Analytical Investigation of Hybrid Engineering Architectures Using Machine-Assisted Decision Models
Keywords:
Hybrid engineering, machine-assisted decision models, engineering architecture, decision support systems, artificial intelligence, structural optimizationAbstract
Hybrid engineering architectures, which integrate human-centric design strategies with machine-driven decision systems, are transforming modern engineering workflows. This paper presents an analytical investigation into the design, performance, and decision-making capabilities of hybrid systems augmented by machine-assisted models. The study explores the interplay between traditional engineering heuristics and algorithmic optimization models, focusing on improving structural reliability, process adaptability, and intelligent automation. Key machine-assisted approaches analyzed include rule-based engines, supervised learning classifiers, and probabilistic inference networks. Experimental design simulations and performance evaluations are used to assess accuracy, responsiveness, and interpretability.
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Copyright (c) 2022 Gayana Herambe, Stanley Jonathan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
