bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data augmentation approach in detecting roof pathologies with UASs images
Authors: Staffa L , Nogueira J , Lima M , Ottoni A , Costa D B , Novo M 
Published: December 2022

Abstract
Machine learning and computer vision techniques contribute to the automation roof pathologies identification from images collected with Unmanned Aerial System (UASs). However, one of the challenges for practical machine learning model tuning is the small-data problem. One strategy is to adopt data augmentation for generating more training data from existing images. This paper evaluates data augmentation in detecting pathologies in roof inspections with UASs images. The study adopted data augmentation for training two models in an image processing system. The training and tests using data augmentation images obtained superior results in accuracy, precision, recall, F-score, negative precision, and specificity metrics compared to the study using only original photos. These results indicate that data augmentation improves the adopted system’s performance in identifying roof pathologies in UAS images. This inspection system proposed with such integrated technologies would make it possible to increase transparency, simplify steps and reduce the time to perform roof inspections, streamlining the preparation of reports and application of corrective actions.
Privacy and security in the use of wearable internet of things for construction safety and health monitoring
Authors: Okonkwo C , Awolusi I , Nnaji C 
Published: December 2022

Abstract
Due to the dynamic and complex nature of construction sites, the conventional method of construction safety monitoring which relies mainly on manual observation by inspectors is highly susceptible to human errors, is time-consuming, and is also becoming increasingly difficult to identify all incidents. Wearable Internet of Things (WIoT) in the construction industry creates a lot of opportunities for safety and health management such as identifying real-time locations of workers, body temperature, heart rate, stress level, and breathing rate, which can all be used to ensure that workers are always in safe environments and good health conditions. The use of WIoT for safety and health monitoring however presents research need on the privacy and security of the construction safety and health data collected, transmitted, and processed over the internet. This study examines the concept of privacy and security in WIoT, the security challenges, infrastructure requirements, and legal issues associated with WIoT. A review of privacy and security regulations for safety and health data is also presented in this paper. This study is expected to generate scientific information that can be used to develop an effective privacy and security framework to foster the adoption and implementation of IoT-based wearable sensing devices (WSDs) for safety and health monitoring in construction.
Improving posture recognition among construction workers through data augmentation with generative adversarial network
Authors: Zhao J , Obonyo E , Yin Q 
Published: December 2022

Abstract
Deep Neural Networks (DNN) models have shown high potential in recognizing workers’ risky postures using data from wearable Inertial Measurement Units (IMUs). However, there is a data paucity challenge - DNN models require a large dataset with annotation for desirable performance. The research discussed in this paper proposes to address this problem through a data generation framework that leverages Generative Adversarial Network (GAN) to i) synthesize motion data, ii) augment training data, then iii) improve the recognition performance. Its potential was validated using naturalistic posture data of workers. Three GAN models were developed for data generation. A Train on Real and Test on Hybrid approach was used to quantitatively assess synthesized data and select sufficiently-trained GAN models. The performance of three commonly-used DNN models was compared after data augmentation. Results showed that the augmentation with GAN-synthesized data improved recognition accuracy by 1.2%-3% for varying postures. These findings suggest the feasibility of applying motion data augmentation with GAN models to advance automated construction safety monitoring.
Multi-requirements ontology engineering for automated processing of document-based building codes to linked building data properties
Authors: Zentgraf S , Hagedorn P , König M 
Published: December 2022

Abstract
The lifecycle of a building is characterized by precise compliance with regulatory specifications for both traditional planning as well as digital planning methods such as Building Information Modeling (BIM). Currently, this regulatory information is mostly available in the unstructured and non-machine-readable form of guidelines, regulations, and standards. The acquisition and conversion of unstructured data into structured semantic knowledge bases for the use in the BIM processes are increasingly being automated using natural language processing in ongoing research. Since ontologies provide such techniques that allow raw data to be formally transformed into domain knowledge, in this paper an ontology is developed based on the data format for data catalogs, properties, and groups of properties described in the ISO 23386. The contribution of this paper is a systematic requirements analysis based on existing literature, standardization, and existing ontologies and the implementation of an ontology accordingly. Applied to a feature of interest, an exemplary property validation using the SPARQL Protocol and RDF Query Language (SPARQL) and generated Shapes and Constraint Language (SHACL) shapes is performed to show how the collected data can be used for automatic constraint checking.
Digital Measurement of Construction Performance: Data-to-dashboard Strategy
Authors: Murguia Danny , Chen Qian , Van Vuuren Tercia Jansen , Rathnayake Asitha , Vilde Vladimir , Middleton Campbell 
Published: December 2022

