Chapter Machine Learning-Based Construction Planning and Forecasting Model
Construction planning and scheduling are crucial aspects of project management that require a lot of time and resources to manage effectively. Machine learning and artificial intelligence techniques have shown great potential in improving construction planning and scheduling by providing more accura...
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| Format: | Online |
| Idioma: | anglès |
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Firenze University Press
2024
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| Accés en línia: | ONIX_20240402_9791221502893_30 |
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| _version_ | 1869527671089659904 |
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| author | Keser, Ahmet Esat Tokdemir, Onur Behzat |
| author_browse | Keser, Ahmet Esat Tokdemir, Onur Behzat |
| author_facet | Keser, Ahmet Esat Tokdemir, Onur Behzat |
| author_sort | Keser, Ahmet Esat |
| collection | Directory of Open Access Books |
| description | Construction planning and scheduling are crucial aspects of project management that require a lot of time and resources to manage effectively. Machine learning and artificial intelligence techniques have shown great potential in improving construction planning and scheduling by providing more accurate insights into project progress and forecasting. This paper proposed a machine learning model that utilizes regularly updated site data to generate predictions of quantity variances from the plan and enable a more accurate forecast of future progress based on historical data on concrete activities. Also, the outputs of this model can be used when creating a schedule for a new project. New schedules created with the help of this model will be more consistent and reliable due to its vast data pool and ability to generate realistic forecasts from this data. The model utilizes data from completed and other ongoing projects to generate insights and provide a more accurate and efficient construction planning and scheduling solution. Within the scope of this study, different attributes of concrete pouring activities of different projects and locations were used as input data for a machine learning process, and then, using this model on test data, the forecast concrete quantities were obtained. This model provides a more advanced solution than traditional project management tools by incorporating machine learning techniques while significantly improving construction planning, scheduling accuracy, and efficiency, leading to more successful projects and increased profitability for construction companies |
| format | Online |
| id | doab-20.500.12854ir-137967 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1379672024-05-14T21:22:39Z Chapter Machine Learning-Based Construction Planning and Forecasting Model Keser, Ahmet Esat Tokdemir, Onur Behzat Machine Learning Planning Scheduling Forecasting Data Visualizing Construction Business Intelligence thema EDItEUR::U Computing and Information Technology Construction planning and scheduling are crucial aspects of project management that require a lot of time and resources to manage effectively. Machine learning and artificial intelligence techniques have shown great potential in improving construction planning and scheduling by providing more accurate insights into project progress and forecasting. This paper proposed a machine learning model that utilizes regularly updated site data to generate predictions of quantity variances from the plan and enable a more accurate forecast of future progress based on historical data on concrete activities. Also, the outputs of this model can be used when creating a schedule for a new project. New schedules created with the help of this model will be more consistent and reliable due to its vast data pool and ability to generate realistic forecasts from this data. The model utilizes data from completed and other ongoing projects to generate insights and provide a more accurate and efficient construction planning and scheduling solution. Within the scope of this study, different attributes of concrete pouring activities of different projects and locations were used as input data for a machine learning process, and then, using this model on test data, the forecast concrete quantities were obtained. This model provides a more advanced solution than traditional project management tools by incorporating machine learning techniques while significantly improving construction planning, scheduling accuracy, and efficiency, leading to more successful projects and increased profitability for construction companies 2024-05-14T21:22:38Z 2024-05-14T21:22:38Z 2024-04-02T15:45:19Z 2023 chapter ONIX_20240402_9791221502893_30 2704-5846 https://library.oapen.org/handle/20.500.12657/89061 9791221502893 https://directory.doabooks.org/handle/20.500.12854/137967 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89061/1/9791221502893_71.pdf Firenze University Press 10.36253/979-12-215-0289-3.71 10.36253/979-12-215-0289-3.71 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 7 Florence open access |
| spellingShingle | Machine Learning Planning Scheduling Forecasting Data Visualizing Construction Business Intelligence thema EDItEUR::U Computing and Information Technology Keser, Ahmet Esat Tokdemir, Onur Behzat Chapter Machine Learning-Based Construction Planning and Forecasting Model |
| title | Chapter Machine Learning-Based Construction Planning and Forecasting Model |
| title_full | Chapter Machine Learning-Based Construction Planning and Forecasting Model |
| title_fullStr | Chapter Machine Learning-Based Construction Planning and Forecasting Model |
| title_full_unstemmed | Chapter Machine Learning-Based Construction Planning and Forecasting Model |
| title_short | Chapter Machine Learning-Based Construction Planning and Forecasting Model |
| title_sort | chapter machine learning based construction planning and forecasting model |
| topic | Machine Learning Planning Scheduling Forecasting Data Visualizing Construction Business Intelligence thema EDItEUR::U Computing and Information Technology |
| topic_facet | Machine Learning Planning Scheduling Forecasting Data Visualizing Construction Business Intelligence thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20240402_9791221502893_30 |
| work_keys_str_mv | AT keserahmetesat chaptermachinelearningbasedconstructionplanningandforecastingmodel AT tokdemironurbehzat chaptermachinelearningbasedconstructionplanningandforecastingmodel |