Seismic damage prediction of RC buildings using machine learning
نویسندگان
چکیده
Decision-makers and stakeholders require a rapid assessment of potential damage after earthquake events in order to develop implement disaster risk reduction strategies respond systematically post-disaster situations. The investigated manually an are complicated, labor-intensive, time-consuming, error prone process. development fragility curves is time consuming unable predict the for wide classes structures since it considers few structural properties only one seismic characteristic. Furthermore, nonlinear finite element method cannot be utilized numerous buildings because involves more money. This paper presents machine learning (ML)-based prediction RC buildings. It found that some research works considered parameters or train ML models assessment. However, these may not fully reveal underlying complexity relationship between input building performance. As result, their applicability will limited. evaluates feasibility using techniques such as K-nearest neighbor, random forest, decision tree, support vector machine, artificial neural network rapidly earthquake-induced reinforced concrete considering both ground motion characteristics. trained simulation results. Due lack real datasets limited access, most used Scikit Learn train_test_split function randomly split entire into training testing performance proposed technique evaluated datasets. this study, performances different collected 2015 Nepal earthquake. overall accuracy on suggests capability algorithms successfully predicting quick with reasonable accuracy. study beneficial emergency response recovery planning
منابع مشابه
Probabilstic Seismic Risk Evaluation of Rc Buildings
As more and more emphasis is being laid on non-linear analysis of RC framed structures subjected to earthquake excitation, the research and development on both non-linear static (pushover) analysis as well as nonlinear dynamic (time history)analysis is in the forefront. Due to prohibitive computational time and efforts required to perform a complete nonlinear dynamic analysis, researchers and d...
متن کاملProbabilistic Seismic Damage Assessment of RC Buildings Based on Nonlinear Dynamic Analysis
The incremental dynamic analysis is a powerful tool for evaluating the seismic vulnerability and risk of buildings. It allows calculating the global damage of structures for different PGAs and representing this result by means of damage curves. Such curves are used by many methods to obtain seismic risk scenarios at urban level. Even if the use of this method in a probabilistic environment requ...
متن کاملSeismic Vulnerability Assessment of Existing Rc Buildings in Gis Environment
With rapid population growth over the past 30 years and no significance seismic activity during this period, the unchecked development of the built environment in Bangladesh has been resulted in little resilience to earthquake. Identifying buildings due to this unchecked development that has high vulnerability is of critical importance both for reliable loss estimation as a result of an expecte...
متن کاملSeismic Damage Assessment of RC Buildings Subjected to the Rotational Ground Motion Records Considering Soil-Structure Interaction
The significance of the seismic rotational components have been overlooked in the seismic evaluation of structural behavior. As researchers have measured seismic components more accurately using sensitive rotational velocity sensor, it was observed that the magnitude of rotational components is considerable and could not be neglected. Hence, some parts of seismic damage or failure of structures...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Earthquake Engineering & Structural Dynamics
سال: 2023
ISSN: ['0098-8847', '1096-9845']
DOI: https://doi.org/10.1002/eqe.3907