Application of Artificial Intelligence (AI) in Civil Engineering

TL;DR

This paper reviews how artificial intelligence and soft computing methods like neural networks, fuzzy logic, genetic algorithms, and probabilistic reasoning are transforming civil engineering by improving analysis and decision-making in various sub-fields.

Contribution

It provides an overview of AI techniques applied to civil engineering, highlighting their roles and benefits in solving complex, real-world problems.

Findings

AI models improve accuracy in slope stability analysis

Fuzzy logic enhances decision-making in civil systems

Genetic algorithms optimize engineering models

Abstract

Hard computing generally deals with precise data, which provides ideal solutions to problems. However, in the civil engineering field, amongst other disciplines, that is not always the case as real-world systems are continuously changing. Here lies the need to explore soft computing methods and artificial intelligence to solve civil engineering shortcomings. The integration of advanced computational models, including Artificial Neural Networks (ANNs), Fuzzy Logic, Genetic Algorithms (GAs), and Probabilistic Reasoning, has revolutionized the domain of civil engineering. These models have significantly advanced diverse sub-fields by offering innovative solutions and improved analysis capabilities. Sub-fields such as: slope stability analysis, bearing capacity, water quality and treatment, transportation systems, air quality, structural materials, etc. ANNs predict non-linearities and provide accurate estimates. Fuzzy logic uses an efficient decision-making process to provide a more precise assessment of systems. Lastly, while GAs optimizes models (based on evolutionary processes) for better outcomes, probabilistic reasoning lowers their statistical uncertainties.