Abstract
Reliability prediction plays a significant role in risk assessment of engineering structures. Mathematically, the prediction task can be seen as a classification (regression) procedure. In this aspect, machine learning methods have recently shown their superior performance over others in various research domains. Random forest (RF) is distinguished for its robustness and high accuracy in modeling and prediction work. However, its application in the area of structural reliability has not been widely explored. This study aims to explore the feasibility of RF as well as examine its performance in modeling and prediction of structure reliability in passive control mode. A numerical example is introduced in the simulation part to evaluate performance of the proposed method in different perspectives.
Highlights
The reliability of an engineering structure varies during its lifetime
The artificial neural network (ANN) is known for its complex architecture optimization, low robustness and enormous training time [4]
The support vector machine (SVM) is time consuming for large-scale applications and sometimes shows large error in sensitivity calculations
Summary
The reliability of an engineering structure varies during its lifetime. Traditional reliability models are generally based on specific a priori assumptions about lifetime distributions, which are no longer realistic for today’s complex structures [1]. For this problem, growing attention has been paid to statistical learning approaches. The prediction task can be seen as a classification/ regression procedure. The ANN is known for its complex architecture optimization, low robustness and enormous training time [4]. This study focus on the feasibility of RF as well as its performance in reliability modeling and prediction of structures in passive control mode
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