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A platform for research: civil engineering, architecture and urbanism
Cost Estimation of Reinforced Concrete Buildings Using Neural Network and Multi Regression Analysis
https://orcid.org/http://orcid.org/0009-0008-7816-3105
https://orcid.org/http://orcid.org/0000-0002-4803-4272
In this study, an Artificial Neural Network and Multi Regression Analysis have been used to evaluate the strengthening cost and total cost of reinforced concrete buildings. To obtain strengthening cost, 377 reinforced concrete buildings which have been designed according to the 1975, 1997 and 2007 Turkish Earthquake Codes have been checked and strengthened according to the new code 2018 Turkish Earthquake Code. After that, to obtain the total cost (rough total construction cost) of the buildings according to the new code, 84 different reinforced concrete buildings have been designed according to the 2018 Turkish Earthquake Code. The professional program Sta4CAD has been used to model, analyze and strengthening those reinforced concrete buildings. When the old buildings are checked according to the new code, they may not satisfy the conditions of the code since the new code has more general rules. According to that, those old buildings will need strengthening. Section enlargement method, addition of shear wall and other methods have been used so that the old buildings can satisfy the new code provisions. For strengthening cost of Reinforced Concrete buildings, 13 parameters have been chosen accordingly. The output parameter for the study is the strengthening cost, which are in Turkish Lira according to the unit prices of materials in Turkey. For rough total cost according to TEC 2018 8 parameters have been used. According to the study, the prediction accuracy of the Artificial Neural Network that has been trained, was found to be 94% accuracy for the strengthening cost. However in the regression analysis method, 71% accuracy has been found. For total cost, Artificial Neural Network gave 97% accuracy and for regression analysis method 95% accuracy has been found.
Cost Estimation of Reinforced Concrete Buildings Using Neural Network and Multi Regression Analysis
https://orcid.org/http://orcid.org/0009-0008-7816-3105
https://orcid.org/http://orcid.org/0000-0002-4803-4272
In this study, an Artificial Neural Network and Multi Regression Analysis have been used to evaluate the strengthening cost and total cost of reinforced concrete buildings. To obtain strengthening cost, 377 reinforced concrete buildings which have been designed according to the 1975, 1997 and 2007 Turkish Earthquake Codes have been checked and strengthened according to the new code 2018 Turkish Earthquake Code. After that, to obtain the total cost (rough total construction cost) of the buildings according to the new code, 84 different reinforced concrete buildings have been designed according to the 2018 Turkish Earthquake Code. The professional program Sta4CAD has been used to model, analyze and strengthening those reinforced concrete buildings. When the old buildings are checked according to the new code, they may not satisfy the conditions of the code since the new code has more general rules. According to that, those old buildings will need strengthening. Section enlargement method, addition of shear wall and other methods have been used so that the old buildings can satisfy the new code provisions. For strengthening cost of Reinforced Concrete buildings, 13 parameters have been chosen accordingly. The output parameter for the study is the strengthening cost, which are in Turkish Lira according to the unit prices of materials in Turkey. For rough total cost according to TEC 2018 8 parameters have been used. According to the study, the prediction accuracy of the Artificial Neural Network that has been trained, was found to be 94% accuracy for the strengthening cost. However in the regression analysis method, 71% accuracy has been found. For total cost, Artificial Neural Network gave 97% accuracy and for regression analysis method 95% accuracy has been found.
Cost Estimation of Reinforced Concrete Buildings Using Neural Network and Multi Regression Analysis
https://orcid.org/http://orcid.org/0009-0008-7816-3105
https://orcid.org/http://orcid.org/0000-0002-4803-4272
In this study, an Artificial Neural Network and Multi Regression Analysis have been used to evaluate the strengthening cost and total cost of reinforced concrete buildings. To obtain strengthening cost, 377 reinforced concrete buildings which have been designed according to the 1975, 1997 and 2007 Turkish Earthquake Codes have been checked and strengthened according to the new code 2018 Turkish Earthquake Code. After that, to obtain the total cost (rough total construction cost) of the buildings according to the new code, 84 different reinforced concrete buildings have been designed according to the 2018 Turkish Earthquake Code. The professional program Sta4CAD has been used to model, analyze and strengthening those reinforced concrete buildings. When the old buildings are checked according to the new code, they may not satisfy the conditions of the code since the new code has more general rules. According to that, those old buildings will need strengthening. Section enlargement method, addition of shear wall and other methods have been used so that the old buildings can satisfy the new code provisions. For strengthening cost of Reinforced Concrete buildings, 13 parameters have been chosen accordingly. The output parameter for the study is the strengthening cost, which are in Turkish Lira according to the unit prices of materials in Turkey. For rough total cost according to TEC 2018 8 parameters have been used. According to the study, the prediction accuracy of the Artificial Neural Network that has been trained, was found to be 94% accuracy for the strengthening cost. However in the regression analysis method, 71% accuracy has been found. For total cost, Artificial Neural Network gave 97% accuracy and for regression analysis method 95% accuracy has been found.
Cost Estimation of Reinforced Concrete Buildings Using Neural Network and Multi Regression Analysis
Lecture Notes in Civil Engineering
Türker, Umut (editor) / Eren, Özgür (editor) / Uygar, Eris (editor) / Rajab, Mohamad Abou (author) / Özay, Giray (author)
International Conference on Advances in Civil Engineering ; 2023
Sustainable Civil Engineering at the Beginning of Third Millennium ; Chapter: 22 ; 245-254
2024-04-29
10 pages
Article/Chapter (Book)
Electronic Resource
English
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