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- W4385380332 abstract "Cost management plays a very important role in the success of a project. Effective and timely control of costs ensures project implementation progress and enhances project efficiency. Currently, investors opt for the design and build contract to expedite construction. Therefore, precise cost estimation in the early stages can enhance the contractor’s competitiveness. The existing estimates lack accuracy, prompting this paper to propose new computational methods based on artificial intelligence (AI) platforms, specifically: case-based reasoning (CBR), random forest (RF) model, and artificial neural networks (ANN). These methods will be applied to estimate the cost of facade work, which typically accounts for about 20–30% of the total construction cost. Creating models that provide accurate cost estimates in the early stages of the project is valuable for decision-makers. To establish the models, Solver in the Excel tool, Python 3.10.9 & Spyder IDE 5.4.1, and SPSS Neural Networks 22 by IBM Software Group were employed for CBR, RF, and ANN, respectively. The cost data from thirty-six actual cases of residential building projects were randomly separated from the dataset for training and testing purposes." @default.
- W4385380332 created "2023-07-30" @default.
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- W4385380332 date "2023-07-29" @default.
- W4385380332 modified "2023-09-27" @default.
- W4385380332 title "Enhancing accuracy in cost estimation for façade works: integration of case-based reasoning, random forest, and artificial neural network techniques" @default.
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- W4385380332 doi "https://doi.org/10.1007/s42107-023-00842-8" @default.
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