Artificial intelligence (AI) is a technology associated with the simulation of human intelligence in machines to enable learning and problem solving. AI makes the decision-making process simple, fast, and efficient. It incorporates a whole set of operations in civil engineering to enhance the construction processes and changes the way engineers and builders work.
Compared to traditional methods, AI in civil engineering brings significant benefits to engineering design, analysis, and construction management. This article discusses some ways through which AI is used in civil engineering.
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Importance of AI in Civil Engineering
The basic idea behind incorporating AI and automation in civil engineering is to perform a task using algorithms and machines in a more efficient manner than what is expected from humans. AI algorithms have the potential to help the construction sector overcome challenges and improve overall productivity and efficiency.
The traditional methods used for modeling and optimizing complex structural systems consume considerable time and computing resources. But AI-based algorithms provide better alternatives to solve problems in civil engineering.
The data required to develop AI algorithms are obtained using programmed machines like drones, smart cameras, smart sensors, etc. The data is analyzed to determine all possible construction aberrations and anomalies. AI algorithms also use trial and error methods to identify the best process that needs to be followed depending on the site conditions. Hence, such implementation for project execution improves the quality and productivity of the overall project.
Artificial Intelligence Techniques in Civil Engineering
Here are some AI techniques used in civil engineering:
1. Evolutionary Computation (EC)
Evolutionary computation (EC) is a subcategory of AI being used in civil engineering for several decades. It uses an iterative process and is an effective method to solve complex optimization problems. The standard evolutionary algorithms used in civil engineering are Genetic Algorithms (GAs), Artificial Immune Systems (AIS), and Genetic Programming (GP).
2. Artificial Neural Networks (ANNs)
The neural networks collect, memorize, analyze and process massive amounts of data obtained from experiments or numerical analyses to provide basic solutions to complex engineering problems. It has widespread application in studying building materials, structural engineering, geotechnical engineering, construction management, and identifying structural defects.
3. Fuzzy Systems
A fuzzy system is a tool for adapting the human way of thinking and problem solving when dealing with uncertain issues faced in construction projects. It considers several aspects like the quality of material, equipment, logistics, and physical risk directly related to administrative and financial capacity.
4. Expert System
An expert system is widely used in construction engineering, underground engineering, geotechnical engineering, geological exploration, disaster prevention, material engineering, and the petrochemical industry. This method relies on the existing knowledge of human experts to set up a knowledge system.
The system focuses on a particular area and uses all the corresponding knowledge and experience stored in the programming system to solve complex problems.
Advantages of AI in Civil Engineering
1. Prevent Cost Overruns
AI helps to predict cost overruns based on the project size and contract type. It indicates the competence level of project managers and staff and provides them real-life training to quickly enhance their skills and knowledge. ANN is one such AI technique used in construction project management.
2. Reduce Risk of AccidentsÂ
AI enables the contractors and project managers to monitor and prioritize the risk on the job site and makes the team focus on it. For example, in a construction site, the project monitoring team rates the subcontractors based on a risk score. The construction managers work closely with the respective subcontractor’s worksite who have a high-risk score.Â
3. Efficient Project Planning
3D scans of construction sites are captured autonomously using robots, and these data are fed into neural networks that can classify the status of completion of different projects. If the project is moving off track, the team can focus on the problems and avoid major issues.
5. Increase Productivity on Job Sites
The use of self-driving machinery to perform repetitive construction activities like pouring concrete, welding, demolition, and bricklaying is more efficient in comparison to human labor. There are autonomous bulldozers for performing excavation and preparation work at the construction site. They are controlled by a human programmer and get the job done as per the required specifications.
The use of facial recognition gadgets, onsite cameras, and other technologies helps project managers to track real-time site work. These technologies finally enable to assess worker productivity and conformance to required standards.
Disadvantages of AI in Civil Engineering
1. AI is Expensive
AI is used only by organizations that can afford its implementation and maintenance costs. Incorporating robots and other complex machines is expensive.
2. Rise of Unemployment
AI promotes more computer technologies and industrial robots that demand fewer human workers. Hence, it has a negative impact on the job market.
FAQs
Artificial intelligence applied to civil engineering works based on several techniques like evolutionary computation (EC), fuzzy system, artificial neural network (ANNs), and expert system.
The neural networks collect, memorize, analyze and process massive amounts of data obtained from experiments or numerical analyses to provide basic solutions to complex engineering problems. It has widespread application in studying building materials, structural engineering, geotechnical engineering, construction management, and identifying structural defects.
A fuzzy system is a tool for adapting the human way of thinking and problem solving when dealing with uncertain issues faced in construction projects. It considers several aspects like the quality of material, equipment, logistics, and physical risk directly related to administrative and financial capacity.
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