Abstract
In this work, we have developed a novel machine (deep) learning computational framework to determine and identify damage loading parameters (conditions) for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure. We have shown that the developed machine learning algorithm can accurately and (practically) uniquely identify both prior static as well as impact loading conditions in an inverse manner, based on the residual plastic strain and plastic deformation as forensic signatures. The paper presents the detailed machine learning algorithm, data acquisition and learning processes, and validation/verification examples. This development may have significant impact on forensic material analysis and structure failure analysis, and it provides a powerful tool for material and structure forensic diagnosis, determination, and identification of damage loading conditions in accidental failure events, such as car crashes and infrastructure or building structure collapses.
Highlights
Most engineering materials, products and structures are designed, manufactured or constructed with an intention to function properly
8 0 2.769 2.361 1.505 In Section 4.1 we demonstrated the capability of the deep neural network (DNN) model to predict the statics loads acting on the training nodes
The prediction on the location of the applied load is not necessarily from the training data, after studying the training data, the machine learning algorithm can automatically use interpolation to find the applied load location that is not in the train data. This is to say that we only need to train the neural network with a limited number of loading sets, it can predict any loading locations on the boundary of the cantilever beam
Summary
Products and structures are designed, manufactured or constructed with an intention to function properly. They can fail, get damaged or may not operate or function as intended due to various reasons including material or design flaws, extreme loading, etc. It is important to identify the reasons of these failures or damage situations to improve the designs and detect any flaws in the materials or designs. Engineering products and structures are designed with specific intent and function They may fail due to reasons including material shortcomings, construction flaws, extreme loading, or other conditions and behaviors exceeding their design parameters. To improve design and prevent failures, it is important to analyze actual failures, and identify the CMES. To improve design and prevent failures, it is important to analyze actual failures, and identify the CMES. doi:10.31614/cmes.2018.04697 www.techscience.com/cmes
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