{"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}
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.
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.