• Title/Summary/Keyword: Corrosion Model

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Seismic damage mitigation of bridges with self-adaptive SMA-cable-based bearings

  • Zheng, Yue;Dong, You;Chen, Bo;Anwar, Ghazanfar Ali
    • Smart Structures and Systems
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    • v.24 no.1
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    • pp.127-139
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    • 2019
  • Residual drifts after an earthquake can incur huge repair costs and might need to replace the infrastructure because of its non-reparability. Proper functioning of bridges is also essential in the aftermath of an earthquake. In order to mitigate pounding and unseating damage of bridges subjected to earthquakes, a self-adaptive Ni-Ti shape memory alloy (SMA)-cable-based frictional sliding bearing (SMAFSB) is proposed considering self-adaptive centering, high energy dissipation, better fatigue, and corrosion resistance from SMA-cable component. The developed novel bearing is associated with the properties of modularity, replaceability, and earthquake isolation capacity, which could reduce the repair time and increase the resilience of highway bridges. To evaluate the super-elasticity of the SMA-cable, pseudo-static tests and numerical simulation on the SMA-cable specimens with a diameter of 7 mm are conducted and one dimensional (1D) constitutive hysteretic model of the SMAFSB is developed considering the effects of gap, self-centering, and high energy dissipation. Two types of the SMAFSB (i.e., movable and fixed SMAFSBs) are applied to a two-span continuous reinforced concrete (RC) bridge. The seismic vulnerabilities of the RC bridge, utilizing movable SMAFSB with the constant gap size of 60 mm and the fixed SMAFSBs with different gap sizes (e.g., 0, 30, and 60 mm), are assessed at component and system levels, respectively. It can be observed that the fixed SMAFSB with a gap of 30 mm gained the most retrofitting effect among the three cases.

Evaluation on De-Icing Salts Laden Environment of Road in Seoul (제설제에 노출된 서울시내 도로 시설물의 열화 환경 분석)

  • Yoon, In-Seok
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.26 no.1
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    • pp.1-9
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    • 2022
  • De -icing salts have been used commonly in areas where snow or ice is a seasonal safety hazard for roadway, however, the salts is one of main causes on serious deterioration of road infrastructures in crowded urban city like Seoul. In order to establish maintenance strategy of road infrastructures under de-icing salts laden environment, it is necessary to examine environmental characteristics and its response to the existing facilities. The purpose of this study is to evaluate the deterioration environment of road infrastructures. Additional purpose is to develop a design model and details for durability design of infrastructures under de-icing salts laden environment, considering mainly a build-up rate of surface chlorides. Concentration of external chloride solution and surface chloride content were calculated at the level of average de-icing salts for 5 years, ratio of auxiliary road of 17.5 to 30%, and effective exposure area to snow 50 to 80%. The chloride build-up rate was 0.073 ~ 0.077% / year and the maximum surface chloride content was calculated to be 2.2 ~ 2.31% by concrete wt. This study is expected to be used for establishing integrated strategy of road infrastructures, such as predicting chloride profiles or degree of chemical corrosion to exposure concrete.

Field Applicability Study of Hull Crack Detection Based on Artificial Intelligence (인공지능 기반 선체 균열 탐지 현장 적용성 연구)

  • Song, Sang-ho;Lee, Gap-heon;Han, Ki-min;Jang, Hwa-sup
    • Journal of the Society of Naval Architects of Korea
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    • v.59 no.4
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    • pp.192-199
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    • 2022
  • With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

Study on Mode I Fracture Toughness and FEM analysis of Carbon/Epoxy Laminates Using Acoustic Emission Signal (음향 방출 신호를 이용한 탄소/에폭시 적층판의 Mode I 파괴 인성 및 유한요소해석에 관한 연구)

