AI, Robotics, and 3D Printing
Constructing the Sustainable City of Tomorrow
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Beschreibung
This book presents a rigorous and forward-looking synthesis of artificial intelligence, robotics, and automated fabrication as they converge to redefine the understanding and practice of modern construction. In this volume, the construction site is no longer a domain dominated by manual labor or static workflows, but rather an intelligent ecosystem—self-correcting, behaviorally aligned, and governed by algorithmic foresight. At the heart of this transformation lies a multi-layered architecture of computational learning that reconfigures how buildings are planned, optimized, and erected in real time. The author begins by embedding resource allocation into a mathematically structured landscape, introducing Multi-Agent Reinforcement Learning (MARL) with cooperative Q-learning as a baseline for decentralized decision-making. Quantum Genetic Algorithms (QGAs) and Graph Convolutional Networks (GCNs) elevate resource planning into a multi-objective, simulation-driven process, where digital twins embedded in BIM environments act as live engines for optimization. This progression culminates in the use of LSTM-based predictive analytics and Proximal Policy Optimization (PPO) scheduling agents, enabling proactive orchestration of labor, materials, and equipment under uncertainty. Dr. Sargiotis maps a tectonic transformation in how we conceive, design, and construct our cities. Through mathematics, machine intelligence, and moral insight, construction becomes a site of environmental stewardship, ethical innovation, and computational creativity.
- Explains AI-driven construction optimization for smarter cities
- Demonstrates robotic automation for efficient, sustainable building
- Shows how 3D printing redefines construction material use and design
This book presents a rigorous and forward-looking synthesis of artificial intelligence, robotics, and automated fabrication as they converge to redefine the understanding and practice of modern construction. In this volume, the construction site is no longer a domain dominated by manual labor or static workflows, but rather an intelligent ecosystem—self-correcting, behaviorally aligned, and governed by algorithmic foresight. At the heart of this transformation lies a multi-layered architecture of computational learning that reconfigures how buildings are planned, optimized, and erected in real time. The author begins by embedding resource allocation into a mathematically structured landscape, introducing Multi-Agent Reinforcement Learning (MARL) with cooperative Q-learning as a baseline for decentralized decision-making. Quantum Genetic Algorithms (QGAs) and Graph Convolutional Networks (GCNs) elevate resource planning into a multi-objective, simulation-driven process, where digital twins embedded in BIM environments act as live engines for optimization. This progression culminates in the use of LSTM-based predictive analytics and Proximal Policy Optimization (PPO) scheduling agents, enabling proactive orchestration of labor, materials, and equipment under uncertainty. Dr. Sargiotis maps a tectonic transformation in how we conceive, design, and construct our cities. Through mathematics, machine intelligence, and moral insight, construction becomes a site of environmental stewardship, ethical innovation, and computational creativity.
Explains AI-driven construction optimization for smarter cities Demonstrates robotic automation for efficient, sustainable building Shows how 3D printing redefines construction material use and design
Autor*in
Dimitrios Sargiotis
Themen in »AI, Robotics, and 3D Printing«
AI robotics construction methods application of generative design algorithms optimization of construction logistics intelligent construction systems Internet of Things (IoT) AI-Driven Construction Robotics in Construction Deep Reinforcement Learning (DRL) 3D Printing in Architecture Generative Adversarial Networks (GANs) Modular and Prefabricated Construction Multi-Agent Reinforcement Learning (MARL) Proximal Policy Optimization (PPO)