Artificial intelligence and machine learning in civil engineering

Publish Year: 1403
نوع سند: مقاله کنفرانسی
زبان: English
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SECONGRESS02_071

تاریخ نمایه سازی: 19 مرداد 1403

Abstract:

Artificial intelligence (AI) and machine learning (ML) have revolutionized the field of civil engineering in recent years. These technologies have the potential to streamline processes, improve decision-making, and enhance efficiency in various aspects of civil engineering projects. AI and ML algorithms can analyze vast amounts of data to identify patterns, predict outcomes, and optimize designs, ultimately leading to more cost-effective and sustainable solutions. One of the key applications of AI and ML in civil engineering is in structural health monitoring. By using sensors to collect data on the condition of infrastructure like bridges and buildings, AI algorithms can detect signs of damage or deterioration early on, allowing for timely repairs and maintenance. This proactive approach can help prevent catastrophic failures and ensure the safety of the public. Additionally, AI can be used to optimize construction processes by analyzing data on material usage, equipment performance, and labor productivity. By identifying inefficiencies and suggesting improvements, AI can help reduce costs and shorten project timelines. Another area where AI and ML are making an impact in civil engineering is in predictive modeling. By analyzing historical data on weather patterns, soil conditions, traffic flow, and more, AI algorithms can predict the potential impact of these factors on infrastructure projects. This information can be used to make informed decisions about design, planning, and risk management. Additionally, AI can be used to automate tasks like surveying, mapping, and project scheduling, freeing up engineers to focus on more complex and creative aspects of their work. Overall, the integration of AI and ML technologies in civil engineering has the potential to transform the way projects are planned, designed, and executed, leading to more efficient, cost-effective, and sustainable infrastructure solutions.

Authors

Amin MehrabaniLari

M.Sc., Department of Civil and Environmental Engineering, Shiraz University of Technology,Shiraz, Fars, Iran