Structural Systems Powered by AI and Machine Learning Technologies

Introduction

As architects confront the difficulty of optimizing buildings for safety and sustainability while adhering to severe budget limits, the discipline of structural engineering is seeing an increase in demand for novel solutions that emphasize sustainability and performance efficiency. Traditional structural design methods take time and rely primarily on human knowledge. AI and machine learning advancements have transformed this procedure. This chapter looks at how intelligent structural engineering may be combined with optimization approaches, performance comparison methodologies, and sustainable design principles, all of which are driven by AI and ML technologies (L. Sun et al., 2020a).

This chapter investigates the use of AI and ML in structural engineering, with an emphasis on automation, optimization, and performance measures. It also explores sustainable design ideas and how they might be combined with AI and machine learning to develop more ecologically aware architecture. It demonstrates the advantages of AI/ML-driven autonomous design, enhancing structural performance and resource usage, using case studies and practical examples. The chapter also discusses the problems and ethical concerns related with the integration of AI and ML in structural engineering (Möhring et al., 2020).

Artificial intelligence and machine learning are reshaping structural engineering by introducing new tools and approaches for structure design, analysis, and upkeep. AI and machine learning algorithms can examine large datasets and uncover patterns that people cannot, result in better predictive modeling and risk assessment. Engineers may use artificial intelligence (AI) to examine historical data, such as construction failures and maintenance records, to detect possible concerns and offer design changes, thereby enhancing the safety and durability of buildings (Shea & Smith, 2005).

AI-powered design optimization tools assist engineers in constructing efficient and cost-effective structures by recommending new solutions that take into account criteria such as materials, load distribution, and environmental variables, decreasing costs and minimizing environmental impact. AI and machine learning are improving real-time monitoring and maintenance of structures, allowing for early diagnosis of deterioration and lowering maintenance costs. This proactive strategy protects the safety and longevity of the building. AI-driven simulations, such as complicated finite element analysis and computational fluid dynamics, may increase structural analysis accuracy and efficiency, allowing for more robust and trustworthy structure design under varying circumstances (Liu et al., 2004). AI and machine learning (ML) integration with Building Information Modeling (BIM) improves collaborative decision-making, stakeholder communication, project management, and design, construction, and maintenance results. These technologies are becoming more important in structural engineering, ushering in a new age of creativity and efficiency. Artificial intelligence and machine learning allow structural engineers to construct detailed designs that maximize cost, energy efficiency, and material utilization. ML algorithms are capable of adapting to local environmental circumstances, guaranteeing that constructions can endure earthquakes and harsh weather occurrences. AI can also access complicated data from building sensors and provide real-time feedback on structural health (Salehi & Burgueo, 2018).