BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Using neural networks to predict workability of concrete incorporating metakaolin and fly ash
Advances in Engineering Software - Civil-comp 2001
Knowledge discovery of concrete material using Genetic Operation Trees
Expert Systems with Applications: An International Journal
Generalized linear model-based expert system for estimating the cost of transportation projects
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
Advances in Engineering Software
Using support vector machine with a hybrid feature selection method to the stock trend prediction
Expert Systems with Applications: An International Journal
Comparison of neural networks and regression analysis: A new insight
Expert Systems with Applications: An International Journal
Nonlinear civil structures identification using a polynomial artificial neural network
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Genetic programming for predicting aseismic abilities of school buildings
Engineering Applications of Artificial Intelligence
Smart meter monitoring and data mining techniques for predicting refrigeration system performance
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The aseismic ability of buildings is generally analyzed using a nonlinear model. Numerical models are constructed based on the structural configuration and material property of buildings by simulating their stress responses and behaviors to obtain their aseismic ability. This method is complex and time-consuming and should be conducted by professionals. Hence, the aseismic ability of buildings cannot be determined rapidly on a large scale. Additionally, rapidly sequencing and screening the aseismic ability of a large number school buildings to make maintenance and management decisions is extremely difficult. This work adopts predictive data-mining models to determine the relationship between basic design parameters of school buildings and their aseismic ability, and then proposes a best model for predicting the aseismic ability of school buildings. Only basic geometric information of school buildings is needed to estimate quickly their aseismic ability. This prediction model must be able to handle the heavy load of evaluating the aseismic ability of school buildings. The proposed model will help maintenance managers conduct detailed assessments and sequencing of reinforcement work through nonlinear analysis. The proposed model can serve as a reference for disaster prevention in disaster plans and staff rescue during rescue work.