A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis
Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management...
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| Other Authors: | , |
| Format: | article |
| Language: | English |
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2018
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| Online Access: | http://hdl.handle.net/20.500.12381/214 |
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| _version_ | 1868890176671973376 |
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| author | Rudeli, Natalia |
| author2 | Viles, Elisabeth Santilli, Adrián |
| author2_role | author author |
| author_browse | Rudeli, Natalia Santilli, Adrián Viles, Elisabeth |
| author_facet | Rudeli, Natalia Viles, Elisabeth Santilli, Adrián |
| author_role | author |
| collection | REDI |
| dc.creator.none.fl_str_mv | Rudeli, Natalia Viles, Elisabeth Santilli, Adrián |
| dc.date.none.fl_str_mv | 2018-09-12 2019-12-24T14:55:14Z 2019-12-24T14:55:14Z |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/20.500.12381/214 POS_EXT_2016 _1_134047 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | World Academy of Science, Engineering and Technology |
| dc.rights.none.fl_str_mv | Acceso abierto info:eu-repo/semantics/openAccess Reconocimiento 4.0 Internacional. (CC BY) |
| dc.source.none.fl_str_mv | International Journal of Civil and Environmental Engineering. 2018; 12 (5) reponame:REDI instname:Agencia Nacional de Investigación e Innovación instacron:Agencia Nacional de Investigación e Innovación |
| dc.subject.none.fl_str_mv | Cluster analysis Construction management Earned value Ingeniería y Tecnología Ingeniería Civil Ingeniería de la Construcción |
| dc.title.none.fl_str_mv | A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article Publicado info:eu-repo/semantics/publishedVersion |
| description | Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_baecc38dfd1b69b2d2d4f711254cf796 |
| 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 | eng |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:redi.anii.org.uy:20.500.12381/214 |
| publishDate | 2018 |
| publishDateSort | 2018 |
| publisher.none.fl_str_mv | World Academy of Science, Engineering and Technology |
| reponame_str | REDI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Acceso abierto Reconocimiento 4.0 Internacional. (CC BY) |
| spelling | A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster AnalysisRudeli, NataliaViles, ElisabethSantilli, AdriánCluster analysisConstruction managementEarned valueIngeniería y TecnologíaIngeniería CivilIngeniería de la ConstrucciónDelays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.Agencia Nacional de Investigación e InnovaciónWorld Academy of Science, Engineering and Technology2019-12-24T14:55:14Z2019-12-24T14:55:14Z2018-09-12Artículoinfo:eu-repo/semantics/articlePublicadoinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/20.500.12381/214POS_EXT_2016 _1_134047International Journal of Civil and Environmental Engineering. 2018; 12 (5)reponame:REDIinstname:Agencia Nacional de Investigación e Innovacióninstacron:Agencia Nacional de Investigación e InnovaciónengAcceso abiertoinfo:eu-repo/semantics/openAccessReconocimiento 4.0 Internacional. (CC BY)oai:redi.anii.org.uy:20.500.12381/2142026-06-16T05:02:42Z |
| spellingShingle | A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis Rudeli, Natalia Cluster analysis Construction management Earned value Ingeniería y Tecnología Ingeniería Civil Ingeniería de la Construcción |
| status_str | publishedVersion |
| title | A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis |
| title_full | A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis |
| title_fullStr | A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis |
| title_full_unstemmed | A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis |
| title_short | A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis |
| title_sort | A Construction Management Tool: Determining a Project Schedule Typical Behaviors Using Cluster Analysis |
| topic | Cluster analysis Construction management Earned value Ingeniería y Tecnología Ingeniería Civil Ingeniería de la Construcción |
| url | http://hdl.handle.net/20.500.12381/214 |