Article
Construction & Building Technology
James Garbett, Thomas Hartley, David Heesom
Summary: Augmented Reality (AR) is increasingly being used in the construction industry to visualize, interact, and collaborate with BIM data. Research shows that marker-based AR systems can better meet the collaborative needs of construction teams.
AUTOMATION IN CONSTRUCTION
(2021)
Review
Chemistry, Multidisciplinary
Jeonghwan Kim, Soomin Lee, Jongwon Seo, Dong-Eun Lee, Hee Seon Choi
Summary: Earthwork is crucial in construction engineering, requiring high accuracy and precise planning. This study utilized an integrated approach of UAV-based point cloud and BIM to verify designs and plan earthwork operations, reducing errors in design and generating explicit task sequences for excavators.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Avinab Marahatta, Sandeep Pirbhulal, Fa Zhang, Reza M. Parizi, Kim-Kwang Raymond Choo, Zhiyong Liu
Summary: With the rapid growth of cloud data centers (CDCs) due to the popularity of cloud computing and high-performance computing, the need to maximize resource utilization and ensure energy efficiency has become critical. An energy-efficient dynamic scheduling scheme (EDS) for real-time tasks in virtualized CDCs has been proposed in this paper to address the challenges of inefficient resource utilization and high energy consumption. By classifying and merging tasks based on historical scheduling records, the EDS significantly improves overall scheduling performance, increases CDC resource utilization, and reduces energy consumption by utilizing the energy efficiencies and optimal operating frequencies of heterogeneous physical hosts.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2021)
Article
Environmental Sciences
Noaman Akbar Sheik, Greet Deruyter, Peter Veelaert
Summary: This research proposes an automated registration method for aligning the as-built point cloud of a building to its as-planned model by extracting plane segments, clustering parallel plane segments, and determining rotation matrices. The method was validated with different datasets and proved to be robust in terms of accuracy and efficiency, especially for registering incomplete buildings under construction.
Article
Chemistry, Multidisciplinary
Kyuhyup Lee, Joonghwan Shin, Soonwook Kwon, Chung-Suk Cho, Suwan Chung
Summary: This study introduces the VDI system recently used in other industries to improve operations in the BIM environment of architectural design companies. It aims to provide initial application for small to medium-sized design firms and validates the KBimVdi system through selected projects using BIM for design work.
APPLIED SCIENCES-BASEL
(2021)
Article
Construction & Building Technology
Seung-Ha Huh, Namhyuk Ham, Ju-Hyung Kim, Jae-Jun Kim
Summary: Building information modeling (BIM) design validation aims to quickly respond to serious design errors. Timely response to BIM requests for information (RFIs) can greatly enhance BIM return on investment (ROI). This study fills the gap in prior research by conducting an economic assessment, using a priority queue model, of the introduction of priority policies in BIM coordination. The findings show that the introduction of priority policies can impact optimal BIM staffing and engineers' waiting cost for high-priority RFIs. We quantify the improved BIM ROI by introducing priority policies in BIM coordination using time and cost metrics derived from the queue model.
AUTOMATION IN CONSTRUCTION
(2023)
Review
Computer Science, Information Systems
Ziad Monla, Ahlem Assila, Djaoued Beladjine, Mourad Zghal
Summary: The AEC industry has experienced significant growth, especially with the use of BIM. However, there are limitations in the interaction between digital and physical environments. Integrating BIM with AR and VR is seen as a solution, but not all companies are able to implement these technologies successfully. Therefore, learning from maturity evaluation is essential.
Review
Chemistry, Multidisciplinary
Muhammad Umair, Abubakar Sharafat, Dong-Eun Lee, Jongwon Seo
Summary: This study aims to investigate the cognitive load, task performance, and situational awareness of participants in virtual reality (VR) environment for building design review tasks. The findings show that participants have higher cognitive load and better performance in the immersive virtual environment.