Abstract
Performance measurement in construction has been a topic of academic and industry inquiry in the UK since the 1990s. Despite the time elapsed, there is little evidence of a consistent industry-wide performance framework that drives decision-making and supports consistent measurement of performance on construction projects. A review of academic advancements and industry practices has been conducted to understand performance measurement in the construction industry, including the metrics assessed, processes for collecting and analysing data, and current limitations. The adoption of digital technologies on construction projects can support timely measurement of performance metrics, allowing for feedback and corrective action to improve performance. However, organisations struggle to connect the top-down measurement value with the bottom-up data capture technologies. The study of an exemplar commercial project was used to inductively develop a data-to-dashboard strategy that supports decision making in construction. The proposed strategy aligns performance metrics, digital tools and processes, and data analysis techniques in a consistent approach to interpret performance-related data and understand key issues. The development and review of the strategy on a live construction project highlights the challenges experienced with multi-source data integration and the translation of information into knowledge that drives decisions and deployment of timely corrective measures. The application of the strategy would ensure a consistent definition of metrics early in the project, and the continuous measurement of leading indicators. Future research will review the proposed strategy on further case study projects and develop an industry-wide multi-level performance measurement framework that uses the proposed strategy to improve performance.
Enabling building digital twin: Ontology-based information management framework for multi-source data integration
Authors: Xie X , Moretti N , Merino J , Chang J Y , Pauwels P , Parlikad A K 
Published: December 2022

Abstract
The emergence of the digital twin concept can potentially change the way people manage built assets thoroughly. This is because the semantics-based model and linked data approach behind the digital twin, as the successor of classical BIM, provide strong capability in integrating data from fragmented and heterogeneous sources and thus enable better-informed decision-making. Taking buildings as the case, this paper demonstrates the ontology-based Information Management Framework and elaborates on the process to integrate data through a common data model. Specifically, the Foundation Data Model (FDM) representing the operation of buildings and embedded systems is developed and two patterns of integration architecture are compared. To conceptualise all the essential entities and relationships, the building topology ontology and BRICK ontology are reused and merged to serve as a feasible FDM. According to the characteristic of asset management services that digital twins support, two integration architectures are compared, including the data warehouse approach and the mediator approach. A case study is presented to elaborate on the implementation of these two approaches and their applicability. This work sets out the standardised and modularised paradigms for discovering, fetching, and integrating data from disparate sources with different data curation manners.
Exploring quality issues in building information models via structural design reviews
Authors: Candir E , Atasoy G 
Published: December 2022

Abstract
Building Information Models (BIMs) are being used widely in the construction industry. BIMs generally contain errors and mistakes, if not identified, might result in long review processes with the employers or cause issues during code compliance checks or construction. Since the quality requirements vary according to projects and even the stakeholders within the same project, it is challenging to establish a standard quality-check procedure. To focus on the model quality, custom procedures tailored according to the needs of the design teams should be prepared. Hence, there is a need to explore the BIM quality issues and develop formulations to identify and eliminate them in time. Currently, there are no specific guidelines and formulations to determine the overall quality level of a BIM and propose Quality Assurance (QA) and Quality Control (QC) procedures. In this study, design and model reviews from the owner are used to analyze the items to be checked for satisfying a structural model’s integrity. The design reviews are classified and analyzed to understand the existing issues. This study contributes to a better understanding of BIM quality issues and proposes a direction toward BIM quality assurance and control formulation that can be tailored by practitioners and design teams.
Challenges in developing a holistic Whole Building Life Cycle Assessment (WBLCA) software tool: developers’ goals
Authors: Sartori T , Drogemuller R , Omrani S , Lamari F 
Published: December 2022

