This chapter explores the integration of artificial intelligence (AI) and machine learning (ML) technologies in structural engineering, focusing on their applications in automating design processes, optimizing structural configurations, and assessing performance metrics. It highlights the efficiency of AI-driven algorithms in generating design alternatives, predicting structural behavior, and enhancing sustainability. The chapter also provides a performance comparison framework for evaluating different structural designs, considering safety, cost-effectiveness, and environmental impact. It discusses case studies and practical examples that demonstrate the advantages of AI/ML-driven autonomous design in achieving superior structural performance while minimizing resource utilization. The chapter emphasizes the potential of AI and ML in revolutionizing structural engineering, enabling engineers to create sustainable and high-performing structures, contributing to a more environmentally conscious and economically viable built environment.
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The field of structural engineering is facing a growing demand for innovative solutions that prioritize sustainability and performance efficiency, as architects face the challenge of optimizing structures for safety and sustainability while adhering to strict budget constraints. The traditional structural design method is time-consuming and heavily reliant on human expertise. Advancements in AI and machine learning have revolutionized this process. This chapter explores the integration of intelligent structural engineering with optimization techniques, performance comparison methodologies, and sustainable design principles, all powered by AI and ML technologies (L. Sun et al., 2020a).
This chapter explores the use of AI and ML in structural engineering, focusing on their automation, optimization, and performance metrics. It also discusses sustainable design principles and their integration with AI and ML to create more environmentally conscious structures. Through case studies and practical examples, it showcases the benefits of AI/ML-driven autonomous design, improving structural performance and resource utilization. The chapter also addresses challenges and ethical considerations associated with AI and ML integration in structural engineering (Möhring et al., 2020).
Artificial intelligence and machine learning are revolutionizing structural engineering by providing new tools and techniques for building design, analysis, and maintenance. AI and ML algorithms can analyze vast datasets, identifying patterns that humans cannot, leading to improved predictive modeling and risk assessment. Engineers can use AI to analyze historical data, such as construction failures and maintenance records, to identify potential issues and recommend design modifications, ultimately improving the safety and longevity of structures (Shea & Smith, 2005).
AI-powered design optimization tools aid engineers in creating efficient and cost-effective structures by suggesting innovative solutions considering parameters like materials, load distribution, and environmental factors, thereby reducing costs and minimizing environmental impact. AI and ML are enhancing real-time monitoring and maintenance of structures, enabling early detection of damage and reducing maintenance costs. This proactive approach ensures building safety and durability. AI-driven simulations, such as complex finite element analysis and computational fluid dynamics, can improve the accuracy and efficiency of structural analysis, enabling more resilient and reliable design of structures under various conditions (Liu et al., 2004). The integration of AI and ML with Building Information Modeling (BIM) enhances collaborative decision-making, improving stakeholder communication, project management, and design, construction, and maintenance outcomes. These tools are becoming indispensable in structural engineering, paving the way for a new era of innovation and efficiency. AI and ML enable structural engineers to create intricate designs that optimize cost, energy efficiency, and material usage. ML algorithms can adapt to local environmental conditions, ensuring structures can withstand earthquakes or extreme weather events. AI can also analyze complex data from sensors in buildings, providing real-time feedback on structural health (Salehi & Burgueño, 2018).
AI and ML are crucial in risk assessment, identifying potential weaknesses in new projects and minimizing structural failures. They also aid in advancing sustainable construction practices by optimizing building designs for energy efficiency and sustainability. AI algorithms can identify renewable energy sources and fine-tune HVAC systems for optimal energy consumption (L. Sun et al., 2020b). ML supports the use of sustainable materials by analyzing their performance and environmental impact. AI and ML are also accelerating innovation in materials science and construction techniques. They can help engineers discover new composite materials with improved strength and durability (Huang & Fu, 2019). Additionally, AI can optimize construction processes, enhancing productivity and reducing waste. By analyzing construction data, AI can identify areas for process scalability, leading to cost savings and more sustainable practices (Pan & Zhang, 2021). AI and ML are increasingly being used in urban infrastructure design and maintenance, enabling predictive maintenance systems for critical components like bridges and tunnels. These smart city initiatives improve traffic flow, manage utilities, and respond to emergencies, ensuring the efficient functioning of urban environments (Guo et al., 2021).