A steel frame is one of the most commonly used structural systems due to its resistance to various types of applied loads. Many studies have been conducted to investigate the effects of connection flexibility, support conditions, and beam-to-column stiffness ratio on the story drift of a frame. Based on the results of these studies, several design guides have been proposed. This research has been conducted to predict the actual behavior of a double angle connection, and to establish its effect on the story drift and the maximum allowable load of a steel frame. For these purposes, several experimental tests were conducted and a simplified analytical model was proposed. This simplified analytical model consists of four spring elements as well as a column member. In addition, a point bracing system was proposed to control the excessive story drift of an unbraced steel frame.

Structural dynamic properties such as natural frequency depend not only on damage but also environmental condition (i.e., Temperature). Without removing the variation of environmental condition in the damage detection, false-positive or negative damage diagnosis may occur so that structural health monitoring becomes unreliable. One of methods used to solve this problem is to construct regression model based on structural responses with the environmental factors. However, it is difficult to determine where and which environmental variables to measure. One alternative is to remove the variability due to environmental variation without measurement of environmental variables. However, the performance of this method is depending on how to define the reference data set. Generally, there is no prior information on reference condition (i.e., healthy condition) during data mining. Reference condition is determined based on subjective perspective with human-intervention. To overcome the drawback of current methods, this paper investigates adaptive PCA technique for the monitoring of structural damage detection under environmental change. This method is not required to determine the reference condition and measure the environmental variables. Proposed method is tested on numerically simulated data for a range of noise in measurement under environmental variation.