3D concrete printing (3DCP) requires precise adjustments to parameters to ensure accurate and high-quality prints. However, despite technological advancements, manual intervention still plays a prominent role in this process, leading to errors and inconsistencies in the final printed part. To address this issue, machine learning vision models have been developed and utilized to analyze captured images and videos of the printing process, detecting defects and deviations. The data collected enable automatic adjustments to print settings, improving quality without the need for human intervention. This work first examines various techniques for real-time and offline corrections. It then introduces a specialized computer vision setup designed for real-time control in robotic 3DCP. Our main focus is on a specific aspect of machine learning (ML) within this system, called speed control, which regulates layer width by adjusting the robot motion speed or material flow rate. The proposed framework consists of three main elements: (1) a data acquisition and processing pipeline for extracting printing parameters and constructing a synthetic training dataset, (2) a real-time ML model for parameter optimization, and (3) a depth camera installed on a customized 3D-printed rotary mechanism for close-range monitoring of the printed layer.

This paper presents a case study on the use of 3D concrete printing (3DCP) to qualify rocky pontoons with spaces for recreational use—namely sitting areas, circulation trails and fishing spots—and biodiversity protection—providing habitat and refuge for native marine species—with a focus on the challenges and opportunities associated with 3DCP prefabrication for such a complex topographical context. We first discuss the benefits and disadvantages of 3DCP over traditional methods for retrofitting strategies with the support of state-of-the-art literature review. We then present a methodology and an experimental case study, organized in three stages: (1) a photogrammetric survey and digital reconstruction of the site´s rocky landscape, (2) the creation of a tool to generate and optimize custom-fit slabs based on their location on site, intended use and role in the protection of the natural ecosystem, and (3) the robotic fabrication of these slabs through 3DCP. Finally, we present our key findings, revealing that 3DCP offers a viable and more efficient alternative for appropriating and revitalizing sites with a disorderly and highly complex topography.

This paper presents a case study on the use of 3DCP to qualify rocky pontoons with spaces for recreational use—namely sitting areas, circulation trails and fishing activities—and biodiversity protection—providing habitat and refuge for native marine species—with a focus on the challenges and opportunities associated with 3DCP prefabrication for such a complex topographical context. We first discuss the benefits and disadvantages of 3DCP over traditional methods for retrofitting strategies with the support of state-of-the-art literature review. We then present a methodology for an experimental case study, organized in three stages: (1) a photogrammetric survey and digital reconstruction of the site´s rocky landscape, (2) the creation of a tool to generate and optimize custom-fit slabs based on their location on site, intended use and role in the protection of the natural ecosystem, and (3) the robotic fabrication of these slabs through 3DCP. Finally, we present our key findings, revealing that 3DCP offers a viable and more efficient alternative for appropriating and revitalizing sites with a disorderly and highly complex topography.

The Architecture, Engineering and Construction (AEC) industry requires strategies to digitalize the built environment. These strategies need to facilitate the development of as-is models from existing repositories and databases, making the management of this information more efficient throughout the asset’s life cycle. One methodology frequently applied in AEC is Building Information Modeling (BIM), which allows the creation of digital replicas of the built environment. To test the advantages of BIM in the management of railway infrastructures, a BIM-based plugin was developed to facilitate the visualization and manipulation of a repository containing BIM models of several railway tunnels. The article elaborates on the methodology adopted in the development of the plugin, as well as the management process it supports. The advantages of centralizing various types of data in a shared 3D model are also discussed, not only in terms of communication and data sharing between various stakeholders, but also regarding decision-making processes and the planning of inspection/intervention activities throughout the assets’ life cycle. O setor da Arquitetura, Engenharia e Construção (AEC) necessita de processos de suporte à digitalização do património construído que facilitem o desenvolvimento de modelos as-is a partir de repositórios de informação alfanumérica e geométrica existentes e consequentemente tornem a gestão dessa informação mais eficiente ao longo do ciclo de vida do ativo representado. Uma metodologia frequentemente aplicada no setor AEC é o Building Information Modelling (BIM), que permite criar réplicas digitais do património construído. Com o objetivo de testar as vantagens da metodologia BIM para as estratégias de manutenção das infraestruturas ferroviárias existentes, foi desenvolvida uma extensão de aplicação em plataformas de base BIM de suporte à gestão de um repositório de modelos de túneis ferroviários e uma interface gráfica para facilitar a sua manipulação. O artigo elabora sobre a metodologia adotada no desenvolvimento da extensão BIM, assim como no processo de gestão por esta suportado. Também são discutidas as vantagens de centralizar vários tipos de informação num modelo 3D partilhado, não só em termos da comunicação e partilha de dados entre vários colaboradores, mas também relativamente à tomada de decisão e ao planeamento de ações de inspeção/intervenção ao longo do ciclo de vida do ativo representado.