A model utilizing the artificial neural network in cost estimation of construction projects in Jordan

Al-Tawal, Dareen Ryied; Arafeh, Mazen; Sweis, Ghaleb

doi:10.1108/ecam-06-2020-0402

Cited by 17 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The models were developed using ANN in IBM-SPSS. To obtain the best model architecture, the models were developed using the trial-and-error method used by Mensah et al (2016), Nani et al (2017) and Al-Tawal et al (2020), which has proven to help identify optimum network parameters and network performance. Several models were developed until the performance stopped improving and the best-fit model was retained for each of the MLP and RBF.…”

Section: Developing the Neural Network Modelsmentioning

confidence: 99%

Modeling duration of building renovation projects

Lawal¹,

Ahmadu

Abdullahi

et al. 2023

JFMPC

View full text Add to dashboard Cite

Purpose This study aims to develop a building renovation duration prediction model incorporating both scope and non-scope factors. Design/methodology/approach The study used a questionnaire to obtain basic information relating to identified project scope factors as well as information relating to the impact of the non-scope factors on the duration of building renovation projects. The study retrieved 121 completed questionnaires from construction firms on tertiary education trust fund (TETFund) building renovation projects. Artificial neural network was then used to develop the model using 90% of the data, while mean absolute percentage error was used to validate the model using the remaining 10% of the data. Findings Two artificial neural network models were developed – a multilayer perceptron (MLP) and a radial basis function (RBF) model. The accuracy of the models was 86% and 80%, respectively. The developed models’ predictions were not statistically different from those of actual duration estimates with less than 20% error margin. Also, the study found that MLP models are more accurate than RBF models. Research limitations/implications The developed models are only applicable to projects that suit the characteristics and nature of the data used to develop the models. Hence, models can only predict the duration of building renovation projects. Practical implications The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it. Social implications The developed models are expected to serve as a tool for realistic estimation of the duration of building renovation projects and thus, help construction project managers to effectively plan and manage it; it also helps clients to effectively benchmark projects duration and contractors to accurately estimate duration at tendering stage. Originality/value The study presents models that combine both scope and non-scope factors in predicting the duration of building renovation projects so as to ensure more realistic predictions.

show abstract

Section: Developing the Neural Network Modelsmentioning

confidence: 99%

Modeling duration of building renovation projects

Lawal¹,

Ahmadu

Abdullahi

et al. 2023

JFMPC

View full text Add to dashboard Cite

show abstract

“…Other researchers also applied ANNs, casebased reasoning (CBR), GA and RA to enhance the cost estimation process (Adeli and Wu, 1998;Hegazy and Ayed, 1998;Al-Tabtabai et al, 1999;Chou, 2009a;Kim and Kim, 2010;Cirilovic et al, 2014;Adel et al, 2016;Meharie et al, 2022). Cost estimation models capable of modeling construction costs as a function of influencing factors are highly likely to generate reliable estimates (Wilmot and Cheng, 2003;Al-Tawal et al, 2021). With increasing transportation needs, funding limitations at both the federal and state levels, and the high cost of transportation improvement projects, it is important to have a toolbox of techniques that support accurate estimation of project costs (AASHTO, 2013).…”

Section: Cost Estimation Of Highway Bid Itemsmentioning

confidence: 99%

“…Asmar et al (2011) also defined a conceptual cost estimate as an estimate prepared at the point at which only a general idea exists about what the project will entail. As a result, estimators must infer many of the cost components from historical costs associated with past projects of similar scope (Trost and Oberlender, 2003;Asmar et al, 2011;Al-Tawal et al, 2021). The construction sector over the years has focused its efforts and resources on improving the quality of cost estimates (Zhang et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

Conceptual cost estimation of highway bid items – A systematic literature review

Awuku

Asa

Baffoe-Twum

et al. 2022

ECAM

View full text Add to dashboard Cite

PurposeChallenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.Design/methodology/approachThis paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.FindingsFindings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.Originality/valueThis paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

show abstract

“…Cost is an essential criterion of project performance due to its impact on feasibility studies and choosing design alternatives [19]. Cost studies describe and evaluate the costs of buildings and other construction projects [20]. ese studies seek to maximize the project's revenue by using the available resources.…”

Section: Introductionmentioning

confidence: 99%

Developing an Integrative Data Intelligence Model for Construction Cost Estimation

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

A model utilizing the artificial neural network in cost estimation of construction projects in Jordan

Cited by 17 publications

References 16 publications

Modeling duration of building renovation projects

Modeling duration of building renovation projects

Conceptual cost estimation of highway bid items – A systematic literature review

Developing an Integrative Data Intelligence Model for Construction Cost Estimation

Contact Info

Product

Resources

About