• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1363-1373
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• 2022

The recent seismic events that occurred in South Korea have increased the interest in the re-evaluation of the seismic capacity of nuclear power plant (NPP) equipment, which is often conservatively estimated. To date, various approaches-including the Bayesian method proposed by the United States (US) Electric Power Research Institute -have been developed to quantify the seismic capacity of NPP equipment. Among these, the Bayesian approach has advantages in accounting for both prior knowledge and new information to update the probabilistic distribution of seismic capacity. However, data availability and region-specific issues exist in applying this Bayesian approach to Korean NPP equipment. Therefore, this paper proposes to construct an earthquake experience database by combining available earthquake records at Korean NPP sites and the general location of equipment within NPPs. Also, for the better representation of the seismic demand of Korean earthquake datasets, which have distinct seismic characteristics from those of the US at a high-frequency range, a broadband frequency range optimization is suggested. The proposed data construction and seismic demand optimization method for seismic capacity re-evaluation are demonstrated and tested on a 480 V motor control center of a South Korea NPP.

• Earthquakes and Structures
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• 제19권3호
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• pp.157-174
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• 2020

The in-situ pushover test differs from the shake-table test because it is performed outdoors and thus its size is not restricted by space, which allows us to test a full-size building. However, to build a new full-size building for the test is not economical, consequently scholars around the world usually make scale structures or full-scale component units to be tested in the laboratory. However, if in-situ pushover tests can be performed on full-size structures, then the seismic behaviors of buildings during earthquakes can be grasped. In view of this, this study conducts two in-situ pushover tests of reinforced concrete (RC) buildings. One is a masonry-infilled RC building with openings (the openings ratio of masonry infill wall is between 24% and 51%) and the other is an RC building without masonry infill. These two in-situ pushover tests adopt obsolescent RC buildings, which will be demolished, to conduct experiment and successfully obtain seismic capacity curves of the buildings. The test results are available for the development or verification of a seismic evaluation model. This paper uses ASCE 41-17 as the main evaluation model and is accompanied by a simplified pushover analysis, which can predict the seismic capacity curves of low-rise buildings in Taiwan. The predicted maximum base shear values for masonry-infilled RC buildings with openings and for RC buildings without masonry infill are, respectively, 69.69% and 87.33% of the test values. The predicted initial stiffness values are 41.04% and 100.49% of the test values, respectively. It can be seen that the ASCE 41-17 evaluation model is reasonable for the RC building without masonry infill walls. In contrast, the analysis result for the masonry infilled RC building with openings is more conservative than the test value because the ASCE 41-17 evaluation model is limited to masonry infill walls with an openings ratio not exceeding 40%. This study suggests using ASCE 41-17's unreinforced masonry wall evaluation model to simulate a masonry infill wall with an openings ratio greater than 40%. After correction, the predicted maximum base shear values of the masonry infilled RC building with openings is 82.60% of the test values and the predicted initial stiffness value is 67.13% of the test value. Therefore, the proposed method in this study can predict the seismic behavior of a masonry infilled RC frame with large openings.

• Smart Structures and Systems
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• 제20권5호
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• pp.549-562
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• 2017

The US railroad network carries 40% of the nation's total freight. Railroad bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a railroad bridge under train crossings is one parameter of interest to railroad bridge owners, as it quantifies a bridge's ability to perform safely and addresses its serviceability. Railroad bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of railroad bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of railroad bridges without a fixed reference.