• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.245-257
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• 2022

As the main lateral load resisting system in high-rise reinforced concrete structures, the mechanical performance of shear wall has a significant impact on the structure, especially for high-rise buildings. Steel corrosion has been recognized as an important factor affecting the mechanical performance and durability of the reinforced concrete structures. To investigate the effect on the seismic behaviour of corroded reinforced concrete shear wall induced by corrosion, analytical investigations and simulations were done to observe the effect of corrosion on the ultimate seismic capacity and drift capacity of shear walls. To ensure the accuracy of the simulation software, several validations were made using both non-corroded and corroded reinforced concrete shear walls based on some test results in previous literature. Thereafter, a parametric study, including 200 FE models, was done to study the influence of some critical parameters on corroded structural shear walls with boundary element. These parameters include corrosion levels, axial force ratio, aspect ratio, and concrete compressive strength. The results obtained would then be used to propose equations to predict the seismic resistance and drift capacity of shear walls with various corrosion levels.

• Journal of the Korea Concrete Institute
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• v.22 no.1
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• pp.19-27
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• 2010

Aging and severe environments are major causes of damage in reinforced concrete (RC) structures such as buildings and bridges. Deterioration such as concrete cracks, corrosion of steel, and deformation of structural members can significantly degrade the structural performance and safety. Therefore, effective and easy-to-use methods are desired for repairing and strengthening such concrete structures. Various methods for strengthening and rehabilitation of RC structures have been developed in the past several decades. Recently, FRP composite materials have emerged as a cost-effective alternative to the conventional materials for repairing, strengthening, and retrofitting deteriorating/deficient concrete structures, by externally bonding FRP laminates to concrete structural members. The main purpose of this study is to investigate the effectiveness of adaptive neuro-fuzzy inference system (ANFIS) in predicting behavior of circular type concrete column retrofitted with FRP. To construct training and testing dataset, experiment results for the specimens which have different retrofit profile are used. Retrofit ratio, strength of existing concrete, thickness, number of layer, stiffness, ultimate strength of fiber and size of specimens are selected as input parameters to predict strength, strain, and stiffness of post-yielding modulus. These proposed ANFIS models show reliable increased accuracy in predicting constitutive properties of concrete retrofitted by FRP, compared to the constitutive models suggested by other researchers.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.1
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• pp.21-28
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• 2008

The CFT (Concrete Filled Steel Tube) columns became popular in high rise building construction due to not only its composite effect but also economic advantage. However, it has been pointed out in various previous researches that the current practice in CFT columns may lead the steel tube to probable local buckling at critical sections of the columns right after yielding. To resolve such a problem, the TR-CFT (Transversely Reinforced Concrete Filled Steel Tube) column is proposed to control or at least delay the local buckling state at the critical section by wrapping the CFT columns with carbon fiber sheet. The validity of the proposed column system is validated through the present paper by observing the experimental performance and comparing it with the analytical prediction of the TR-CFT columns with hish strength concrete. It is also shown that the current design code provisions such as ACI-318, in which the contribution of concrete confining effect filled in steel tube is not appropriately accounted for, may contain too much conservatism.

• Steel and Composite Structures
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• v.48 no.5
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• pp.531-545
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• 2023

Composite dowels are implemented as a powerful alternative to headed studs for the efficient combination of Ultra High-Performance Concrete (UHPC) with high-strength steel in novel composite structures. They are required to provide sufficient shear resistance and ensure the transmission of tensile forces in the composite connection in order to prevent lifting of the concrete slab. In this paper, the load bearing capacity of puzzle-shaped and clothoidal-shaped dowels encased in UHPC specimen were investigated based on validated experimental test data. Considering the influence of the embedment depth and the spacing width of shear dowels, the characteristics of UHPC square plate on the load bearing capacity of composite structure, 240 numeric models have been constructed and analyzed. Three artificial intelligence approaches have been implemented to learn the discipline from collected experimental data and then make prediction, which includes Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Extreme Learning Machine (ELM). Among the factors, the embedment depth of composite dowel is proved to be the most influential parameter on the load bearing capacity. Furthermore, the results of the prediction models reveal that ELM is capable to achieve more accurate prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.133-141
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• 2017

Traffic volume increases according to highway expansion and industrial development which causes repetitive defect and durability degradation on pavement. The research of quality assurance system used abroad has introduced Korea. Korea Expressway Corporation (KEC) has developed a Quality Performance Index (QPI) that quantitatively assesses the level of quality of the final product, and practical applications. Assessment factor on concrete pavement consisted of pavement thickness, compressive strength, IRI and spacing factor. Assessment factor on concrete pavement is determined by empirical evaluation factor from abroad. In this study, analysis of evaluation factors of concrete pavement by using pavement life prediction simulation and measured data were evaluated with consideration of feasibility of the assessment factor. Pavement life, performance and durability are affected by pavement thickness, compressive strength, IRI and spacing factor in assessment factor on concrete pavement, QPI.

