Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network

Hakami, Waled; Hassan, Awad Saad

doi:10.2478/bjreecm-2019-0007

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…In another emerging economy, this time that of Yemen, a study was carried out to provide a cost prediction tool based on ANN models by Hakami and Hassan [42], based on historical data from 136 buildings constructed from 2011 to 2015. They included 17 independent variables to be implemented in their model to produce a preliminary total construction cost estimate.…”

Section: Application Of Anns To Cost Estimation In the Particular Fie...mentioning

confidence: 99%

A Final Cost Estimating Model for Building Renovation Projects

Papadimitriou,

Aretoulis

2024

Buildings

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The construction sector in Greece has been developing radically in the field of building renovations. The foremost problem for projects in the building construction industry is producing an accurate and reliable cost estimate at the onset of construction. The artificial neural network (AΝΝ) approach, using data available at the early stages of the project, can help resolve or prevent any kind of difficulty that could make the successful completion of a building less likely. ANNs have been highly efficient in gaining results which could prevent the failure of building constructions projects. The ultimate goal is to highlight the usefulness of the adoption of ANNs models to predict the final cost of a building renovation project. Thus, construction companies could avoid financial failure, provided that the gap between cost prediction and final cost for renovation projects is minimized. This paper presents an artificial neural network (ANN) approach for predicting renovation costs in Greek construction projects. The study, based on a comprehensive literature review and real renovation data from construction companies, employs IBM SPSS Statistics software to build, train, and test the ANN model. The model, which uses initial cost, estimated time, and initial demolition cost as inputs, is based on the radial basis function procedure. The model presents high performance with up to 2% sum of squares error and near zero relative error, demonstrating the ANN’s effectiveness in estimating total renovation costs.

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Section: Application Of Anns To Cost Estimation In the Particular Fie...mentioning

confidence: 99%

A Final Cost Estimating Model for Building Renovation Projects

Papadimitriou,

Aretoulis

2024

Buildings

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“…They choose 3 hidden layers model with 20 projects data. Moreover, in Yemen, Hakami and Hassan (2019) studied preliminary construction cost estimation by using ANN with one hidden layer and 136 different projects. As a result of their study, they found the MAPE value as 0.14%.…”

Section: Previous Studies On Cost Estimationmentioning

confidence: 99%

Forecasting Preliminary Order Cost to Increase Order Management Performance

Günsari¹,

Kaya²,

Ekinci

2022

International Journal of Business Analytics

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In this study, the cost estimation to be used in the optimization of proposed order price offer is made by artificial neural network (ANN) method. A case study is performed by the real data of a company, and the forecast results of the traditional arithmetic model used by the company and the proposed ANN based method are compared and it is seen that the proposed method results outperform the other. The biggest contribution of this study to companies is to increase the company’s order management performance by helping the company to make more accurate pricing due to more accurate cost estimation. Moreover, to the best of our knowledge, this is the first study on forecasting preliminary order cost in the apparel industry and fills an important gap in the literature.

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A Multilayer Perception for Estimating the Overall Risk of Residential Projects in the Conceptual Stage

2022

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The ability to foresee hazards early plays a critical role in estimating the entire cost of a project. Although several studies have established models to predict the total cost of a project at a conceptual stage, there remains a research vacuum in measuring the overall risk at this stage. Using artificial neural networks, this research provides a strategy for estimating the overall risk in residential projects at the conceptual stage. There are eight important components in the suggested paradigm. The model was created using data from 149 projects. In the first hidden layer in the model, there are five neurons, and in the second hidden layer, there are three neurons. The suggested model’s mean absolute error rate was 11.7%. In the conceptual stage of residential projects, the number of floors, the type of interior finishes, and the implementation of risk management processes are the significant aspects that influence the overall risk. The proposed model assists project managers in precisely estimating the project’s overall risk, which leads to a more accurate estimation of the contract’s entire worth at the conceptual stage, allowing the stakeholders to decide whether or not to proceed with the project.

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Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network

Cited by 3 publications

References 13 publications

A Final Cost Estimating Model for Building Renovation Projects

A Final Cost Estimating Model for Building Renovation Projects

Forecasting Preliminary Order Cost to Increase Order Management Performance

A Multilayer Perception for Estimating the Overall Risk of Residential Projects in the Conceptual Stage

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