Artificial intelligence (AI) provides a wide range of current society applications, including predicting, classifying, and solving both social and scientific problems. As one of the oldest and most traditional engineering disciplines, civil engineering covers various aspects of the built environment, from design and construction to maintenance. Civil engineering offers ample practical scope for applications of AI. In turn, AI can improve human life quality and originate novel approaches to solving engineering problems.
AI methods and techniques, including neural networks, evolutionary computation, fuzzy logic systems, and deep learning, have rapidly evolved over the past few years. AI algorithms have recently attracted close attention from researchers and have also been applied successfully to solve problems in civil engineering, i.e., intelligent and fully automatic urban and regional planning, and developing new technologies in civil engineering design, construction, maintenance, and disaster management.
McKinsey’s report reveals that the civil construction sector has a net worth of over USD 10 trillion a year. Not only is AI making construction operations more manageable, but it is also set to make the construction business more lucrative. The report also estimates that construction companies integrating AI techniques are 50% more likely to generate profits than those who don’t.
AI offers a wide range of applications in civil engineering that processes and transforms the way builders and engineers work.
Construction enterprises utilize deep-learning techniques to improve the level of quality of their construction processes. Image recognition of photographs gathered through manual drones is used to detect risk areas and is also compared against existing blueprints to identify any possible construction defects. AI algorithms can use trial and error techniques to recognize the best processes, followed through reinforcement learning. By implementing these process changes in project planning and scheduling, construction companies can significantly improve their overall project workflow quality.
Stakeholders can also use neural networks and laser-generated images to gain insights into individual construction projects’ progress. By using artificial intelligence to create 3D models, they can match them with the original models to check for any quality discrepancies. It can significantly stimulate the decision-making process while also implementing actionable insights.
Building Information Modeling is a 3D model-based process that gives architecture, engineering, and construction professionals’ insights to efficiently plan, design, construct and manage buildings and infrastructure. The 3D model needs to consider the architecture, engineering, mechanical, electrical, and plumbing (MEP) plans and the sequence of activities of the respective teams to plan and design the construction of a building. The challenge is to make sure that the different sub-teams’ different models do not clash with each other. The industry is trying to use machine learning in the form of generative design to recognize and mitigate clashes between the different models generated by the various teams in the planning and design phase to prevent work. Some software uses a machine-learning algorithm to explore all the variations of a solution and generates design alternatives. It leverages machine learning to create 3D models of mechanical, electrical, and plumbing systems while simultaneously ensuring that MEP systems’ entire routes do not clash with the building architecture. It learns from iteration to come up with an optimal solution.
Prevent Cost Overruns
Most big projects exceed budget despite employing the best project teams. Artificial neural networks are used to predict cost overruns based on factors like project size, contract type, and the competence level of project managers. Predictive models use historical data such as planned start and end dates to envision realistic timelines for future projects. AI helps staff remotely access real-life training material that helps them improve their skills and knowledge quickly. It minimizes the time taken to onboard new resources onto projects. Consequently, project delivery is expedited.
Artificial neural networks through artificial intelligence can be useful measures for risk control as they interpret a collection of construction information to draw meaningful conclusions. It helps construction firms predict the likelihood of possible failures, therefore, preparing them to develop appropriate contingency plans.
Construction firms can also apply AI techniques to address client and market risk factors. Through the Naive Bayes algorithm, engineers can perform sentiment analysis on their firm’s standing in the market and thus come with targeted efforts to prevent stock prices from falling. Other AI algorithms can also be segment customers based on their characteristics and behaviour patterns to deliver better business development strategies, hence preventing them from risks of likely fallout.
Making project development faster and more cost-effective, AI has carved a niche for itself in the civil sector. The construction industry is at a point where it is poised at a technological breakthrough in its processes. In this respect, investing in AI courses is bound to provide a competitive edge to anyone who’s building a career in civil engineering.
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