Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation Projects
One of the most preferred flood mitigation techniques for existing homes is raising the elevation of the lowest floor above the base flood elevation (BFE). Determination of project effectiveness through benefit-cost analysis (BCA) relies on the expected avoided flood loss and the project cost. Conve...
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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2021-06-01
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Series: | Frontiers in Built Environment |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fbuil.2021.646668/full |
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author | Arash Taghinezhad Carol J. Friedland Robert V. Rohli Brian D. Marx Jeffrey Giering Isabelina Nahmens |
author_facet | Arash Taghinezhad Carol J. Friedland Robert V. Rohli Brian D. Marx Jeffrey Giering Isabelina Nahmens |
author_sort | Arash Taghinezhad |
collection | DOAJ |
description | One of the most preferred flood mitigation techniques for existing homes is raising the elevation of the lowest floor above the base flood elevation (BFE). Determination of project effectiveness through benefit-cost analysis (BCA) relies on the expected avoided flood loss and the project cost. Conventional construction cost estimates are highly detailed, considering specific details of the project; however, mitigation project decisions must often be made while considering only highly generalized building details. To provide a robust, generalized project cost estimation method, this paper implements data modeling and mining methods such as multiple regression, random forest, generalized additive model (GAM), and model evaluation and selection with cross-validation methods to hindcast elevation costs for existing single-family homes based on average floor area, increase in floor elevation, number of stories, and foundation type. Project cost data for homes elevated in Louisiana, United States, between 2005 and 2015 are used in cost prediction analysis. The statistical modeling results are compared with detailed estimations for several types of home foundations over a range of elevations. The results show substantial agreement between regression predictions and detailed estimates using RSMeans cost data. |
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issn | 2297-3362 |
language | English |
last_indexed | 2024-12-17T06:43:16Z |
publishDate | 2021-06-01 |
publisher | Frontiers Media S.A. |
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series | Frontiers in Built Environment |
spelling | doaj.art-fa1b82c45645468d800a2f6f98911caf2022-12-21T21:59:49ZengFrontiers Media S.A.Frontiers in Built Environment2297-33622021-06-01710.3389/fbuil.2021.646668646668Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation ProjectsArash Taghinezhad0Carol J. Friedland1Robert V. Rohli2Brian D. Marx3Jeffrey Giering4Isabelina Nahmens5Bert S. Turner Department of Construction Management, College of Engineering, Louisiana State University, Baton Rouge, LA, United statesBert S. Turner Department of Construction Management, College of Engineering, Louisiana State University, Baton Rouge, LA, United statesDepartment of Oceanography & Coastal Sciences, Louisiana State University, Baton Rouge, LA, United StatesDepartment of Experimental Statistics, Louisiana State University, Baton Rouge, LA, United StatesLouisiana Governor’s Office of Homeland Security and Emergency Preparedness, Baton Rouge, LA, United StatesDepartment of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, United StatesOne of the most preferred flood mitigation techniques for existing homes is raising the elevation of the lowest floor above the base flood elevation (BFE). Determination of project effectiveness through benefit-cost analysis (BCA) relies on the expected avoided flood loss and the project cost. Conventional construction cost estimates are highly detailed, considering specific details of the project; however, mitigation project decisions must often be made while considering only highly generalized building details. To provide a robust, generalized project cost estimation method, this paper implements data modeling and mining methods such as multiple regression, random forest, generalized additive model (GAM), and model evaluation and selection with cross-validation methods to hindcast elevation costs for existing single-family homes based on average floor area, increase in floor elevation, number of stories, and foundation type. Project cost data for homes elevated in Louisiana, United States, between 2005 and 2015 are used in cost prediction analysis. The statistical modeling results are compared with detailed estimations for several types of home foundations over a range of elevations. The results show substantial agreement between regression predictions and detailed estimates using RSMeans cost data.https://www.frontiersin.org/articles/10.3389/fbuil.2021.646668/fullflood mitigationFreeboardcost estimationregressionrandom forestGAM |
spellingShingle | Arash Taghinezhad Carol J. Friedland Robert V. Rohli Brian D. Marx Jeffrey Giering Isabelina Nahmens Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation Projects Frontiers in Built Environment flood mitigation Freeboard cost estimation regression random forest GAM |
title | Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation Projects |
title_full | Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation Projects |
title_fullStr | Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation Projects |
title_full_unstemmed | Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation Projects |
title_short | Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation Projects |
title_sort | predictive statistical cost estimation model for existing single family home elevation projects |
topic | flood mitigation Freeboard cost estimation regression random forest GAM |
url | https://www.frontiersin.org/articles/10.3389/fbuil.2021.646668/full |
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