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.

Accurate assessment of CO 2 emission from buildings requires gathering CO 2 emission data of various construction materials. Unfortunately, the amount of available data is limited in most countries. This study was conducted to present the CO 2 emission data of concrete, which is the most important construction material in Korea, by conducting a statistical analysis of the concrete mix proportion. Finally, regression models that can be used to estimate the CO 2 emission of concrete in all strengths were developed, and the validity of these models was evaluated using 24 and 35MPa concrete data. The validation test showed that the error ratio of the estimated value did not exceed a maximum of 5.33%. This signifies that the models can be used in acquiring the CO 2 emission data of concrete in all strengths. The proposed equations can be used in assessing the environmental impact of various construction structural designs by presenting the CO 2 emission data of all concrete types.

Seventy-six species of fishes, representing 60 genera and 34 families, were recorded from tidal pools on Jeju Island, southern Korea. The major families in terms of species were the Gobiidae (11 species), Pomacentridae (8 species), Blenniidae (6 species), and Labridae (5 species). Thirty-nine species were classified as tropical, 26 as temperate and 11 as subtropical.

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.