توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Steel Buildings Damage Classification by damage spectrum and Decision Tree Algorithm
اطلاعات انتشار: Journal of Rehabilitation in Civil Engineering، سوم،شماره۱، ۲۰۱۵، سال
تعداد صفحات: ۱۹
Results of damage prediction in buildings can be used as a useful tool for managing and decreasing seismic risk of earthquakes. In this study, damage spectrum and C4.5 decision tree algorithm were utilized for damage prediction in steel buildings during earthquakes. In order to prepare the damage spectrum, steel buildings were modeled as a single–degree–of–freedom (SDOF) system and time–history nonlinear analysis was carried out to develop a set of SDOF structures. Then, damage index was used to prepare the damage spectrum. Data parameters required for training and evaluating the C4.5 decision tree algorithm were obtained from the results of damage spectra for steel structures and using Krawinkler damage index Also, two decision trees were trained based on quantitative indices. The first decision tree determined whether damage occurred in buildings or not and the second predicted severity of damage as repairable, beyond repair, or collapse. decision tree classification algorithm was used to predict damage to steel structures.

۲A new two–stage method for damage identification in linear–shaped structures via Grey System Theory and optimization algorithm
اطلاعات انتشار: Journal of Rehabilitation in Civil Engineering، سوم،شماره۲، ۲۰۱۵، سال
تعداد صفحات: ۱۴
The main objective of this paper is concentrated on presenting a new two–stage method for damage localization and quantification in the linear–shaped structures. A linear–shaped structure is defined as a structure in which all elements are arranged only in a straight line. At the first stage, by employing Grey System Theory (GST) and diagonal members of the Generalized Flexibility Matrix (GFM), a new damage index is suggested for damage localization using only the first several modes’ data. It is followed by the second stage which is devoted to damage quantification in the damaged elements by proposing an optimization–based procedure to formulate fault prognosis problem as an inverse problem, and it is solved by the Pattern Search Algorithm (PSA) to reach the optimal solution. The applicability of the presented method is demonstrated by studying different damage patterns on three numerical examples of linear–shaped structures. In addition, the stability of the presented method in the presence of random noises is evaluated. The obtained results reveal good and acceptable performance of the proposed method for detecting damage in linear–shaped structures.
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