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      This paper suggests how to identify the sources of the error in the column shortening analysis by using the field measurement. The causes of the error between structural analysis and field measurement can be divided into the loads and the column properties. These two error sources are acting simultaneously and make it difficult to identify the causes of the error. The method for identifying error sources by using the strain of upper and lower columns having equal section and material properties has been developed to increase the accuracy of the column shortening prediction. In order to verify efficiency of the proposed method, several numerical analysis having assumed errors were carried out. The results of the numerical analysis showed that the error sources could be identified in all cases. The proposed method can be used to investigate the errors between the prediction and the measurement and to enhance the accuracy of the prediction.
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      This paper suggests how to identify the sources of the error in the column shortening analysis by using the field measurement. The causes of the error between structural analysis and field measurement can be divided into the loads and the column prope...

      This paper suggests how to identify the sources of the error in the column shortening analysis by using the field measurement. The causes of the error between structural analysis and field measurement can be divided into the loads and the column properties. These two error sources are acting simultaneously and make it difficult to identify the causes of the error. The method for identifying error sources by using the strain of upper and lower columns having equal section and material properties has been developed to increase the accuracy of the column shortening prediction. In order to verify efficiency of the proposed method, several numerical analysis having assumed errors were carried out. The results of the numerical analysis showed that the error sources could be identified in all cases. The proposed method can be used to investigate the errors between the prediction and the measurement and to enhance the accuracy of the prediction.

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