The science behind the Tech4EUConstruction cluster’s innovations - digital innovation technology
Eight European-funded projects have united in the Tech4EUConstruction cluster, and today are sharing 30 must-read publications that offer fresh insights for researchers and industry professionals
Compared to other sectors, the European construction industry faces significant challenges in sustainability, innovation adoption and labour shortages, with nearly half of the industry’s jobs in short supply. Transforming this sector into a smarter and greener industry will enhance competitiveness, resource efficiency, and attractiveness for young generations.
Eight European-funded projects, tackling these issues, have united in the Tech4EUConstruction cluster, and today are sharing 30 must-read publications that offer fresh insights for researchers and industry professionals.
The cluster is dedicated to creating a lasting impact by exchanging expertise and technical innovations. This article delves into the science behind next-generation technologies and innovations in areas such as building renovation, sustainability monitoring, digital innovation technologies, energy efficiency, renewable energy, and materials and design.
What will be the advancements in AI and robotics shaping the future of the construction industry? Find out below!
This paper introduces the InCUBE dataset, resulting from the activities of the project, focused on unlocking the EU building renovation through integrated strategies and processes for efficient built-environment management (including the use of innovative renewable energy technologies and digitalization). The set of data collects raw and processed data produced for the Italian demo site in the Santa Chiara district of Trento (Italy). The diversity of the shared data enables multiple possible uses, investigations and developments, and some of them are presented in this contribution.
BEEYONDERS core ambition is to address challenges by producing, commercializing and integrating beyond the state-of-the-art solutions into real construction scenarios. To do so, they will make extensive use of AI, automation, and digitisation. This publication presents a modeling sensor noise for better simulations of robotic systems.
This publication released by BEEYONDERS presents an online algorithm to assess work task ergonomics and prevent accidents.
The Reincarnate project is working on a platform that aims to provide information on the life cycle and reuse potential of construction materials and methods to predict and extend product lifetime. This publication highlights the project’s aim to promote circular practices by extending material durability through digital methods, integrating NDT techniques and AI methods for recycling.
The publication introduces the concept of Inverse Design and demonstrates how an open-source app “SLAMD” developed by Reincarnate provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers.
HumanTech is advancing innovative human-centred technologies – from robotic devices and exoskeletons to a new generation of digital twins – to help make the construction industry safer, greener, more efficient and attractive to a new generation of highly skilled workers.
The paper by HumanTech proposes U-RED, an Unsupervised shape Retrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database.
To bridge the gap between theoretical research on point cloud data and manual inspection, HumanTech proposes in this research a list of object-oriented classes for semantic segmentation.
In this publication, HumanTech shares a computer vision for transportation, deep learning for visual perception, object detection.
Applications of deep learning have recently seen a surge in the field of construction. Supervised semantic segmentation of 2D or 3D data acquired from buildings requires the use of annotated data for training, validation, and testing. Although various datasets have been published targeting this application, they lack a common convention and definitions based on construction ontologies. In this work, HumanTech presents a guideline for ontology-based semantic annotation of RGB-D and point cloud datasets for buildings. Such a contribution facilitates the use of deep learning in construction by bridging the gap between this field and computer science.
Recent work in the RECONMATIC project, referring to the UTH (University of Thessaly) team work on design and enhancements of “HyperLedger Fabric”, the open-source technology that uses blockchain to enable trackability and immutability. They explain how we use “Machine Learning” to improve the “Ordering service”, its election process, when the components of this service are distributed to multiple sites. This is especially useful when the components of the RECONMATIC supply chain, used in Work Package 2, are geo-distributed over long distances.
Recent work in the RECONMATIC project, referring to the UTH (University of Thessaly) team work on design and enhancements of “HyperLedger Fabric”, the open-source technology that uses blockchain to enable trackability and immutability. They explain how we use “Machine Learning” to improve the “Ordering service”, in particular its election process, when the components of this service are distributed to multiple sites. This is especially useful when the components of the RECONMATIC supply chain, used in Work Package 2, are geo-distributed over long distances.
The aim of this paper released by RECONMATIC is to critically understand how CDW is classified, as well as to differentiate between the various methods employed in the waste management process. Additionally, it presents a discussion on the existing thought regarding the concept of CE in the context of the construction sector.
One of the most prominent aspects of Hyperledger Fabric is its three-phase transaction flow architecture, which consists of the execution, ordering and validation phases. The ordering phase involves communication between the client and the ordering service, as the latter is responsible for the transaction assembly and distribution. This study released by RECONMATIC reconstructs the ordering phase and proposes a mechanism for faster communication between the client and the ordering service. Notably, the proposed mechanism can be easily integrated in a future Hyperledger Fabric release.
The RoBétArmé European project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. This paper showcases a case study on which novel robotic systems will be developed for the automation of shotecrete application. The outcomes of this research can be widely used in other application technologies related to the construction domain.
The use of Building/Civil-Construction Information Modeling (BIM/CIM) for the creation of a digital representation of the physical process and asset plays a vital role. The construction process considered for this research study is shotcrete application and surface finishing during the construction and finishing phases. The research from RoBétArmé presents the role of adaptive BIM/CIM models for the digital replication of automated shotcreting of civil infrastructure projects.
Autonomous vehicle navigation in complex and unpredictable outdoor environments requires extensive and detailed understanding of the surrounding area and compliance with the traffic rules. In this paper, RoBétArmé attempts to imitate human driver behavior towards autonomous navigation that is suitable for diverse, challenging environments, whether urban, semi-structured or rural-like.
The utilization of Unmanned Aerial Vehicles (UAV) for the inspection of critical power infrastructure has made significant strides in recent years. Hardware and software advancements enabled the transition from manual to semi-autonomous and fully autonomous UAV operations, which are capable of traversing complex environments and identifying potential flaws. This paper from RoBétArmé presents a novel path planning method that leverages robot vision derived from LiDAR (Light Detection and Ranging) and RGB data for the inspection of insulators on power towers.
The accurate and detailed 3D reconstruction of the construction sites plays a vital role in the digitalization of the construction domain, since accurate 3D models constitute the basis for the adaptation of advanced technologies from Industry 4.0 towards realizing Construction 4.0. This study from RoBétArmé provides a comprehensive assessment of key methodologies employed for 3D reconstruction in the construction sector.
The integration of robot vision techniques, specifically focused on 3D reconstruction, assumes paramount significance in the construction sector, serving as a key enabler for fulfilling the imperative digitalization prerequisites inherent to the principles of Industry 4.0. This study from RoBétArmé proposes a real-time 3D reconstruction pipeline, based on common algorithms, that utilizes both RGB and depth information.
In this publication, INCUBE investigates different heating configurations utilizing various renewable thermal sources in conjunction with an HP-based system in order to determine the optimal configuration in terms of efficiency, using an existing, fully functioning residential building in Zaragoza, Spain, as our case study, comprising 40 dwellings.
In this publication, INCUBE shares building life cycle assessments/ innovative sustainable tools.
In this publication, HUMANTECH introduces a novel approach to automated quality control that enhances element-wise quality assessments by exploiting semantics in BIM.
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HORIZON-CL5-2021-D4-01
EUROPEAN COMMISSION
European Climate, Infrastructure and Environment Executive Agency
Grant agreement no. 101069610
This project is funded by the European Union under grant agreement no. 101069610. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them.