  • Cho, Hyun-jun;Jeon, Min-Hyeok;No, Hae-Ri;Kim, In-Gul
    • Composites Research
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    • v.35 no.2
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    • pp.61-68
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    • 2022
  • Composite materials have been used in aerospace industry and many applications because of many advantages such as specific strength and stiffness and corrosion resistance etc. However, it is vulnerable to impacts, these impact lead to formation of cracks in composite laminate and failure of structures. In this paper, we analyzed Mode I fracture toughness of Carbon/Epoxy laminates using acoustic emission signal. DCB test was carried out to analyze Mode I failure characterization of Carbon/Epoxy laminates, and AE sensor was attached to measure AE signal induced by failure of specimen. Fracture toughness was calculated using cumulative AE energy and measured crack length using camera. The calculated fracture toughness was applied in FE model and the result of FE analysis compared with DCB test results. The results show good agreement with between FEM and DCB test results.

Damaged cable detection with statistical analysis, clustering, and deep learning models

  • Son, Hyesook;Yoon, Chanyoung;Kim, Yejin;Jang, Yun;Tran, Linh Viet;Kim, Seung-Eock;Kim, Dong Joo;Park, Jongwoong
    • Smart Structures and Systems
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    • v.29 no.1
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    • pp.17-28
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    • 2022
  • The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

Optimised neural network prediction of interface bond strength for GFRP tendon reinforced cemented soil

  • Zhang, Genbao;Chen, Changfu;Zhang, Yuhao;Zhao, Hongchao;Wang, Yufei;Wang, Xiangyu
    • Geomechanics and Engineering
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    • v.28 no.6
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    • pp.599-611
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    • 2022
  • Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

Buckling resistance behavior of WGJ420 fire-resistant weathering steel columns under fire

  • Yiran Wu;Xianglin Yu;Yongjiu Shi;Yonglei Xu;Huiyong Ban
    • Steel and Composite Structures
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    • v.47 no.2
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    • pp.269-287
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    • 2023
  • The WGJ420 fire-resistant weathering (FRW) steel is developed and manufactured with standard yield strength of 420 MPa at room temperature, which is expected to significantly enhance the performance of steel structures with excellent fire and corrosion resistances, strong seismic capacity, high strength and ductility, good resilience and robustness. In this paper, the mechanical properties of FRW steel plates and buckling behavior of columns are investigated through tests at elevated temperatures. The stress-strain curves, mechanical properties of FRW steel such as modulus of elasticity, proof strength, tensile strength, as well as corresponding reduction factors are obtained and discussed. The recommended constitutive model based on the Ramberg-Osgood relationship, as well as the relevant formulas for mechanical properties are proposed, which provide fundamental mechanical parameters and references. A total of 12 FRW steel welded I-section columns with different slenderness ratios and buckling load ratios are tested under standard fire to understand the global buckling behavior in-depth. The influences of boundary conditions on the buckling failure modes as well as the critical temperatures are also investigated. In addition, the temperature distributions at different sections/locations of the columns are obtained. It is found that the buckling deformation curve can be divided into four stages: initial expansion stage, stable stage, compression stage and failure stage. The fire test results concluded that the residual buckling capacities of FRW steel columns are substantially higher than the conventional steel columns at elevated temperatures. Furthermore, the numerical results show good agreement with the fire test results in terms of the critical temperature and maximum axial elongation. Finally, the critical temperatures between the numerical results and various code/standard curves (GB 51249, Eurocode 3, AS 4100, BS 5950 and AISC) are compared and verified both in the buckling resistance domain and in the temperature domain. It is demonstrated that the FRW steel columns have sufficient safety redundancy for fire resistance when they are designed according to current codes or standards.

