• Advances in concrete construction
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• v.12 no.6
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• pp.469-477
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• 2021

Keratin fibres are waste products of the poultry industry. Natural materials made from chicken feather fibres (CFFs) are used in concrete-reinforced composites in this study. Brittleness is a major problem of high-strength concrete (HSC) that leads to sudden failure at the ultimate capacity of concrete. Hence, this work aims to investigate effects of using CFFs on improving the brittle behaviour of HSC. Two scenarios are performed to analyse the effectiveness of using CFFs. HSC containing different ratios of CFF (0% as the control, 0.5%, 1%, 1.5%, 2%, and 3%) by volume are tested in the first scenario. Glass fibres (GF) are used to replace CFFs in the other scenario. Tests of fresh, hardened and morphological properties for concrete are performed. Results showed the enhanced brittle behaviour of HSC when using both types of fibres. The preferable ratio of both types of fibres is 1% by volume. Flexural and splitting tensile strengths increased by about 44.9 % and 42.65 % for mixes containing 0.1% GF, respectively. While they were increased by about 21.6 % and 21.16 % for mixes containing 0.1% CFF, respectively.

• Earthquakes and Structures
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• v.23 no.6
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• pp.503-515
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• 2022

Global or partial damage to a structure due to the failure of gravity or lateral load-bearing elements is called progressive collapse. In the present study, the alternate load path (ALP) method introduced by GSA and UFC 4-023-03 guidelines is used to evaluate the progressive collapse in special steel moment-resisting frame (SMRF) buildings. It was assumed that the progressive collapse is due to the earthquake force and its effects after the removal of the elements still remain on the structures. Therefore, nonlinear dynamic time history analysis employing 7 earthquake records is used to investigate this phenomenon. Internal and external column removal scenarios are investigated and the stiffness of the connections is changed from semi-rigid to rigid. The results of the analysis performed in the OpenSees program show that the loss of the bearing capacity of an exterior column due to a seismic event and the occurrence of progressive collapse can increase the inter-story drift of the structure with semi-rigid connections by more than 50% and make the structure unable to satisfy the life safety performance level. Furthermore, connection stiffness severely affects the redistribution of forces and moments in the adjacent elements of the removed column.

• Earthquakes and Structures
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• v.21 no.5
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• pp.461-475
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• 2021

Strategic structures are a potential target of the growing terrorist attacks, so their performance under explosion hazard has been paid attention by researchers in the last years. In this regard, the aim of this study is to evaluate the blast-resistance performance of lead-rubber bearing (LRB) base isolation system based on a probabilistic framework while uncertainties related to the charge weight and standoff distance have been taken into account. A sensitivity analysis is first performed to show the effect of explosion uncertainty on the response of base-isolated buildings. The blast fragility curve is then developed for three base-isolated steel moment-resisting buildings with different heights of 4, 8 and 12 stories. The results of sensitivity analysis show that although LRB has the capability of reducing the peak response of buildings under explosion hazard, this control system may lead to increase in the peak response of buildings under some explosion scenarios. This shows the high importance of probabilistic-based assessment of isolated structures under explosion hazard. The blast fragility analysis shows effective performance of LRB in mitigating the probability of failure of buildings. Therefore, LRB can be introduced as effective control system for the protection of buildings from explosion hazard regarding uncertainty effect.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2747-2756
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• 2023

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

• Journal of the Korean Society of Safety
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• v.38 no.1
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• pp.18-24
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• 2023

Severe natural disasters and man-made attacks such as terrorism are causing unprecedented disruptions in power systems. Due to rapid climate change and the aging of energy infrastructure, both the frequency of failure and the level of damage are expected to increase. Resilience is a concept proposed to respond to extreme disaster events that have a low probability of occurrence but cause enormous damage and is defined as the ability of a system to recover to its original function after a disaster. Resilience is a comprehensive indicator that can include system performance before and after a disaster and focuses on preparing for all possible disaster scenarios and having quick and efficient recovery actions after an incident. Various studies have been conducted to evaluate resilience, but studies on economic damage considering the duration of a power outage are scarce. In this study, we propose an optimal algorithm that can identify failures after an extreme disaster and restore the load on the distribution system through emergency distributed power generation input and system reconfiguration. After that, the cost of power outage damage is analyzed by applying VoLL and CDF according to each restoration strategy.

