This study proposes a load identification for the safety monitoring of the steel structure based on measured strain data. Instead of parameterizing the stiffness of structure in the existing system identification researches, the loads on a structure and a matrix (the unit strain matrix) defined by the relationship between strain and load on structure are parameterized in this study. The error function is defined by the difference between measured strain and strain estimated by parameters. In order to minimize this error function, the genetic algorithm which is one of the optimization algorithm is applied and the parameters are found. The loads on the structure can be identified through the founded parameters and measured strain data. When the loads are changed, the unmeasured strains are estimated based on founded parameters and measured strains on changed state of structure. To verify the load identification algorithm in this paper, the static experimental test for 3 dimensional steel frame structure was implemented and the loads were exactly identified through the measured strain data. In case of loading changes, the unmeasured strains which are monitoring targets on the structure were estimated in acceptable error range (0.17~3.13%). It is expected that the identification method in this study is applied to the safety monitoring of steel structures more practically.

In the buildings, the systems of structures are influenced by the gravity load changes due to room alteration or construction stage. This paper proposes a system identification method establishing mass as well as stiffness to parameters in model updating process considering mass change in the buildings. In this proposed method, modified genetic algorithm, which is optimization technique, is applied to search those parameters while minimizing the difference of dynamic characteristics between measurement and FE model. To search more global solution, the proposed modified genetic algorithm searches in the wider search space. It is verified that the proposed method identifies the system of structure appropriately through the analytical study on a steel beam structure in the building. The comparison for performance of modified genetic algorithm and existing simple genetic algorithm is carried out. Furthermore, the existing model updating method neglecting mass change is performed to compare with the proposed method.

This study proposes an estimation method of strain distribution for multi-span steel beam structure under unspecific loading conditions. The estimation method in this paper employs the curve fitting using the least square method from measured strain data, not analytical method. To verify the proposed estimation method, a static loading test for multi-span steel beam on which distributed and concentrated loads act was conducted. The strain data for verification was measured by FBG sensors that have multiplexing technology. The analysis of the accuracy of strain estimation for distributed and concentrated loads and the errors by considering the number of measured points used in the estimation were conducted. In the maximum strain points, the strains could be estimated with the errors of 5.89% (loading step 1) and 6.26% (loading step 2). In case of decreasing the number of sensors, it was also confirmed that the errors increased (0.26~0.37%). Through the curve fitting method, it is possible to estimate the strain distribution (maximum strains and their locations) of multi-span beam for unspecific loads and go over the limit of the analytical estimation method which is suitable for specific distributed loads.

In this paper, the optimal seismic design method for inducing the beam-hinge collapse mechanism of steel moment frames is presented. This uses the non-dominated sorting genetic algorithm II(NSGA-II) as an optimal algorithm. The constraint condition for preventing the occurrence of plastic hinges at columns is used to induce the beam-hinge collapse mechanism. This method uses two objective functions to minimize the structural weight and maximize the dissipated energy. The proposed method is verified by the application to nine story steel moment frame example. The minimum column-to-beam strength ratio to induce the beam-hinge collapse mechanism are investigated based on the simulation results. To identify the influence of panel zone on the minimum column-to-beam strength ratio, three analytic modeling methods(nonlinear centerline model without rigid end offsets, nonlinear centerline model with rigid end offsets, nonlinear model with panel zones) are used.

The existing research on the damage detection method for building structures has considered the damages from the excessive loadings such as the earthquake. However, the structural performance of building structures could be reduced due to the deterioration based on the chloride, carbonation during the long-term time. Thus, to effectively manage the healthiness of structures, the deterioration influences on the structures should be checked. In this study, the corrosion of rebars by the chloride is considered as the deterioration factor. To consider the structural performance reduction of the corroded rebars, the yield strength, cross-sectional area, rupture strain of rebars and the compressive strength of cover concrete based on the corrosion level are estimated. These properties of rebars and cover concrete are used for the procedure to evaluate the structural performance reduction of structural member level and the building level. The moment-curvature analysis is performed to evaluate the structural performance reduction of structural member level. Also, the eigenvalue analysis and the pushover analysis are performed to investigate the natural period and mode shape and the strength and deformation performance of buildings, respectively.