Cost estimation of civil construction projects using machine learning paradigm

Arage, Sphurti S.; Dharwadkar, Nagaraj V.

doi:10.1109/i-smac.2017.8058249

Cited by 15 publications

(5 citation statements)

References 10 publications

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“…Preliminary or conceptual cost estimation is commonly used to predict the cost at an earlier stage in the project development [19]. It is a greatly experience-based process and involves the assessment of several multifaceted relationships of cost-influencing factors [20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…e input data set that contains 12 years cost of different construction items were analysed through graph plotting, which shows the relationship between the year and cost for the purpose of forecasting the future cost of construction projects [19]. In the study conducted by Ma et al [27], the proficiency of estimators on specifications for construction cost estimation was mentioned as a significant factor that affects the accuracy and efficiency of cost estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Meharie

Gariy

Mutuku

et al. 2019

Advances in Civil Engineering

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Accurate cost estimates are vital to the effective realisation of construction projects. Extended knowledge, wide-ranging information, substantial expertise, and continuous improvement are required to attain accurate cost estimation. Cost estimation at the preliminary phase of the project is always a challenge as only limited information is available. Hence, rational selection of input variables for preliminary cost estimation could be imperative. A systematic input variable selection approach for preliminary estimating using an integrated methodology of factor analysis and fuzzy AHP is presented in this paper. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP based on the geometric mean method is employed to determine the weights of input variables in a fuzzy environment where the subjectivity and vagueness are handled with natural language expressions parameterized by triangular fuzzy numbers. Then, the input variables are suggested to be selected starting with those having high coefficient and high importance weight. A set of three variables, one from each group, can be added to the estimating model at a time so that the problem of collinearity can vanish and good accuracy of the estimate can be ensured. The proposed approach enables cost estimators to better understand the complete input variable selection process at the early stage of project development and provide a more accurate, rational, and systematic decision support tool.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Meharie

Gariy

Mutuku

et al. 2019

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…Other algorithms explored include regression analysis, MP, and support vector machine (Arage, S.S., Dharwadkar, N.V., 2017, Petruseva, S., et al, 2017. A comparison was made among CBR, MP, and regression algorithms by Kim, G.H., et al, (2004).…”

Section: A Brief Critical Review On Related Workmentioning

confidence: 99%

Towards Automated Cost Analysis, Benchmarking and Estimating in Construction: A Machine Learning Approach

Chen¹,

Hajderanj²,

Fiske³

2019

Proceedings of the International Conferences Big Data Analytics, Data Mining and Computational Intelligence 2019; And Theory An

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In this paper, a novel machine learning based approach is proposed for automated cost analysis on priced bill of quantities prepared by tenders in the construction industry. The proposed approach features: 1) An effective integration of structured project-specific information with surveyor's domain knowledge in order to model the complex interrelationships between the specifications and descriptions of an item and its trade category; 2) An effective transformation by supervised t-SNE to map the original data into a 2-dimensional space to tackle issues of high dimensionality in modelling and creating classifiers, and 3) Simple classifiers with a high classification accuracy and a good generalization capability. Relevant comparative experimental results have demonstrated the effectiveness of the proposed approach.

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“…e comparison analysis showed that SVM has higher accuracy and fewer errors than the ANN model. Two researchers presented the ordinary least square regression (OLSR) for construction cost forecasting of the Pune region in India [43,44]. e model was applied over a 12-year prediction period and attained 91%-97% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Developing an Integrative Data Intelligence Model for Construction Cost Estimation

et al. 2022

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Construction cost estimation is one of the essential processes in construction management. Project cost is a complex engineering problem due to various factors affecting the construction industry. Accurate cost estimation is important in construction management and significantly impacts project performance. Artificial intelligence (AI) models have been effectively implemented in construction management studies in recent years owing to their capability to deal with complex problems. In this research, extreme gradient boosting is developed as an advanced input selector algorithm and coupled with three AI models, including random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for cost estimation. Datasets were gathered based on a survey conducted on 90 building projects in Iraq. Statistical indicators and graphical methods were used to evaluate the developed models. Several input predictors were used, and XGBoost highlighted inflation as the most crucial parameter. The results indicated that the best prediction was attained by XGBoost-RF using six input parameters, with r-squared and the mean absolute percentage error equal to 0.87 and 0.25, respectively. The comparison results revealed that all AI models showed good prediction performance when applied to datasets affected by more than two parameters. The outcomes of this research revealed an optimistic strategy that can help decision makers select the influencing parameters in the early phases of project management. Also, developing a prediction model with high precision results can assist the project’s estimators in decreasing the errors in the cost estimation process.

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Cost estimation of civil construction projects using machine learning paradigm

Cited by 15 publications

References 10 publications

An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Towards Automated Cost Analysis, Benchmarking and Estimating in Construction: A Machine Learning Approach

Developing an Integrative Data Intelligence Model for Construction Cost Estimation

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