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Generative Engineering: A Practitioner's Guide to Artificial Intelligence in Civil Engineering provides a comprehensive yet accessible introduction to the application of artificial intelligence (AI) in civil engineering. By demystifying AI and providing actionable guidance, this book empowers civil engineers to leverage these technologies effectively to enhance efficiency, improve decision-making, and drive innovation in their practice.
Coverage includes
- overview of AI technologies relevant to civil engineering, including machine learning, computer vision and natural language processing
- case studies of successful AI implementation across civil engineering disciplines
- strategies for initiating and managing AI projects within engineering firms
- collaborating effectively with external AI experts and technology providers
- developing in-house AI capabilities and upskilling engineering teams
- ethical considerations and potential challenges
- future trends and long-term impact of AI on the civil engineering profession.
Generative Engineering: A Practitioner's Guide to Artificial Intelligence in Civil Engineering bridges the gap between advanced AI technologies and their practical implementation. Written for practicing engineers, project managers, and decision-makers, it provides a clear, non-technical overview of AI concepts relevant to the industry.
Dr.-Eng. Michael Rustell CEng FICE is the founder and CEO of Inframatic. He was previously a Lecturer in Structural Engineering at Brunel University, London. He is a Chartered Civil Engineer and AI specialist with over 15 years of experience in software development, including generative engineering, design automation, and large language models. His extensive background includes roles as Senior Ports Engineer and EMEA Lead Data Scientist at AECOM where he has developed software for use in highly regulated industries.
Part I: Foundations
Chapter 1: Introduction – AI and the Civil Engineer
Chapter 2: How Modern AI Works
Chapter 3: The AI Technology Stack for Civil Engineering
Part II: Applying AI in Practice
Chapter 4: Prompting Strategies for Civil Engineering
Chapter 5: Retrieval-Augmented Generation for Engineering Knowledge
Chapter 6: Agentic AI and System Design
Chapter 7: AI Assurance and Trust in Engineering Contexts
Part III: Implementation
Chapter 8: AI in Practice – Agentic Workflows Across Civil Engineering
Chapter 9: Implementing AI in Your Organisation
Chapter 10: Fine-Tuning and Custom Models
Chapter 11: Regulation, Ethics and Professional Responsibility
Chapter 12: How Agentic AI Reshapes the Engineering Industry
Chapter 13: Practical Next Steps
Appendix A: Technical Deep Dive
Appendix B: Getting Started with Vibe-Coding
Appendix C: How This Book Was Written