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Existing reinforced concrete building structures have low lateral resistance capacities due to seismically deficient details. Since these building structures suffer an increase in axial loads to the main structural elements due to green retrofits (e.g., installation of energy equipment/devices, roof gardens) as one of the sustainable building solutions and/or vertical extensions, their capacities can be reduced. This paper aims to propose a rapid estimation method incorporating a previously developed machine-learning model to find an allowable range of axial loads for reinforced concrete columns using simple structural details for enhancement in the sustainability performance of existing buildings in structural and energy fields. The methodology consists of two steps: (1) a machine-learning-based failure detection model, and (2) column damage limits proposed by previous researchers. To demonstrate this proposed method, an existing building structure built in the 1990s was selected, and the allowable range for the target structure was computed for both exterior and interior columns. A machine-learning-based method showed that axial loading could be increased by a factor of 1.35. Additionally, nonlinear time-history analysis for the target structure was performed to compare the seismic responses before and after applying the maximum allowable axial load. Based on the dynamic responses, the increased axial loads from green retrofits and/or vertical extensions could degrade structural performance and change its failure mode. The proposed methodology can rapidly estimate the allowable axial load range for existing reinforced concrete buildings without repeated modeling and computing processes. In addition, nonlinear time-history analysis is needed to accurately evaluate the impact of the increased axial loads from green retrofits/vertical extensions on structural performance. [ABSTRACT FROM AUTHOR]

One of the most challenging elements of modeling the behaviour of reinforced concrete (RC) walls is combining realistic material models that can capture the observable behaviour of the physical system. Experiments with realistic loading rates and pressures reveal that steel and concrete display complicated nonlinear behaviour that is challenging to represent in a single constitutive model. To investigate the response of a reinforced concrete structure subjected to dynamic loads, this paper's study is based on many different material models to assess the advantages and disadvantages of the models on 2D and 3D RC walls using the LS-DYNA program. The models consisted of the KCC model and the CDP model, which represented plasticity and distinct tensile/compressive damage models, and the Winfrith model, which represented plasticity and the smeared crack model. Subsequently, the models' performances were assessed by comparing them to experimental data from reinforced concrete structures, in order to validate the accuracy of the overall behaviour prediction. The Winfrith model demonstrated satisfactory results in predicting the behaviour of 2D and 3D walls, including maximum strength, stiffness deterioration, and energy dissipation. The method accurately predicted the maximum strength of the Winfrith concrete model for the 2D wall with an error of 9.24% and for the 3D wall with errors of 3.28% in the X direction and 5.02% in the Y direction. The Winfrith model demonstrated higher precision in predicting dissipation energy for the 3D wall in both the X and Y directions, with errors of 6.84% and 6.62%, correspondingly. Additional parametric analyses were carried out to investigate structural behaviour, taking into account variables such as concrete strength, strain rate, mesh size, and the influence of the element type. [ABSTRACT FROM AUTHOR]

To reduce the thickness of reinforced concrete foundation members used in construction and structural applications, a previous study developed and tested a strut–tie retrofit system installed in the foundations. This study proposes the optimum retrofit details of a steel-tie retrofit system for foundation members with reduced thickness via a finite element simulation-based load-bearing capacity assessment. The retrofit parameters (structural steel type, plate thickness, and number of strut frames) that significantly affected the load-bearing capacities were optimized by comparing the maximum effective stress and code-defined allowable stress limits. The optimum retrofit details were compared with those computed using a code-defined strut–tie model. Based on the load-bearing capacity assessment for the design of loading combinations, the optimum retrofit details can be reduced in transverse (by 55%) and longitudinal (by 87%) directions compared with those designed using the strut–tie model approach. [ABSTRACT FROM AUTHOR]

This research proposes a surrogate model to predict the seismic response of individual structural elements in structures whose inherent vertical and horizontal irregularities result in components with different seismic vulnerabilities. A frequency-based data-driven model was developed which predominantly uses the frequency spectrum of earthquakes as input data. The seismic responses of several structural components can be simultaneously generated as output using the proposed model. A comparison of structure fragility assessments obtained with a conventional approach, and the proposed Deep Learning-based approach, was conducted to verify the accuracy of the proposed method's prediction capability. [ABSTRACT FROM AUTHOR]

A hybrid concrete wall is made up of a traditional reinforced concrete wall with post-tensioned technology. Recent research has shown that post-tensioned (PT) reinforced concrete shear walls have a wide range of advantages when it comes to resisting lateral forces and lateral moments caused by earthquake loading. To explore the PT reinforced concrete wall behavior subjected to seismic load, the concrete models were augmented with various material models, including the KCC, CDP, and Winfrith models for a PT 2D wall under pushover analysis. To ensure that the overall behavior forecast was qualitatively acceptable, the models' performance was compared to experimental findings. Then, the post-tensioned modeling approach was implemented with the 3D wall in order to predict structural responses of the PT 3D wall. The well-validated finite element models were ultilized to estimate the effects of the post-tensioned bars on lateral resisting capacities of the precast concrete shear wall. [ABSTRACT FROM AUTHOR]