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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]

The seismically deficient column details in existing reinforced concrete buildings affect the overall behavior of the building depending on the failure type of the column. The purpose of this study is to develop and validate a machine-learning-based prediction model for the column failure modes (shear, flexure–shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using the concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating the accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model has the highest average value for the classification model performance measurements among the considered learning methods and can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with the simple column details. Additionally, it was demonstrated that the predicted failure modes from the selected model were exactly same as the failure mode determined from a code-defined equation (traditional method). [ABSTRACT FROM AUTHOR]

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage grade prediction by fire based on simplified building information. This paper's primary objective is to propose and verify a framework for predicting the scale of property damage caused by fire using machine learning (ML). Korean public datasets are collected and preprocessed, and ML algorithms are trained with only 15 input data using building register and fire scenario information. Four models (artificial neural network (ANN), decision tree (DT), k-nearest neighbor (KNN), and random forest (RF)) are used for ML. The RF model is the most suitable for this study, with recall and precision of 74.2% and 73.8%, respectively. Structure, floor, causes, and total floor area are the critical factors that govern the fire size. This study proposes a novel approach by utilizing ML models to accurately and rapidly predict the size of fire damage based on basic building information. By analyzing domestic fire incident data and creating fire scenarios, a similar ML model can be developed. [ABSTRACT FROM AUTHOR]

Blast loads have significantly damaged non-ductile reinforced concrete building structures with seismically deficient column details, which have lower shear capacity than the building structures with the current code-required details. This paper aims to evaluate the blast performance of non-ductile (shear-governed) and ductile (flexure-governed) RC columns using ductility- and residual capacity-based performance limits. To accomplish this research goal, the shear- and flexure-governed RC column models were developed using a finite element method and validated with previous experimental results. The efficient blast modeling method representing the reflected and incident waves was implemented in the validated column models, and these were simulated under various blast loading scenarios. The blast-induced responses for each column were investigated and utilized to determine the performance levels using the two different performance limits. The flexure-governed column model has higher performance levels than the shear-governed column model because of the lower ductility capacity of the shear-governed column model. The residual capacity-based performance level of the shear-governed column was underestimated compared to the ductility-based limits specified in a current design code. [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]

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]

Conventional seismic performance evaluation methods for building structures with soil–structure interaction effects are inefficient for regional seismic damage assessment as a predisaster management system. Therefore, this study presented the framework to develop an artificial neural network-based model, which can rapidly predict seismic responses with soil–structure interaction effects and determine the seismic performance levels. To train, validate and test the model, 11 input parameters were selected as main parameters, and the seismic responses with the soil–structure interaction were generated using a multistep analysis process proposed in this study. The artificial neural network model generated reliable seismic responses with the soil–structure interaction effects, and it rapidly extended the seismic response database using a simple structure and soil information. This data generation method with high accuracy and speed can be utilized as a regional seismic assessment tool for safe and sustainable structures against natural disasters. [ABSTRACT FROM AUTHOR]

This study evaluated the influence of additional shear walls, constructed on the first floor, as strengthening methods for a piloti-type building subjected to earthquake loadings. Piloti-type buildings are commonly designed as urban structures in many cities of South Korea. The existence of just columns on the first floor of the building is a feature that is advantageous from an architectural viewpoint, and yet has potential structural disadvantages. Such columns usually exhibit shear–axial failure, due to inherent vertical and horizontal irregularities and insufficient seismic reinforcements. Among several retrofitting methods, including additional braces, carbon fiber reinforced polymers, dampers, and so forth, this research considered reinforced concrete shear walls to improve the seismic responses of piloti buildings. A parametric analysis of the location of the retrofitted shear walls in a typical piloti building was implemented using the Zeus-NL program. Nonlinear time history analysis and incremental dynamic analysis were performed to comparatively evaluate the structure's seismic responses and fragility curves before and after retrofit. [ABSTRACT FROM AUTHOR]