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• W4385813903 startingPage "110749" @default.
• W4385813903 abstract "Accurate cost estimation in tunneling is key to the project’s success. Such information is critical for the early conceptual and design phases when key choices must be made. Numerous variables influence the cost of tunnel construction, and limited information is available at the early stages of design when the possible use of tunnels is being studied. Therefore, there is a limited number of models at engineers’ disposal to develop a proper cost estimate for tunneling projects. This study aimed to offer a model for estimating the construction cost of drilling and blasting tunnels in the preliminary stage of a project. For this purpose, an optimized gene expression programming (GEP) method was used based on the study of 900 data points obtained from ten drilling and blasting tunnels, which were randomly split into the training (800 data points) and testing (100 data points) datasets. With the experience of previously constructed tunnels, eleven parameters were considered the most effective for the tunnel’s construction cost. The best fit on predictions generated an equation for the optimized GEP model. Finally, by comparing the equation’s outputs with the actual costs and its behavior with practice, its potential ability to estimate the construction cost of drilling and blasting tunnels was approved. Moreover, the Graphical User Interface (GUI) of the GEP model was created as a practical tool for estimating the construction cost of tunnels. This model can reduce tunnel uncertainties and give ML development in tunnel planning." @default.
• W4385813903 created "2023-08-15" @default.
• W4385813903 creator A5013989648 @default.
• W4385813903 creator A5080141958 @default.
• W4385813903 date "2023-11-01" @default.
• W4385813903 modified "2023-09-27" @default.
• W4385813903 title "An optimized equation based on the gene expression programming method for estimating tunnel construction costs considering a variety of variables and indexes" @default.
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• W4385813903 doi "https://doi.org/10.1016/j.asoc.2023.110749" @default.
• W4385813903 hasPublicationYear "2023" @default.
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