Fundamentals of developing conceptual cost estimation models using machine learning techniques: Selection and measurement of building attributes

Ensuring the identification of building attributes is the primary task in developing a machine learning cost estimation model. However, the existing research on building attributes has the following shortcomings: it struggles to categorize building characteristics according to various cost types, an...

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Main Authors: Wang, Rui, Hafez, Salleh, Zulkiflee, Abdul Samad, Nabilah Filzah, Mohd Radzuan, Wen, Kok Ching
Format: Article
Language:English
Published: Malaysian Institute of Planners 2024
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/42323/
http://umpir.ump.edu.my/id/eprint/42323/1/Fundamentals%20of%20developing%20conceptual%20cost%20estimation%20models%20using%20machine%20learning%20techniques%20Selection%20and%20measurement%20of%20building%20attributes.pdf
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author Wang, Rui
Hafez, Salleh
Zulkiflee, Abdul Samad
Nabilah Filzah, Mohd Radzuan
Wen, Kok Ching
author_facet Wang, Rui
Hafez, Salleh
Zulkiflee, Abdul Samad
Nabilah Filzah, Mohd Radzuan
Wen, Kok Ching
author_sort Wang, Rui
building UMP Institutional Repository
collection Online Access
description Ensuring the identification of building attributes is the primary task in developing a machine learning cost estimation model. However, the existing research on building attributes has the following shortcomings: it struggles to categorize building characteristics according to various cost types, and the suggested sets of attributes do not clearly establish measurement standards for these qualities. To address these issues, this study aims to select a set of building attributes suitable for conceptual cost estimation and establishment of measurement standards. Through a two-round process of focused group discussions, this research ultimately identified 13 building attributes that can be collected before the completion of building design. These attributes serve as a basis for assessing completed building projects during the model development phase and for evaluating new projects during the model application phase. This study provides a foundational framework for the development of conceptual cost estimation models, ultimately enhancing the accuracy of machine learning cost estimation models.
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publishDate 2024
publisher Malaysian Institute of Planners
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spelling ump-423232024-08-13T01:11:03Z http://umpir.ump.edu.my/id/eprint/42323/ Fundamentals of developing conceptual cost estimation models using machine learning techniques: Selection and measurement of building attributes Wang, Rui Hafez, Salleh Zulkiflee, Abdul Samad Nabilah Filzah, Mohd Radzuan Wen, Kok Ching QA75 Electronic computers. Computer science Ensuring the identification of building attributes is the primary task in developing a machine learning cost estimation model. However, the existing research on building attributes has the following shortcomings: it struggles to categorize building characteristics according to various cost types, and the suggested sets of attributes do not clearly establish measurement standards for these qualities. To address these issues, this study aims to select a set of building attributes suitable for conceptual cost estimation and establishment of measurement standards. Through a two-round process of focused group discussions, this research ultimately identified 13 building attributes that can be collected before the completion of building design. These attributes serve as a basis for assessing completed building projects during the model development phase and for evaluating new projects during the model application phase. This study provides a foundational framework for the development of conceptual cost estimation models, ultimately enhancing the accuracy of machine learning cost estimation models. Malaysian Institute of Planners 2024 Article PeerReviewed pdf en cc_by_nc_nd http://umpir.ump.edu.my/id/eprint/42323/1/Fundamentals%20of%20developing%20conceptual%20cost%20estimation%20models%20using%20machine%20learning%20techniques%20Selection%20and%20measurement%20of%20building%20attributes.pdf Wang, Rui and Hafez, Salleh and Zulkiflee, Abdul Samad and Nabilah Filzah, Mohd Radzuan and Wen, Kok Ching (2024) Fundamentals of developing conceptual cost estimation models using machine learning techniques: Selection and measurement of building attributes. Journal of the Malaysian Institute of Planners, 22 (3). pp. 242-256. ISSN 1675-6215. (Published) https://doi.org/10.21837/pm.v22i32.1505 10.21837/pm.v22i32.1505
spellingShingle QA75 Electronic computers. Computer science
Wang, Rui
Hafez, Salleh
Zulkiflee, Abdul Samad
Nabilah Filzah, Mohd Radzuan
Wen, Kok Ching
Fundamentals of developing conceptual cost estimation models using machine learning techniques: Selection and measurement of building attributes
title Fundamentals of developing conceptual cost estimation models using machine learning techniques: Selection and measurement of building attributes
title_full Fundamentals of developing conceptual cost estimation models using machine learning techniques: Selection and measurement of building attributes
title_fullStr Fundamentals of developing conceptual cost estimation models using machine learning techniques: Selection and measurement of building attributes
title_full_unstemmed Fundamentals of developing conceptual cost estimation models using machine learning techniques: Selection and measurement of building attributes
title_short Fundamentals of developing conceptual cost estimation models using machine learning techniques: Selection and measurement of building attributes
title_sort fundamentals of developing conceptual cost estimation models using machine learning techniques: selection and measurement of building attributes
topic QA75 Electronic computers. Computer science
url http://umpir.ump.edu.my/id/eprint/42323/
http://umpir.ump.edu.my/id/eprint/42323/
http://umpir.ump.edu.my/id/eprint/42323/
http://umpir.ump.edu.my/id/eprint/42323/1/Fundamentals%20of%20developing%20conceptual%20cost%20estimation%20models%20using%20machine%20learning%20techniques%20Selection%20and%20measurement%20of%20building%20attributes.pdf