Theses and Dissertations

Issuing Body

Mississippi State University


Peterson, Luke

Committee Member

To, Filip Sumunto D.

Committee Member

Lee, Nayeon

Committee Member

Pezzola, Genevieve

Committee Member

Elder, Steven H.

Date of Degree


Document Type

Graduate Thesis - Open Access


Biomedical Engineering

Degree Name

Master of Science (M.S.)


James Worth Bagley College of Engineering


Department of Agricultural and Biological Engineering


Artificial Neural Network (ANN) ensemble and Response Surface Method (RSM) surrogate models were generated from Finite Element (FE) simulations to predict the overpressure load and vehicle impact response of a novel rapidly deployable protective structure. A Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used in conjunction with the surrogate models to determine structure topology input variable configurations which were suited to produce the optimal balance of minimum mass, minimum rotation angle, minimum displacement, and maximum total length of the deployable structure. The structure was designed to retract into a container, be lightweight to facilitate transportation, and be able to adapt to varying terrain slopes. This research demonstrates that, in comparison to the RSM, ANN ensembles can more accurately and efficiently be used for identifying optimal design solutions for multi-objective design problems when two surrogate models from the same method corresponding to separate FE models are used simultaneously in a NSGA-II.

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