Data-Driven Insights on the Knowledge Gaps of Conceptual Cost Estimation Modeling

He, Xiang; Li, Rui; Anumba, Chimay J.

doi:10.1061/(asce)co.1943-7862.0001963

Cited by 16 publications

(16 citation statements)

References 86 publications

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“…A modeling result is a set of rules that indicate the relationships between the vague and imprecise input variables and output cost evaluation [7]. The model can be used for automated analogous estimations and in computational systems in the defense and aerospace industries [2].…”

Section: The Results Of the Scientometric Analysismentioning

confidence: 99%

“…Economic effectiveness can be accurately estimated far more lately, after layouts creation and production launch. The elaborate computer systems for enterprise resources management cannot eliminate the time gap between the design engineering stage and the manufacturing stage effectiveness evaluation [2]. Similar questions exist not only while product designing but at every life-cycle product stage.…”

Section: Introductionmentioning

confidence: 99%

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An Enterprise Management Framework for Costs Engineering Estimation Models Based on Scopus Scientometrics. An Aerospace Engineering Case Study

Аксенова

2021

MATEC Web Conf.

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The cutting-edge industrial product creation faces battling goals. There is an additional expenditure need to enhance new product reliability, and nevertheless, an enterprise should reduce product costs to receive the long-term development funds. This conflict resolution depends on a design engineer, who should take into account the future product costs as early, as possible. So, the purpose of this article is to elicit the most widespread cost estimation models at all design stages. I purposely investigate the models' limits to propose their common frame. The research methodology is Scopus scientometrics. First, I elicit the most authoritative reviews on the design cost estimation topic. Then I made the reviews content analysis and summarize the models’ limitations. Findings show that the design cost estimation models significantly vary. They do not eliminate or substitute for each other. Each model is suitable in an appropriate designing process place. Scientometric analysis points that advanced cost estimation models are poorly evolved for enterprise efficiency prediction including the aerospace industry. To overcome these obstacles, I propose an enterprise goal model. This study’s novelty is that a fitting combination of cost estimation models ensures the whole enterprise's economical effectiveness.

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Section: The Results Of the Scientometric Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Enterprise Management Framework for Costs Engineering Estimation Models Based on Scopus Scientometrics. An Aerospace Engineering Case Study

Аксенова

2021

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…The authors believe that collecting numerical data such as economic indicators and physical characteristics of projects for use in estimation models is more accessible than other uncertainties. This study using the CBR approach, a humancentered approach rather than machine-dependent (He et al, 2021), considered uncertainties such as complexity, labor force, and weather conditions in the estimation calculations. The results obtained from the RS dataset can also point to the better adaptation of the identified…”

Section: Discussionmentioning

confidence: 99%

“…This model has been used in two categories of regression and classification in different areas of the construction industry (Tayefeh Hashemi et al, 2020). However, He et al (2021) point out that regression algorithms have been used in initial cost estimation more than in other areas of machine learning. Present research considers diverse variables (features) to implement the data in different regression models.…”

Section: Case-based Reasoningmentioning

confidence: 99%

“…The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/0969-9988.htm identified cases are used as parameters to improve and measure the similarity of new files with previous ones. These variables have an essential role in improving the performance of artificial intelligence models (Abioye et al, 2021;He et al, 2021). Despite extensive study on cost and cost overrun forecasting, uncertainties and risks have not yet been adequately used in models such as CBR (Petheram and McMahon, 2019;Somi et al, 2021).…”

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confidence: 99%

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Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Sohrabi

Noorzai

2022

ECAM

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PurposeThe present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step.Design/methodology/approachThe cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria.FindingsThe findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others.Originality/valueThis research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.

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Estimating the volume of civil construction materials by machine learning models

Son

Nguyen

2023

Asian J Civ Eng

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Previous construction project cost models, especially in the project initiation phase, often mainly focused on estimating the total project cost without taking into account their constituent aspects such as materials, labor, machineries and equipment. Therefore, building a material quantity estimation model will have a positive impact on improving the accuracy of the total project cost. There have been many studies related to this issue, but there are few studies on building a model to estimate the quantity of materials for civil projects with reinforced concrete structures and they use specialized software (which is di cult to access for many subjects in the construction industry). The founding of many machines learning software, especially Weka software, helps to model with powerful algorithms with high reliability.In this study, suitable machine learning models will be proposed for estimating the quantity of materials as: concrete, formwork, steel of the components: foundation, column, beam and oor. Suitable machine learning models will be suggested to rank for each different model.

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Data-Driven Insights on the Knowledge Gaps of Conceptual Cost Estimation Modeling

Cited by 16 publications

References 86 publications

An Enterprise Management Framework for Costs Engineering Estimation Models Based on Scopus Scientometrics. An Aerospace Engineering Case Study

An Enterprise Management Framework for Costs Engineering Estimation Models Based on Scopus Scientometrics. An Aerospace Engineering Case Study

Risk-supported case-based reasoning approach for cost overrun estimation of water-related projects using machine learning

Estimating the volume of civil construction materials by machine learning models

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