亚的斯亚贝巴公共建筑项目混合法成本估算因素建模

IF 1.5 4区 工程技术 Q3 CONSTRUCTION & BUILDING TECHNOLOGY
Behailu Temesgen Habe, Lucy Feleke Nigussie, Mamaru Dessalegn Belay
{"title":"亚的斯亚贝巴公共建筑项目混合法成本估算因素建模","authors":"Behailu Temesgen Habe, Lucy Feleke Nigussie, Mamaru Dessalegn Belay","doi":"10.1155/2024/1737352","DOIUrl":null,"url":null,"abstract":"Assessing the most important cost-influencing factors is essential for enhancing the predictive ability of cost estimation for building construction projects. The goal of this study is to examine and design a valid cost prediction model for assessing factors that impact the cost estimation of public buildings in Addis Ababa. This research solves these issues that typically arise in predictive cost estimation models in two major processes. First, the insights of 133 professionals gathered on the 38 cost-impacting elements, and 15 top factors design, time or cost, and parties’ experience were determined. The suggested hybrid approach is based on the Akaike information criterion (AIC) and principal component regression (PCR) employed, coupling a stepwise linear regression model. According to the findings of the study, principal component analysis reduced important factors to 14 and efficiently solved the problem of multicollinearity with a variance inflation factor of less than 2, while stepwise cross-validation solved the overfitting problem at the lowest AIC. The cost prediction model sorted out five factors: design completion by the public body when bids are invited; completion of the project scope definition when bids are invited; level of construction complexity; importance of project completion within budget; and subcontractor experience and capability have all been identified as the main cost-determining factors. The study’s contribution is the first approach (PCR–AIC) utilized in this work to explore numerous cost-estimating components, eliminate those that were related to one another, and identify the most crucial ones that consisted of the majority of the original variables’ attributes.","PeriodicalId":7242,"journal":{"name":"Advances in Civil Engineering","volume":"96 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling Cost-Estimation Factors for Public Building Projects with Hybrid Approach in Addis Ababa\",\"authors\":\"Behailu Temesgen Habe, Lucy Feleke Nigussie, Mamaru Dessalegn Belay\",\"doi\":\"10.1155/2024/1737352\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Assessing the most important cost-influencing factors is essential for enhancing the predictive ability of cost estimation for building construction projects. The goal of this study is to examine and design a valid cost prediction model for assessing factors that impact the cost estimation of public buildings in Addis Ababa. This research solves these issues that typically arise in predictive cost estimation models in two major processes. First, the insights of 133 professionals gathered on the 38 cost-impacting elements, and 15 top factors design, time or cost, and parties’ experience were determined. The suggested hybrid approach is based on the Akaike information criterion (AIC) and principal component regression (PCR) employed, coupling a stepwise linear regression model. According to the findings of the study, principal component analysis reduced important factors to 14 and efficiently solved the problem of multicollinearity with a variance inflation factor of less than 2, while stepwise cross-validation solved the overfitting problem at the lowest AIC. The cost prediction model sorted out five factors: design completion by the public body when bids are invited; completion of the project scope definition when bids are invited; level of construction complexity; importance of project completion within budget; and subcontractor experience and capability have all been identified as the main cost-determining factors. The study’s contribution is the first approach (PCR–AIC) utilized in this work to explore numerous cost-estimating components, eliminate those that were related to one another, and identify the most crucial ones that consisted of the majority of the original variables’ attributes.\",\"PeriodicalId\":7242,\"journal\":{\"name\":\"Advances in Civil Engineering\",\"volume\":\"96 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Civil Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1155/2024/1737352\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Civil Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1155/2024/1737352","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

评估最重要的成本影响因素对于提高建筑施工项目成本估算的预测能力至关重要。本研究的目标是研究和设计一个有效的成本预测模型,用于评估影响亚的斯亚贝巴公共建筑成本估算的因素。本研究通过两个主要过程解决了成本估算预测模型中通常会出现的这些问题。首先,收集了 133 位专业人士对 38 个影响成本因素的见解,并确定了 15 个首要因素设计、时间或成本以及各方经验。建议采用的混合方法基于阿卡克信息准则(AIC)和主成分回归(PCR),并结合逐步线性回归模型。研究结果表明,主成分分析将重要因子减少到 14 个,并有效地解决了多重共线性问题,方差膨胀因子小于 2,而逐步交叉验证则以最低的 AIC 解决了过拟合问题。成本预测模型梳理出五个因素:公共机构在招标时完成设计;招标时完成项目范围定义;施工复杂程度;在预算范围内完成项目的重要性;分包商的经验和能力都被认为是决定成本的主要因素。本研究的贡献在于,它首次采用了 PCR-AIC 方法,对众多成本估算要素进行了探讨,剔除了相互关联的要素,并确定了最关键的要素,这些要素包含了原始变量的大部分属性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling Cost-Estimation Factors for Public Building Projects with Hybrid Approach in Addis Ababa
Assessing the most important cost-influencing factors is essential for enhancing the predictive ability of cost estimation for building construction projects. The goal of this study is to examine and design a valid cost prediction model for assessing factors that impact the cost estimation of public buildings in Addis Ababa. This research solves these issues that typically arise in predictive cost estimation models in two major processes. First, the insights of 133 professionals gathered on the 38 cost-impacting elements, and 15 top factors design, time or cost, and parties’ experience were determined. The suggested hybrid approach is based on the Akaike information criterion (AIC) and principal component regression (PCR) employed, coupling a stepwise linear regression model. According to the findings of the study, principal component analysis reduced important factors to 14 and efficiently solved the problem of multicollinearity with a variance inflation factor of less than 2, while stepwise cross-validation solved the overfitting problem at the lowest AIC. The cost prediction model sorted out five factors: design completion by the public body when bids are invited; completion of the project scope definition when bids are invited; level of construction complexity; importance of project completion within budget; and subcontractor experience and capability have all been identified as the main cost-determining factors. The study’s contribution is the first approach (PCR–AIC) utilized in this work to explore numerous cost-estimating components, eliminate those that were related to one another, and identify the most crucial ones that consisted of the majority of the original variables’ attributes.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Advances in Civil Engineering
Advances in Civil Engineering Engineering-Civil and Structural Engineering
CiteScore
4.00
自引率
5.60%
发文量
612
审稿时长
15 weeks
期刊介绍: Advances in Civil Engineering publishes papers in all areas of civil engineering. The journal welcomes submissions across a range of disciplines, and publishes both theoretical and practical studies. Contributions from academia and from industry are equally encouraged. Subject areas include (but are by no means limited to): -Structural mechanics and engineering- Structural design and construction management- Structural analysis and computational mechanics- Construction technology and implementation- Construction materials design and engineering- Highway and transport engineering- Bridge and tunnel engineering- Municipal and urban engineering- Coastal, harbour and offshore engineering-- Geotechnical and earthquake engineering Engineering for water, waste, energy, and environmental applications- Hydraulic engineering and fluid mechanics- Surveying, monitoring, and control systems in construction- Health and safety in a civil engineering setting. Advances in Civil Engineering also publishes focused review articles that examine the state of the art, identify emerging trends, and suggest future directions for developing fields.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信