Can Civil Engineers Use AI for Construction Projects?

Civil engineers can use AI models to make construction projects more accurate, cheaper, and less disruptive. AI models can be used for precise routing of electrical & plumbing systems & more.

Can Civil Engineers Use AI for Construction Projects?

Civil engineers can use AI models to make construction projects more accurate, cheaper, and less disruptive. AI models can be used to produce building plans, floor plans, and more by incorporating artificial intelligence into the BIM process. Engineers can also make the necessary changes to all design areas with pinpoint accuracy. AI models can also be used to plan the routing of electrical and plumbing systems in modern structures.

Instead of having engineers do all the designs on their computers, they can simply enter the specifications into their computer and the machine learning system will handle the rest of the design process. This process could go even faster with machine learning. AI is a science about research and law enforcement of human intelligence activities. It has been a far-reaching cross-border issue, after 50 years of progress. Nowadays, this technology is applied in many fields, such as expert system, knowledge base system, intelligent database system and intelligent robot system.

The expert system is the earliest and most extensive area, the most active and most fruitful, which was named “the knowledge management and decision-making technology of the 21st century”.In civil engineering, many problems, especially in engineering design, construction management and program decision-making, are influenced by many uncertainties that can be solved not only with mathematical, physical and mechanical calculations but also depend on the experience of practitioners. This knowledge and experience is illogically incomplete and imprecise, and cannot be handled by traditional procedures. However, artificial intelligence has its own superiority. Complex problems can be solved at expert levels by imitating experts. AI has broad application perspectives in civil engineering practice.

The developed EFHNN combines neural networks (NN) and high-order neural networks (HONN) into a hybrid neural network (HNN), which acts as the main inference engine and works with alternating connections of linear and non-linear NN layers. Levinson's “Improving Transportation Education Through Online Simulation Using an Agent-based Demand and Allocation Model” in the Journal of Professional Issues in Engineering Education and Practice; Abourizk's “Methodology for Integrating Fuzzy Expert Systems and Discrete Event Simulation in Construction Engineering” in the Canadian Journal of Civil Engineering; Ramaswamy's “Optimal Fuzzy Logic Control for MDOF Structural Systems Using Evolutionary Algorithms” in Artificial Intelligence Engineering Applications; Etemad Shahidi's “A hybrid fuzzy inference system based on networks adaptive to genetic algorithms in the prediction of wave parameters” in Engineering Applications of Artificial Intelligence; Guzelbey's “A Gentle Computation-Based Approach to Predicting the Maximum Strength of Metal Plates in Compression” in Engineering Structures; Bajcsy's “Image-based Machine Learning to Reduce User Fatigue in an Interactive Model Calibration System” in Journal of Computing in Civil Engineering; Sapuan's “A Knowledge-based System for Material Selection in Mechanical Engineering Design” in Materials and Design; are some examples. Civil engineering companies are turning to machine learning and data science consulting to help with the construction and design of roads, bridges, and other infrastructure projects. Civil engineering students need to learn how to offer practical and sustainable solutions for engineering projects. The goal of the proposed KBE system was to integrate the hot forging design process into a single framework to capture the knowledge and experience of design engineers. Sometimes civil engineers also work with customers to decide how they should build something and how to incorporate what the customer needs and wants into their design.

Engineers can now improve design using data acquired from previous simulations, models, and projects thanks to the integration of AI-based design exploration. Unsupervised learning is most common when there is no specific objective or task for the machine to accomplish, and engineers just want to see what the machine will discover.