Performance evaluation of normalization-based CBR models for improving construction cost estimation

Ahn, Joo‐Myung; Ji, Sae-Hyun; Ahn, Sung Joon; Park, Moonseo; Lee, Hyunsoo; Kwon, Nahyun; Lee, Eul‐Bum; Kim, Yonggu

doi:10.1016/j.autcon.2020.103329

Cited by 49 publications

(19 citation statements)

References 41 publications

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“…Their study had limited test results due to the lack of a dataset [19]. Ahn et al [20] conducted a performance evaluation of the normalized case-based reasoning (CBR) model to improve the estimation of the initial design cost of a construction project for multi-family houses. Wong et al [21] investigated the role of building information modeling (BIM) to improve design errors and reworking among construction experts in China, and conducted a study to identify seven indicators influencing design errors.…”

Section: Machine Learning's Application To Plant Projectsmentioning

confidence: 99%

The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects

Choi

Kim²

2021

Sustainability

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Plant projects, referred to as Engineering Procurement and Construction (EPC), generate massive amounts of data throughout their life cycle, from the planning stages to the operation and maintenance (OM) stages. Many EPC contractors struggle with their projects due to the complexity of the decision-making processes, owing to the vast amount of project data generated during each project stage. In line with the fourth industrial revolution, the demand for engineering project management solutions to apply artificial intelligence (AI) in big data technology is increasing. The purpose of this study was to predict the risk of contractor and support decision-making at each project stage using machine-learning (ML) technology based on data generated in the bidding, engineering, construction, and OM stages of EPC projects. As a result of this study, the Engineering Machine-learning Automation Platform (EMAP), a cloud-based integrated analysis tool applied with big data and AI/ML technology, was developed. EMAP is an intelligent decision support system that consists of five modules: Invitation to Bid (ITB) Analysis, Design Cost Estimation, Design Error Checking, Change Order Forecasting, and Equipment Predictive Maintenance, using advanced AI/ML algorithms. In addition, each module was validated through case studies to assure the performance and accuracy of the module. This study contributes to the strengthening of the risk response for each stage of the EPC project, especially preventing errors by the project managers, and improving their work accuracy. Project risk management using AI/ML breaks away from the existing risk management practices centered on statistical analysis, and further expands the research scalability of related works.

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Section: Machine Learning's Application To Plant Projectsmentioning

confidence: 99%

The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects

Choi

Kim²

2021

Sustainability

Self Cite

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“…A review on CBR use for construction management can be found in Hu et al (2016) and its use for cost estimation can be found in Kim and Kim (2005), Ji et al (2010) or Ahn et al (2020). A comparison between the three methods was done by Kim et al (2004), with the new tools achieving better results than regression models.…”

Section: Literature Reviewmentioning

confidence: 99%

Cost Modelling from the Contractor Perspective: Application to Residential and Office Buildings

Monteiro¹,

Sousa²,

Meireles³

et al. 2021

Preprint

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For the majority of the contractual arrangements used in construction projects, the owner is not responsible for the cost deviations due to the variability of labor productivity or material price, amongst many other aspects. Consequently, the cost performance of a project may be entirely distinct for the owner and the contractor. Since the majority of the quantitative research on cost estimation and deviation found in the literature adopts the owners’ perspective, this research provides a contribution towards modelling costs and cost deviation from a contractors’ perspective. From an initial sample of 13 residential building and 10 office building projects, it was possible to develop models for cost estimation at the early stage of development including both endogenous and exogenous variables. Although the sample is relatively small, the authors were able to fully analyze all the cost data, using no secondary sources of data (very frequent in cost modelling studies). The statistically significant variables in the cost estimation models were the areas above and below ground and the years following the 2008 financial crisis, including the international bailout (2011-2014) period. For estimating the unit cost, a nonlinear model was obtained with the number of underground and total floor, the floor ratio and the years following the 2008 financial crisis, including the international bailout (2011-2014) period as predictors. For the office buildings, it was also found a statistically significant correlation between the cost deviation and the number of underground floors.

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“…Poor project preparation, lapses in management and control, over budgeting, poor materials, labor shortages, increased material costs, delays in deliveries, material waste, unexpected weather changes, material loss, insecurity, and poor communication are among the issues that most project managers and contractors face when it comes to cost control on their construction sites [5]. In the realm of estimation, several previous studies have been conducted to identify the various critical factors affecting the accuracy of highway project duration estimation using different statistical techniques such as mean score, relative importance index [16][17][18][19][20][43][44][45], principal component analysis [19,20], stepwise regression [46], trial and error method of artificial neural network [47], sensitivity analysis [18], Likert scale analysis [18], as well as correlation analysis [4,6,21,48]. There is no prior study in identifying and analyzing the critical factors that affect the estimation accuracy of highway project duration in a fuzzy environment because the qualitative attributes for expert evaluation are always imprecise and subjective.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Cost Estimation Practices of Fuzzy AHP for Building Construction Projects in Libya

Alfaggi

Naimi

2022

Civ Eng J

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This paper analysis and reviews the construction projects in Libya. In Libya, construction projects often face challenges due to the lack of proper information and data in cost-estimation methods. The primary goal of this paper is to demonstrate the estimation of construction costs using various methods. An optimization strategy based on a well-known robust algorithm The Fuzzy AHP technique is used to estimate the best roof structure choice based on cost rank among one or two-way flat slab, post-tension slab, pre-tension slab, waffle slab, and hollow core slab. The roofs were created employing five main factors that were implemented in a real-world situation in the Libyan building industry. The cost of materials, labor, machinery, transportation, and trash on site were all considered. Research findings show that the models can assist decision-makers in determining the cost rank of roof selection. When a range of methods are applied and compared to guarantee that this is the best option. This research study must be taken seriously when estimating and managing the contract and length of highway construction projects in the early stages of project development so that the time difference at the end of the project can be kept to a minimum by decision-makers when choosing the roof with the lowest cost. Doi: 10.28991/CEJ-2022-08-06-08 Full Text: PDF

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Performance evaluation of normalization-based CBR models for improving construction cost estimation

Cited by 49 publications

References 41 publications

The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects

The Engineering Machine-Learning Automation Platform (EMAP): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects

Cost Modelling from the Contractor Perspective: Application to Residential and Office Buildings

An Optimal Cost Estimation Practices of Fuzzy AHP for Building Construction Projects in Libya

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