Performance analysis of machine learning-based prediction models for residential building construction waste
Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop...
Ausführliche Beschreibung
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| Autor*in: |
Gulghane, Akshay [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Asian journal of civil engineering - Cham : Springer International Publishing, 2017, 24(2023), 8 vom: 18. Mai, Seite 3265-3276 |
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| Übergeordnetes Werk: |
volume:24 ; year:2023 ; number:8 ; day:18 ; month:05 ; pages:3265-3276 |
| Links: |
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| DOI / URN: |
10.1007/s42107-023-00708-z |
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SPR053213963 |
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| 520 | |a Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. By predicting the amount of waste accurately at each stage of the project, effective measures can be taken to promote sustainable construction practices. | ||
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| 700 | 1 | |a Sharma, R. L. |4 aut | |
| 700 | 1 | |a Borkar, Prashant |4 aut | |
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10.1007/s42107-023-00708-z doi (DE-627)SPR053213963 (SPR)s42107-023-00708-z-e DE-627 ger DE-627 rakwb eng Gulghane, Akshay verfasserin aut Performance analysis of machine learning-based prediction models for residential building construction waste 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. By predicting the amount of waste accurately at each stage of the project, effective measures can be taken to promote sustainable construction practices. Construction waste management (dpeaa)DE-He213 Quantification of waste and waste minimization (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Sharma, R. L. aut Borkar, Prashant aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 24(2023), 8 vom: 18. Mai, Seite 3265-3276 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:24 year:2023 number:8 day:18 month:05 pages:3265-3276 https://dx.doi.org/10.1007/s42107-023-00708-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 8 18 05 3265-3276 |
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10.1007/s42107-023-00708-z doi (DE-627)SPR053213963 (SPR)s42107-023-00708-z-e DE-627 ger DE-627 rakwb eng Gulghane, Akshay verfasserin aut Performance analysis of machine learning-based prediction models for residential building construction waste 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. By predicting the amount of waste accurately at each stage of the project, effective measures can be taken to promote sustainable construction practices. Construction waste management (dpeaa)DE-He213 Quantification of waste and waste minimization (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Sharma, R. L. aut Borkar, Prashant aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 24(2023), 8 vom: 18. Mai, Seite 3265-3276 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:24 year:2023 number:8 day:18 month:05 pages:3265-3276 https://dx.doi.org/10.1007/s42107-023-00708-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 8 18 05 3265-3276 |
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10.1007/s42107-023-00708-z doi (DE-627)SPR053213963 (SPR)s42107-023-00708-z-e DE-627 ger DE-627 rakwb eng Gulghane, Akshay verfasserin aut Performance analysis of machine learning-based prediction models for residential building construction waste 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. By predicting the amount of waste accurately at each stage of the project, effective measures can be taken to promote sustainable construction practices. Construction waste management (dpeaa)DE-He213 Quantification of waste and waste minimization (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Sharma, R. L. aut Borkar, Prashant aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 24(2023), 8 vom: 18. Mai, Seite 3265-3276 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:24 year:2023 number:8 day:18 month:05 pages:3265-3276 https://dx.doi.org/10.1007/s42107-023-00708-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 8 18 05 3265-3276 |
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10.1007/s42107-023-00708-z doi (DE-627)SPR053213963 (SPR)s42107-023-00708-z-e DE-627 ger DE-627 rakwb eng Gulghane, Akshay verfasserin aut Performance analysis of machine learning-based prediction models for residential building construction waste 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. By predicting the amount of waste accurately at each stage of the project, effective measures can be taken to promote sustainable construction practices. Construction waste management (dpeaa)DE-He213 Quantification of waste and waste minimization (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Sharma, R. L. aut Borkar, Prashant aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 24(2023), 8 vom: 18. Mai, Seite 3265-3276 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:24 year:2023 number:8 day:18 month:05 pages:3265-3276 https://dx.doi.org/10.1007/s42107-023-00708-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 8 18 05 3265-3276 |
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10.1007/s42107-023-00708-z doi (DE-627)SPR053213963 (SPR)s42107-023-00708-z-e DE-627 ger DE-627 rakwb eng Gulghane, Akshay verfasserin aut Performance analysis of machine learning-based prediction models for residential building construction waste 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. By predicting the amount of waste accurately at each stage of the project, effective measures can be taken to promote sustainable construction practices. Construction waste management (dpeaa)DE-He213 Quantification of waste and waste minimization (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Prediction model (dpeaa)DE-He213 Sharma, R. L. aut Borkar, Prashant aut Enthalten in Asian journal of civil engineering Cham : Springer International Publishing, 2017 24(2023), 8 vom: 18. Mai, Seite 3265-3276 (DE-627)101384565X (DE-600)2919928-1 2522-011X nnns volume:24 year:2023 number:8 day:18 month:05 pages:3265-3276 https://dx.doi.org/10.1007/s42107-023-00708-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2023 8 18 05 3265-3276 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. 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performance analysis of machine learning-based prediction models for residential building construction waste |
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Performance analysis of machine learning-based prediction models for residential building construction waste |
| abstract |
Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. By predicting the amount of waste accurately at each stage of the project, effective measures can be taken to promote sustainable construction practices. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. By predicting the amount of waste accurately at each stage of the project, effective measures can be taken to promote sustainable construction practices. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The process of anticipating the amount of waste that will be produced during construction projects can help in reducing the overall waste. In order to make accurate predictions, it is crucial to estimate the amount of waste generated at each stage of the project. This study aimed to develop prediction models to estimate the amount of construction waste at different stages of the construction project for basic civil engineering materials. Data were collected from 134 construction sites in order to accurately calculate the amount of waste generated during the dynamic nature of construction activities. The collected data were analyzed using the decision tree and K-nearest neighbors’ algorithm, and the neural networks performance was studied by providing gross floor area and material estimation. The accuracy of the prediction models was evaluated using the root mean square method and Mean Absolute Percent Error method. The study results revealed that waste is generated at every stage of the construction project and can significantly impact the cost. The study also observed a pattern in waste generation at typical stages of the project. The prediction model showed satisfactory accuracy with an average RSME value of 0.49, indicating the model can be used for predictions. The combined average accuracy of the decision tree and KNN was found to be 88.32 and 88.51%, respectively. These findings can be used as a reference for the management and utilization of construction waste, and can help reduce the amount of waste generated during construction projects. By predicting the amount of waste accurately at each stage of the project, effective measures can be taken to promote sustainable construction practices. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Performance analysis of machine learning-based prediction models for residential building construction waste |
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Sharma, R. L. Borkar, Prashant |
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| score |
7.399967 |