Abstract
The Intergovernmental Panel on Climate Change (IPCC) recently released a report emphasizing the importance of reducing carbon emissions, indicating that the construction industry is a key player in this process. Whole Building Life Cycle Assessment (WBLCA) is the most efficient method for evaluating buildings’ environmental impacts. It considers the effects of energy consumption during operation and the environmental impacts of extracting, manufacturing, transporting, assembling, and maintaining the building’s components. However, numerous obstacles prevent WBLCA from being fully applied by decision-makers during the building project process (BPP). This paper discusses and suggests improvements to the challenges of integrating WBLCA and BPP. An online survey was performed with nine WBLCA software developers worldwide, examining the critical challenges and how to overcome them. Most participants agreed that increasing WBLCA demand is the most pressing issue. It would encourage the entire construction sector to adopt a life-cycle mindset, pushing WBLCA software developers to improve software tool integration with the BPP. Results from this study will assist software companies and policymakers develop tools and regulations more aligned with the interests of designers.
Enhancing the resilience of low-income housing using emerging digital technologies
Authors: Obonyo E A , Ouma L A 
Published: December 2022

Abstract
The research discussed is part of a Belmont Forum disaster risk reduction project aimed at enhancing the resilience of low-income housing. This paper examines feasibility and viability of using emerging digital technologies to enhance the resilience of low-income housing based on requirements of resource constrained, low-lying coastal areas in East Africa. The authors focus on the need to facilitate data and knowledge sharing across domains to: 1) reduce or avoid the potential property loss from flooding events through mapping the interdependencies and interconnectedness across natural and human systems; 2) coordinate the provision of temporary shelter for displaced victims, and 3) building (back) better during the recovery phase. The deployment of Artificial Intelligence, Internet of Things, BIM, Digital twin, VR/AR in disaster risk management is still an emerging area of research. In general, cutting-edge digital technologies are deployed as standalone solutions to address existing data and knowledge sharing needs that are unique to a sub-group of stakeholders. A more holistic and comprehensive solution will require an integrative framework that supports the seamless flow of information across the stakeholders. We propose to address this need through an artificial intelligence enhanced data, information and knowledge sharing platform that synthesizes content into actionable insights
Lessons from implementation of Key Technological Developments to improve occupational safety and health processes in a complex UK-based construction project
Authors: Tender Manuel , Fuller Paul , Vaughan Alex , Long Marc , Couto João Pedro , Damien Peter , Chow Vivien , Silva Firmino , Reis Francisco , Reis Ricardo 
Published: December 2022

Abstract
The use of new Key Technological Developments to transform the way Occupational Safety and Health is managed is now becoming a viable option for contractors to consider. These developments can be used to modify the traditional approaches to Occupational Safety and Health to minimize the risks and costs associated with accidents at work and occupational diseases. The research reported in this paper aims to examine the benefits, barriers and challenges associated with the introduction of new digital information technologies in Occupational Safety and Health settings. A longitudinal case study of a complex construction project based in the United Kingdom is reported, which will be used to identify practical solutions and guidelines that can assist organisations in the adoption of these technologies. The lessons learned, both positive and negative, from this project are shared to provide valuable insights to the wider construction industry. The main focus is in the following areas: training; risks identification; site planning. The results of the study can be used to optimise how Health and Safety risks are managed in non-United Kingdom construction projects. The main benefits from this is the resulting productivity gains which can improve project outcomes significantly.
Health, well-being and comfort in smart buildings innovation: state-of-play and opportunities.
Authors: Kubicki Sylvain , Zarli Alain , Coujard Clémentine , Guerriero Annie 
Published: December 2022

Abstract
The market of smart building solutions aiming at improving the comfort, well-being and health of occupants, but also the inclusiveness and accessibility of the building to people with more specific needs is growing. More and more smart solutions, as well as standards, certifications and public regulations are currently being developed, but a more global and systemic approach seems to be missing. The paper reviews the status of research related to the means and methods enabling interactions with occupants in smart buildings, which feeds an iterative approach with European industry and research actors aiming at collectively identify barriers and opportunities in this area.
Integration of blockchain with emerging technologies in AEC industry: Merits and challenges
Authors: Gupta Purva , Jha Kumar Neeraj 
Published: December 2022

Abstract
While in the last decade, digital tools and technologies have made their way into the construction industry, digital growth is still slow and incremental. The challenge is the consequential need for a collaborative and trustworthy environment with cooperation and concurrence among stakeholders. Blockchain technology holds promise and potential in providing this environment, infusing transparency and security in the mix. This study is thus motivated to explore the emerging information and communication technologies (ICT) in the architecture, engineering, and construction (AEC) industry and their integration with blockchain technology. Greater amounts of real-time digital information and automated systems with the security and transparency of blockchain could be a game-changer in planning and executing construction projects. It will affect not just the decision-makers at the top managerial level but will assist individuals down the line on-field to make decisions based on real-time data available to them.
Analyzing the impact of simulation fidelity on VR-enabled hazard detection on construction sites – A case study on crane lift operation
Authors: Jiang Tanghan , Goh Jiantsen , Xu Zheng , Zheng Nan , Fang Yihai 
Published: December 2022