Behaviour and design of stainless steel shear connectors in composite beams

  • Yifan Zhou;Brian Uy;Jia Wang;Dongxu Li;Xinpei Liu
    • Steel and Composite Structures
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    • v.46 no.2
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    • pp.175-193
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    • 2023
  • Stainless steel-concrete composite beam has become an attractive structural form for offshore bridges and iconic high-rise buildings, owing to the superior corrosion resistance and excellent ductility of stainless steel material. In a composite beam, stainless steel shear connectors play an important role by establishing the interconnection between stainless steel beam and concrete slab. To enable the best use of high strength stainless steel shear connectors in composite beams, high strength concrete is recommended. To date, the application of stainless steel shear connectors in composite beams is still very limited due to the lack of research and proper design recommendations. In this paper, a total of seven pushout specimens were tested to investigate the load-slip behaviour of stainless steel shear connectors. A thorough discussion has been made on the differences between stainless steel bolted connectors and welded studs, in terms of the failure modes, load-slip behaviour and ultimate shear resistance. In parallel with the experimental programme, a finite element model was developed in ABAQUS to simulate the behaviour of stainless steel shear connectors, with which the effects of shear connector strength, concrete strength and embedded connector height to diameter ratio (h/d) were evaluated. The obtained experimental and numerical results were analysed and compared with existing codes of practice, including AS/NZS 2327, EN 1994-1-1 and ANSI/AISC 360-16. The comparison results indicated that the current codes need to be improved for the design of high strength stainless steel shear connectors. On this basis, modified design approaches were proposed to predict the shear capacity of stainless steel bolted connectors and welded studs in the composite beams.

Development of Heterogeneous Damage Cause Estimation Technology for Bridge Decks using Random Forest (랜덤포레스트를 활용한 교량 바닥판의 이종손상 원인 추정 기술 개발)

  • Jung, Hyun-Jin;Park, Ki Tae;Kim, Jae Hwan;Kwon, Tae Ho;Lee, Jong-Han
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.44 no.1
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    • pp.19-32
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    • 2024
  • An investigation into the detailed safety diagnosis report indicates that domestic highway bridges mainly suffer from defects, deterioration, and damage due to physical forces. In particular, deterioration is an inevitable damage that occurs due to various environmental and external factors over time. In particular, bridge deck is very vulnerable to cracks, which occur along with various types of damages such as rebar corrosion and surface delamination. Thus, this study evaluates a correlation between heterogeneous damage and deterioration environment and then identifies the main causes of such heterogeneous damage. After all, a bridge heterogeneous damage prediction model was developed using random forests to determine the top five factors contributing to the occurrence of the heterogeneous damage. The results of the study would serve as a basic data for estimating bridge maintenance and budget.

Analysis of Activation Energy of Thermal Aging Embrittlement in Cast Austenite Stainless Steels (주조 오스테나이트 스테인리스강의 열취화 활성화에너지 분석)

  • Gyeong-Geun Lee;Suk-Min Hong;Ji-Su Kim;Dong-Hyun Ahn;Jong-Min Kim
    • Transactions of the Korean Society of Pressure Vessels and Piping
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    • v.20 no.1
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    • pp.56-65
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    • 2024
  • Cast austenitic stainless steels (CASS) and austenitic stainless steel weldments with a ferrite-austenite duplex structure are widely used in nuclear power plants, incorporating ferrite phase to enhance strength, stress relief, and corrosion resistance. Thermal aging at 290-325℃ can induce embrittlement, primarily due to spinodal decomposition and G-phase precipitation in the ferrite phase. This study evaluates the effects of thermal aging by collecting and analyzing various mechanical properties, such as Charpy impact energy, ferrite microhardness, and tensile strength, from various literature sources. Different model expressions, including hyperbolic tangent and phase transformation equations, are applied to calculate activation energy (Q) of room-temperature impact energies, and the results are compared. Additionally, predictive models for Q based on material composition are evaluated, and the potential of machine learning techniques for improving prediction accuracy is explored. The study also examines the use of ferrite microhardness and tensile strength in calculating Q and assessing thermal embrittlement. The findings provide insights for developing advanced prediction models for the thermal embrittlement behavior of CASS and the weldments of austenitic steels, contributing to the safety and reliability of nuclear power plant components.