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From Waste to Structure: A Deep Reinforcement Learning Approach to Circular Design

Sørensen, Karl-Johan I. (Massachusetts Institute of Technology, 2024-05)

The design-to-construction process of buildings predominantly follows a top-down linear workflow, where a design is drawn and subsequently refined to determine the required materials and components. This approach assumes ...

Automated Engineering Design for Reusable Concrete Building Structures

Wongsittikan, Pitipat (Massachusetts Institute of Technology, 2024-05)

Concrete contributes to 8% of global CO2 emission through reinforced concrete (RC) structural system. Unlike steel and timber structures, RC components are rarely reused due to the inseparable phase between concrete and ...

In Tension: Computational exploration of the design space of tensile network structures

Burke, Adam T. (Massachusetts Institute of Technology, 2024-05)

Cable and rope net structures are lightweight tensile systems and generally cannot resist compression or bending. Tensile network structures are often used to span long distances without intermediate supports and have ...

DSpace@MIT

Building Technology Program Theses: Recent submissions

From Waste to Structure: A Deep Reinforcement Learning Approach to Circular Design

Automated Engineering Design for Reusable Concrete Building Structures

In Tension: Computational exploration of the design space of tensile network structures

Building Technology Program Theses: Recent submissions

From Waste to Structure: A Deep Reinforcement Learning Approach to Circular Design ﻿

Automated Engineering Design for Reusable Concrete Building Structures ﻿

In Tension: Computational exploration of the design space of tensile network structures ﻿

From Waste to Structure: A Deep Reinforcement Learning Approach to Circular Design

Automated Engineering Design for Reusable Concrete Building Structures

In Tension: Computational exploration of the design space of tensile network structures