Abstract
Crane operator training is an essential part of construction safety and is attracting extensive attention from researchers worldwide. Virtual reality (VR) is considered an effective tool to improve training outcomes by providing users with an immersive, risk-free experience in various environments. However, previous VR-based training platforms mainly focused on the scenario and task design; few studies attempted to investigate the impact of simulation fidelity on training efficiency. This research aims to explore the effect of simulation fidelity on training outcomes by comparing user performance in two scenarios. A typical construction site was modelled in a game engine using two rendering approaches; an eye-tracking system was adopted for data collection. The results from a subject experiment indicated the high efficiency of VR in operator safety training and demonstrated the usefulness of eye-tracking in measuring hazard detection performance. Findings showed that a higher level of simulation fidelity might not significantly improve the training efficiency, especially in hazard detection aspects.
Neural Semantic Parsing of Building Regulations for Compliance Checking
Authors: Fuchs S , Witbrock M , Dimyadi J , Amor R 
Published: December 2022

Abstract
Computerising building regulations to allow reasoning is one of the main challenges in automated compliance checking in the built environment. While there has been a long history of translating regulations manually, in recent years, natural language processing (NLP) has been used to support or automate this task. While rule- and ontology-based information extraction and transformation approaches have achieved accurate translations for narrow domains and specific regulation types, machine learning (ML) promises increased scalability and adaptability to new regulation styles. Since ML usually requires many annotated examples as training data, we take advantage of the long history of building code computerisation and use a corpus of manually translated regulations to train a transformer-based encoder-decoder model. Given a relatively small corpus, the model learns to predict the logical structure and extracts entities and relations reasonably well. While the translation quality is not adequate to fully automate the process, the model shows the potential to serve as an auto-completion system and to identify manually translated regulations that need to be reviewed.
Development of the Reward Function to support Model-Free Reinforcement Learning for a Heat Recovery Chiller System Optimization
Authors: Landry Jean-Francois , McArthur J. J. , Genkin Mikhail , Mokhtari Karim El 
Published: December 2022

Abstract
Heat recovery chiller systems have significant strategic value to reduce building greenhouse gas emissions although this potential remains unrealized in practice. Real-time optimization using model-free reinforcement learning provides a potential solution to this challenge. A full-scale case study to implement reinforcement learning in a 6,000 m2 academic laboratory is planned. This paper presents the methodology used to translate historical data correlations and expert input from operations personnel into the development of the reinforcement learning agent and associated reward function. This approach will permit a more stable and robust implementation of model-free reinforcement learning and the methodology presented will allow operator-identified constraints to be translated into reward functions more broadly, allowing for generalization to similar heat recovery chiller systems.
Implementation of integrated BIM-VR into construction management curriculum: lessons learned and development of a decision support system
Authors: Abbasnejad Behzad , Aranda-Mena Guillermo , Nasirian Araz , Wong Peter SP , Ahankoob Alireza 
Published: December 2022

Abstract
Several educational institutions and universities have introduced Building Information Modelling (BIM) and Virtual Reality (VR) courses separately in the construction management (CM) programs around the globe, and others are in the process of integrating them into their curricula. However, fewer institutions have included integrated BIM and VR into their courses. The industry needs for integrated BIM-VR are not fully recognised by universities due to the lack of a guideline and cognitive dissonance between academia and industry. As a result, most universities do not have a rigorous strategy for developing BIM-VR teaching topics and plans. This study firstly aims to report on some of the employed processes by university lecturers and academics in an Australian University to integrate BIM-VR education into CM curriculum. The review on the secondary sources indicates that the integration of BIM/VR education requires the consideration of three aspects including: industry needs, course contents, classroom size and software and hardware selection. The second aim of this paper is to develop a decision support system by utilising the PROMETHEE method as one of the multi-criteria decision-making approaches. The findings of the secondary sources and development of the decision support system help the educational institutions and practitioners for their future BIM-VR implementation initiatives.