Use of Artificial Neural Network in Engineering: A Review

Abstract

In the context of Artificial Neural Networks (ANNs), the use of an AI driven machine learning approach has been explored for addressing real world problems in the current scenario. For the effective functioning of ANN based models, similar datasets specific to the targeted problem are required, so that behavior resembling human general intelligence can be emulated. A comprehensive review of earlier studies related to ANNs has been presented in this paper, and conclusions have been drawn based on the evaluated literature. Within these reviewed works, significant contributions have been made using architectures such as Multi Layer Perceptron Neural Networks (MLPNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), each applied respectively to one dimensional, two dimensional and time series data for pattern recognition and learning. To ensure proper learning and adaptive behavior in ANN systems, the initial selection of an AI capable programming language has been emphasized, which must be able to process datasets intelligently before translating this behavior into artificial intelligence. As a result of these findings, clear and focused conclusions were drawn which forms the research objectives of our further technical study.

Introduction

Artificial Neural Networks (ANNs) are computational models inspired by the structure and functioning of the human brain. Built with interconnected nodes (neurons) across multiple layers (input, hidden, output), ANNs learn from data by adjusting weighted connections through experience-based training. They are particularly effective in modeling complex, nonlinear relationships and are widely used in machine learning tasks such as classification, prediction, and decision-making.

Key Applications and Research Studies:

1. Algorithm Optimization & Code Efficiency

• AlphaDev (Daniel J. Mankowitz, 2023): Used reinforcement learning to discover faster sorting algorithms that outperformed human-developed routines and were integrated into standard C++ libraries.

• Xiaoming Li (2007): Proposed machine learning and genetic algorithm-based adaptive sorting strategies, achieving performance boosts of up to 62%.

• Raimondas Sasnauskas (n.d.): Developed the Souper superoptimizer, streamlining compiler performance and reducing binary size by 4.4%.

2. Civil Engineering

• Xu Yang (2021): Reviewed 683 papers on ANN in pavement engineering; CNNs were most effective in inspection/monitoring tasks.

• Frank Jesús Valderrama Purizaca (2020): Systematic review revealed ANN effectiveness in modeling soil behavior, seismic performance, and concrete properties (with 0.99 R²).

• Asraar Anjum (2024): Discussed integration of AI/ANNs in structural optimization, noting issues like computational demand and data privacy.

• Alexandrina Elena Andon (2022): Highlighted CNN's superiority over traditional ANNs in civil image processing tasks.

• Alexandrina Elena C. Pandelea (2014): Detailed ANN use in geotechnical analysis, structural systems, and heat transfer modeling.

3. Agriculture and Food Processing

• Pallavi U. Patil (2021): Applied CNN, ANN, and SVM for dragon fruit sorting and maturity grading using Raspberry Pi.

• Arshad Shaikh (2024): Used CNNs for raisin quality classification based on size, color, and texture, improving sorting efficiency.

4. Robotics & Automation

• Hongyan Zhang (2021): Developed a robotic multi-object sorting system using Mask R-CNN and 3D object segmentation for unstructured environments.

5. Climate & Sustainability

• Crop diversification and ANN sorting (Pallavi U. Patil, 2021): Focused on sustainable agriculture using AI for grading on degraded lands.

6. AI in Sorting & Signal Processing

• Somshubra Majumdar (2016): Introduced adaptive algorithms for sorting large-scale data using machine learning and algebraic systems.

• Dominik Fuchsgruber (2024): Developed EIGN, a Graph Neural Network for modeling edge-level signals (directed/undirected) in traffic and utility systems, improving RMSE by 43.5%.

7. AI in Code Generation & Security

• Mark Chen (2021): Analyzed Codex (used in GitHub Copilot) for code generation; achieved up to 70.2% accuracy with repeated sampling.

• Hammond Pearce (2022): Studied large language models (LLMs) for automated bug repair, noting success with synthetic cases but limits in real-world vulnerability fixes.

8. Neural Network Architecture Enhancements

• Noam Shazeer (2019): Improved Transformer model efficiency with "multi-query attention" for faster decoding and lower memory use.

Conclusion

A gap in the literature regarding prior work on the current topic has been identified. The necessity for further research to address realworld problems has been emphasized. From the literature review, conclusions have been drawn that underscore the primary findings and enumerate essential outcomes: 1) The feasibility of programming languages for artificial intelligence combined with machine learning has been evaluated, with examples including Python and VBA. 2) Appropriate environments capable of executing programming language code with simulation have been determined, such as Python launcher and Google Colaboratory. 3) Programming code has been developed by studying architecture and hierarchy, followed by the implementation of learning processes inspired by human behavior, then translated into machine code. 4) Code optimization and debugging have been performed wherever possible to ensure smooth execution. 5) It has concluded that motivation plays a crucial role in the design phase of machine learning and AIbased programs. These findings represent the conclusive outcomes of the study. The feasibility of using artificial intelligence to explore AIbased machine learning programming for automatic simulation of problem solving has been set as the primary objective of this study. The aim is to obtain solutions with simplicity, which will serve as the central focus for the forthcoming proposed work.

References

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Copyright

Copyright © 2025 Tarun Athya, Dr. Raghvendra Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.