• International journal of steel structures
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• v.18 no.4
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• pp.1107-1124
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• 2018

The structural behavior of reinforced concrete coupled shear wall structures is greatly influenced by the behavior of their coupling beams. This paper presents a process of the seismic analysis of reinforced concrete coupled shear wall-frame system linked by hysteretic dampers at each floor. The hysteretic dampers are located at the middle portion of the linked beams which most of the inelastic damage would be concentrated. This study concerned particularly with wall-frame structures that do not twist. The proposed method, which is based on the energy equilibrium method, offers an important design method by the result of increasing energy dissipation capacity and reducing damage to the wall's base. The optimum distribution of yield shear force coefficients is to evenly distribute the damage at dampers over the structural height based on the cumulative plastic deformation ratio of the dissipation device. Nonlinear dynamic analysis indicates that, with a proper set of damping parameters, the wall's dynamic responses can be well controlled. Finally, based on the total plastic strain energy and its trend through the height of the buildings, a prediction equation is suggested.

• Computers and Concrete
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• v.12 no.3
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• pp.243-259
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• 2013

Recent developments in Artificial Intelligence (AI) and computational intelligence have made it viable in the construction industry and structural analysis. This study usesthe Adaptive Network-based Fuzzy Inference System (ANFIS) as a modelling tool to predict the strain in tie section for High Strength Self Compacting Concrete (HSSCC) deep beams. 3773 experimental data were collected. The input data andits corresponding strains in tie section as output data were recorded at all loading stages. Results from ANFIS are compared with the classical linear regression (LR). The comparison shows that the ANFIS's results are highly accurate, precise and satisfactory.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.329-332
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• 2006

To study the effect of the macroscopic TVLEM(Three Vertical Line Element Model) which is developed in 2D, a bearing wall system is selected and 2D and 3D pushover analyses are carried out. In 2D model, the participating width of a flage wall to lateral resistance is modelled based on Paulay's effective width. From the comparisons of roof displacements, 2D model which uses the effective width of flange wall has better prediction and less analysis time than 3D model which has intrinsically the full width of the flange that causes higher stiffness and strength and shorter deformation capacity than 2D model.

• Smart Structures and Systems
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• v.25 no.6
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• pp.751-764
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• 2020

Real-time monitoring of stiffness and strength in cement based system has received significant attention in past few decades owing to the development of advanced techniques. Also, use of environment friendly supplementary cementitious materials (SCM) in cement, though gaining huge interest, severely affect the strength gain especially in early ages. Continuous monitoring of strength- and stiffness- gain using an efficient technique will systematically facilitate to choose the suitable time of removal of formwork for structures made with SCM incorporated concrete. This paper presents a technique for monitoring the strength and stiffness evolution in hydrating fly ash blended cement systems using electro-mechanical impedance (EMI) based technique. It is important to observe that the slower pozzolanic reactivity of fly ash blended cement systems could be effectively tracked using the evolution of equivalent local stiffness of the hydrating medium. Strength prediction models are proposed for estimating the strength and stiffness of the fly ash cement system, where curing age (in terms of hours/days) and the percentage replacement of cement by fly ash are the parameters. Evaluation of strength as obtained from EMI characteristics is validated with the results from destructive compression test and also compared with the same obtained from commonly used ultrasonic wave velocity (UPV). Statistical error indices indicate that the EMI technique is capable of predicting the strength of fly ash blended cement system more accurate than that from UPV. Further, the correlations between stiffness- and strength- gain over the time of hydration are also established. From the study, it is found that EMI based method can be effectively used for monitoring of strength gain in the fly ash incorporated cement system during hardening.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.9-18
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• 2023

Predicting the compressive strength of high-performance concrete (HPC) is challenging because of the use of additional cementitious materials; thus, the development of improved predictive models is essential. The purpose of this study was to develop an HPC compressive-strength prediction model using an ensemble machine-learning method of combined bagging and stacking techniques. The result is a new ensemble technique that integrates the existing ensemble methods of bagging and stacking to solve the problems of a single machine-learning model and improve the prediction performance of the model. The nonlinear regression, support vector machine, artificial neural network, and Gaussian process regression approaches were used as single machine-learning methods and bagging and stacking techniques as ensemble machine-learning methods. As a result, the model of the proposed method showed improved accuracy results compared with single machine-learning models, an individual bagging technique model, and a stacking technique model. This was confirmed through a comparison of four representative performance indicators, verifying the effectiveness of the method.