Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Yeom, Dong-Jun; Seo, Hae-Mi; Kim, Yoo-Jun; Cho, Chung-Suk; Kim, Young-Suk

doi:10.3846/jcem.2018.1646

Cited by 16 publications

(9 citation statements)

References 15 publications

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“…In addition, the proposed method estimation error constitutes an improvement above the customized agencies tools whose error was found to be 200% in some studies (Abdel-Raheem et al, 2018). Further, the obtained results are in conformity with the literature finding that prediction of construction duration is more challenging than cost (Pesko et al, 2017, Yeom et al, 2018. This is demonstrated by the results where the error percentages of ensemble duration models are higher than those of ensemble cost models.…”

Section: Testingsupporting

confidence: 85%

Conceptual estimation of construction duration and cost of public highway projects

Mohamed¹,

Moselhi²

2022

ITcon

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State Highway Agencies (SHAs) and Departments of Transportation (DOTs) allocate their limited resources to thousands of competing projects in multi-year transportation programs using expert judgement for the expected construction costs and durations. Such estimates overlook influencing parameters known in the planning phase and the importance of building reliable databases to support decision making. Meanwhile, it is possible to generate meaningful predictions in early stages of project development based on historical data gathering and analysis. The present research introduces a newly developed method for conceptual cost and duration estimation for public highway projects utilizing an ensemble of machine learning (ML) models and data collected for projects completed between 2004 and 2015 (roads, bridges, and drainage projects). Unlike previous studies, the proposed method includes project parameters that affect construction durations and costs and were not studied simultaneously before. The parameters considered are facility type, project scope, highway type, length, width, location, level of technical complexity, and new parameters pertinent to payment and procurement methods. The developed method was tested using 29 and 56 randomly selected projects, and the results yielded a Mean Absolute Percentage Error (MAPE) of 7.4% and 4.5% for the duration and cost, respectively, which are lower than the estimation errors of methods reported in recent literature. Additionally, the generalization abilities were assessed by the Mann-Whitney test, and the developed method is found to successfully handle diverse projects. Thus, machine learning models can assist agencies in the review process of competing projects from a high-level management perspective to ultimately develop better management execution programs.

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Section: Testingsupporting

confidence: 85%

Conceptual estimation of construction duration and cost of public highway projects

Mohamed¹,

Moselhi²

2022

ITcon

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“…Literature indicates that case studies are necessary for the analysis of the status and problems of calculating construction duration, along with a proposed model utilizing factors affecting the construction duration [51][52][53]. Toor and Ogunlana [6] conducted a case study of international airport construction projects.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yeom [53] conducted a case study to develop a construction estimation model using multiple linear regression during the project planning phase for office buildings in Korea. Their findings showed that the average construction duration was dependent on various factors.…”

Section: Literature Reviewmentioning

confidence: 99%

Total Construction Duration Calculation of Public Housing Projects in Turkey: A Case Study

Tıratacı¹,

Yaman²

2023

JCEMI

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Time planning for the accurate estimation of construction duration is important to prevent construction delays and successfully complete projects on time. Although the main reasons for construction delays differ among developing countries, several factors related to project characteristics and clients contribute. In this study, interviews and observations were conducted with TOKI (Toplu Konut Idaresi-Housing Development Administration of the Republic of Turkey), a major housing construction authority in Turkey, to explore construction delays. It was revealed that TOKI uses three factors to estimate total construction duration, which are the number of flats, type of drawings utilized during the tender stage, and nonworking days. Data analysis of the TOKI ProjectsStatus Table, which lists detailed project information for 2800 completed TOKI projects, showed that most delays occurred in housing projects. Therefore, the construction delays for TOKI housing projects were investigated in this case study. The findings revealed that there is a wide construction duration gap between calculated construction durations using TOKI's calculation method and durations indicated in official construction contracts between TOKI as a client and the awarded contractors. Therefore, it was emphasized that TOKI significantly shortened the contract duration, which ultimately resulted in delays for a considerable number of housing projects. These findings can be helpful for clients and project managers worldwide to mitigate construction delays. To effectively overcome construction delays in developing countries, suggestions are made for the need to develop accurate construction duration estimation models.

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“…duration. These additional variables have included quantitative and qualitative factors as scope related (Le-Hoai et al , 2013; Hoffman, 2005; Yeom et al , 2018). This study is delimited to project scope factor, which includes, construction cost, gross floor area, number of floors, average floor area, project type and cost level factors as potential predictive variables for time prediction model as these factors are widely recognized by previous studies and proved to be identifiable during the early phase.…”

Section: Literature Reviewmentioning

confidence: 99%

Construction time prediction model for public building projects

Alemu

2021

ECAM

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PurposeThe aim of the study was to develop a practical construction time model for public building projects in Addis Ababa, Ethiopia.Design/methodology/approachThis research work used regression analysis and also exploratory scatter and residual plot techniques. Simple and multiple regressions were used for the investigation of the best fit time model. The analyses were carried out using IBM SPSS statistical software, version 20.FindingsThe result revealed that the Bromilow time-cost principle was moderately applicable. However, the cubic regression model (CUB) was found a better time-cost relationship. On the contrary, the study has shown a poor relationship between actual time and gross floor area. Furthermore, multiple linear regression analysis (MLR) consists of three statistically significant variables were found a better fit time model.Research limitations/implicationsThe study is limited to only six project scope factors. Further research is recommended to include more building projects of similar type and implications of other factors to improve the reliability of the models.Practical implicationsThe developed model was not intended as a replacement for detailed construction scheduling techniques. The resulting model is applicable for front-end predictions of construction duration.Originality/valueThe main parties involved in the building projects should apply the model for benchmarking a precise construction time during the early planning phase.

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Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Cited by 16 publications

References 15 publications

Conceptual estimation of construction duration and cost of public highway projects

Conceptual estimation of construction duration and cost of public highway projects

Total Construction Duration Calculation of Public Housing Projects in Turkey: A Case Study

Construction time prediction model for public building projects

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