Structural Reliability Analysis Using Genetic Algorithm and Gaussian Process Regression
نویسندگان
چکیده
منابع مشابه
STRUCTURAL SYSTEM RELIABILITY-BASED OPTIMIZATION OF TRUSS STRUCTURES USING GENETIC ALGORITHM
Structural reliability theory allows structural engineers to take the random nature of structural parameters into account in the analysis and design of structures. The aim of this research is to develop a logical framework for system reliability analysis of truss structures and simultaneous size and geometry optimization of truss structures subjected to structural system reliability constraint....
متن کاملOptimisation of shock absorber process parameters using failure mode and effect analysis and genetic algorithm
The various process parameters affecting the quality characteristics of the shock absorber during the process were identified using the Ishikawa diagram and by failure mode and effect analysis. The identified process parameters are welding process parameters (squeeze, heat control, wheel speed, and air pressure), damper sealing process parameters (load, hydraulic pressure, air pressure, and ...
متن کاملGaussian Process Regression Plus Method for Localization Reliability Improvement
Location data are among the most widely used context data in context-aware and ubiquitous computing applications. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning. The most useful method is focused on the received signal strength and provides a set of signal transmission access points. However, compiling a man...
متن کاملGaussian Process Regression for Trajectory Analysis
Cognitive scientists have begun collecting the trajectories of hand movements as participants make decisions in experiments. These response trajectories offer a fine-grained glimpse into ongoing cognitive processes. For example, difficult decisions show more hesitation and deflection from the optimal path than easy decisions. However, many summary statistics used for trajectories throw away muc...
متن کاملGaussian Process Quantile Regression using Expectation Propagation
Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagat...
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: IOP Conference Series: Earth and Environmental Science
سال: 2021
ISSN: 1755-1307,1755-1315
DOI: 10.1088/1755-1315/783/1/012066