• Journal of the Korean Society of Safety
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• v.38 no.1
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• pp.87-92
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• 2023

The number of unmanned light rail train operators is continuously increasing in Korea. In a failure event during an operation due to the nature of the unmanned operation, recovery is performed based on the remote control. However, if remote recovery is not feasible, safety personnel arrive at the train to resume the train operation. There are regulations on safety personnel and the suspension time of the train operation. However, there is currently no rule for safety personnel deployment. Currently, railway operating organizations operate in three scenarios: safety personnel on board trains, stationed at stations, and deployed at major stations. Four major factors influence the downtime for each emergency response scenario. However, these four influencing factors vary too much to predict results with simple calculations. In this study, four influencing factors were considered as random variables with high uncertainty. In addition, the Monte Carlo method was applied to each scenario for the safety personnel deployment to predict train service downtime. This study found a 17% difference in train service suspension by safety personnel deployment scenario. The results of this study can be used in setting service goals, such as standards for future safety personnel placement and frequency of service interruptions.

• Coupled systems mechanics
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• v.12 no.2
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• pp.167-181
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• 2023

The determination of the blast protection level and the corresponding minimum load-bearing capacity for a laminated glass (LG) window is of crucial importance for safety and security design purposes. In this paper, the focus is given to the window response under near-field blast loading, i.e., where relatively small explosives would be activated close to the target, representative of attack scenarios using small commercial drones. In general, the assessment of the load-bearing capacity of a window is based on complex and expensive experiments, which can be conducted for a small number of configurations. On the other hand, nowadays, validated numerical simulations tools based on the Finite Element Method (FEM) are available to partially substitute the physical tests for the assessment of the performance of various LG systems, especially for the far-field blast loading. However, very little literature is available on the LG window performance under near-field blast loads, which differs from far-field situations in two points: i) the duration of the load is very short, since the blast wavelength tends to increase with the distance and ii) the load distribution is not uniform over the window surface, as opposed to the almost plane wave configuration for far-field configurations. Therefore, the current study focuses on the performance assessment and structural behaviour of LG windows under near-field blasts. Typical behavioural trends are investigated, by taking into account possible relevant damage mechanisms in the LG window components, while size effects for target LG windows are also addressed under a multitude of blast loading configurations.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.211-219
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• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.6
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• pp.293-301
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• 2023

Existing reinforced concrete buildings with seismically deficient details have premature failure under earthquake loads. The fiber-reinforced polymer column jacket enhances the lateral resisting capacities with additional confining pressures. This paper aims to quantify the retrofit effect varying the confinement and stiffness-related parameters under three earthquake scenarios and establish the retrofit strategy. The retrofit effects were estimated by comparing energy demands between non-retrofitted and retrofitted conditions. The retrofit design parameters are determined considering seismic hazard levels to maximize the retrofit effects. The critical parameters of the retrofit system were determined by the confinement-related parameters at moderate and high seismic levels and the stiffness-related parameters at low seismic levels.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.79-91
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• 2024

Large warehouses are building automation systems to increase efficiency. However, small warehouses, military bases, and local stores are unable to introduce automated logistics systems due to lack of space and budget, and are handling tasks manually, failing to improve efficiency. To solve this problem, this study designed small loading and unloading equipment that can be mounted on transportation vehicles. The equipment can be controlled remotely and is automatically controlled from the point where pallets loaded with cargo are visible using real-time video from an attached camera. Cargo recognition and control command generation for automatic control are achieved through a newly designed deep learning model. This model is designed to be optimized for loading and unloading equipment and mission environments based on the YOLOv3 structure. The trained model recognized 10 types of palettes with different shapes and colors with an average accuracy of 100% and estimated the state with an accuracy of 99.47%. In addition, control commands were created to insert forks into pallets without failure in 14 scenarios assuming actual loading and unloading situations.