb3MgcGFyYSBxdWUgaGFnYW4gZGUgZWxsYSB1biB1c28ganVzdG8geSByZXNwZXR1b3NvIGRlIGxvcyBkZXJlY2hvcyBkZWwgYXV0b3IgeSBjb24gZmluZXMgZGUgZXN0dWRpbywgaW52ZXN0aWdhY2nDs24sIG8gY3VhbHF1aWVyIG90cm8gZmluIGzDrWNpdG8uIEVsIG1lbmNpb25hZG8gdXNvLCBtw6FzIGFsbMOhIGRlIGxhIGNvcGlhIHByaXZhZGEsIHJlcXVlcmlyw6EgcXVlIHNlIGNpdGUgbGEgZnVlbnRlIHkgc2UgcmVjb25vemNhIGxhIGF1dG9yw61hLiBBIHRhbGVzIGZpbmVzIGVsIEF1dG9yIGFjZXB0YSBlbCB1c28gZGUgbGljZW5jaWFzIENyZWF0aXZlIENvbW1vbnMgeSBFTElHRSB1bmEgZGUgZXN0YXMgbGljZW5jaWFzIGVzdGFuZGFyaXphZGFzIGEgbG9zIGZpbmVzIGRlIGNvbXVuaWNhciBzdSBvYnJhLjwvcD4NCg0KPHA+RWwgQXV0b3IsIGNvbW8gZ2FyYW50ZSBkZSBsYSBhdXRvcsOtYSBkZSBsYSBvYnJhIHkgZW4gcmVsYWNpw7NuIGEgbGEgbWlzbWEsIGRlY2xhcmEgcXVlIGxhIEFOSUkgc2UgZW5jdWVudHJhIGxpYnJlIGRlIHRvZG8gdGlwbyBkZSByZXNwb25zYWJpbGlkYWQsIHNlYSDDqXN0YSBjaXZpbCwgYWRtaW5pc3RyYXRpdmEgbyBwZW5hbCwgeSBxdWUgw6lsIG1pc21vIGFzdW1lIGxhIHJlc3BvbnNhYmlsaWRhZCBmcmVudGUgYSBjdWFscXVpZXIgcmVjbGFtbyBvIGRlbWFuZGEgcG9yIHBhcnRlIGRlIHRlcmNlcm9zLiBMQSBBTklJIGVzdGFyw6EgZXhlbnRhIGRlIGVqZXJjaXRhciBhY2Npb25lcyBsZWdhbGVzIGVuIG5vbWJyZSBkZWwgQXV0b3IgZW4gZWwgc3VwdWVzdG8gZGUgaW5mcmFjY2lvbmVzIGEgZGVyZWNob3MgZGUgcHJvcGllZGFkIGludGVsZWN0dWFsIGRlcml2YWRvcyBkZWwgZGVww7NzaXRvIHkgYXJjaGl2byBkZSBsYSBvYnJhLjwvcD4NCg0KPHA+QU5JSSBub3RpZmljYXLDoSBhbCBBdXRvciBkZSBjdWFscXVpZXIgcmVjbGFtYWNpw7NuIHF1ZSByZWNpYmEgZGUgdGVyY2Vyb3MgZW4gcmVsYWNpw7NuIGNvbiBsYSBvYnJhIHksIGVuIHBhcnRpY3VsYXIsIGRlIHJlY2xhbWFjaW9uZXMgcmVsYXRpdmFzIGEgbG9zIGRlcmVjaG9zIGRlIHByb3BpZWRhZCBpbnRlbGVjdHVhbCBzb2JyZSBlbGxhLjwvcD4NCg0KPHA+RWwgQXV0b3IgcG9kcsOhIHNvbGljaXRhciBlbCByZXRpcm8gbyBsYSBpbnZpc2liaWxpemFjacOzbiBkZSBsYSBvYnJhIGRlIFJFREkgc8OzbG8gcG9yIGNhdXNhIGp1c3RpZmljYWRhLiBBIHRhbCBmaW4gZGViZXLDoSBtYW5pZmVzdGFyIHN1IHZvbHVudGFkIGVuIGZvcm1hIGZlaGFjaWVudGUgeSBhY3JlZGl0YXIgZGViaWRhbWVudGUgbGEgY2F1c2EganVzdGlmaWNhZGEuIEFzaW1pc21vIEFOSUkgcG9kcsOhIHJldGlyYXIgbyBpbnZpc2liaWxpemFyIGxhIG9icmEgZGUgUkVESSwgcHJldmlhIG5vdGlmaWNhY2nDs24gYWwgQXV0b3IsIGVuIHN1cHVlc3RvcyBzdWZpY2llbnRlbWVudGUganVzdGlmaWNhZG9zLCBvIGVuIGNhc28gZGUgcmVjbGFtYWNpb25lcyBkZSB0ZXJjZXJvcy48L3A+Gobiernohttps://www.anii.org.uy/https://redi.anii.org.uy/oai/requestjmaldini@anii.org.uyUruguayopendoar:94212020-09-18T14:57:52REDI - Agencia Nacional de Investigación e Innovaciónfalse |
spellingShingle | Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database Rudeli, Natalia Prediction Schedule Earned schedule Earned value management Cost and schedule Ingeniería y Tecnología Ingeniería Civil Ingeniería de la Construcción |
status_str | publishedVersion |
title | Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database |
title_full | Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database |
title_fullStr | Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database |
title_full_unstemmed | Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database |
title_short | Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database |
title_sort | Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database |
topic | Prediction Schedule Earned schedule Earned value management Cost and schedule Ingeniería y Tecnología Ingeniería Civil Ingeniería de la Construcción |
url | http://hdl.handle.net/20.500.12381/213 |