APPLIED SCIENCES-BASEL
(2022)
Article
Construction & Building Technology
Xingyu Tao, Yuhan Liu, Peter Kok-Yiu Wong, Keyu Chen, Moumita Das, Jack C. P. Cheng
Summary: This paper proposes a confidentiality-minded framework (CMF) for blockchain-based design collaboration, which protects sensitive BIM data effectively by developing an access control model and design strategies.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Multidisciplinary Sciences
Tyler Ard, Michael S. Bienkowski, Sook-Lei Liew, Farshid Sepehrband, Lirong Yan, Arthur W. Toga
Summary: Scientific research is closely linked to digital information, but scientific publication is still limited to static text and figures, hindering effective communication of complex data. This article demonstrates how digital data can be integrated into the publication system through web-based and augmented reality technologies, providing a framework for the scientific community. Augmenting articles with data can modernize scientific communication by bridging the gap between digital research and printable articles.
Article
Construction & Building Technology
Yuanhang Jiao, Ping Cao
Summary: With the rapid advancement of China's construction industry informatization process, the integration of engineering design and construction is becoming increasingly important. This paper examines the problems related to project design management in China and introduces a form of BIM that combines design BIM and construction BIM to optimize the design management workflow.
Article
Construction & Building Technology
Michele Caroline Bueno Ferrari Caixeta, Marcio Minto Fabricio
Summary: This study presents a co-design tool integrated into the BIM platform to involve users in the design process of healthcare buildings, with the main contribution being the development of a physical-digital model that enables collaborative design with healthcare professionals and future data insertions in the BIM platform. The model was evaluated in a Controlled Experiment with members of the nursing team, achieving the research goals.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Computer Science, Information Systems
Zhibao Wang, Shuaijun Chen, Lu Bai, Juntao Gao, Jinhua Tao, Raymond R. Bond, Maurice D. Mulvenna
Summary: The high energy consumption and carbon emissions within data centers are significant contributors to global energy consumption and carbon emissions. The traditional cloud computing has reached a bottleneck due to its high energy consumption. A proposed framework, Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), aims to reduce energy consumption and carbon emissions in cloud data centers by leveraging the variations across geographically distributed centers through reinforcement learning. ERLFC uses the Actor-Critic algorithm to intelligently select data centers for task assignment based on factors such as energy consumption, cooling method, waiting time, energy type, emission ratio, and total energy consumption. Simulation results show that ERLFC effectively reduces energy consumption and emissions compared to other algorithms.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2023)
Article
Environmental Sciences
Pedro Martin-Lerones, David Olmedo, Ana Lopez-Vidal, Jaime Gomez-Garcia-Bermejo, Eduardo Zalama
Summary: This paper discusses the process of processing 3D point cloud data for use in BIM software to improve efficiency and accuracy in heritage asset management. Specific H-BIM specifications have been developed for heritage projects, and non-proprietary formats like IFC are used for information exchange.
Article
Computer Science, Theory & Methods
Su Hu, Yinhao Xiao
Summary: This research proposes a cloud computing task offloading algorithm based on dynamic multi-objective evolution, which efficiently reduces energy consumption and waiting time for tasks, achieving task allocation with minimum cost and shortest time. By dynamically adjusting parameters and trade-off coefficients, the algorithm effectively offloads cloud computing tasks.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Engineering, Industrial
Peter E. D. Love, Jane Matthews, Lavagnon A. Ika, Pauline Teo, Weili Fang, John Morrison
Summary: This paper suggests the need for construction organizations to establish an error management culture to reduce rework and support lean thinking. It emphasizes the importance of leadership, psychological safety, and coaching in cultivating a culture that accepts errors and focuses on mitigating their consequences. The contributions of this paper include a new theoretical foundation grounded in Quality-II and practical suggestions based on real experiences to monitor and anticipate rework in construction.
PRODUCTION PLANNING & CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Weili Fang, Yixiao Shao, Peter E. D. Love, Timo Hartmann, Wenli Liu
Summary: When monitoring safety levels in deep pit foundations using sensors, the extracted time series data often contain anomalies and noise, which hinders the assessment of risks. In this research, we propose a hybrid smart data approach that combines Extended Isolation Forest and Variational Mode Decomposition models to detect anomalies and de-noise the data effectively. Our approach is validated using real-life sensor data from a deep pit foundation project and achieves a root mean square error of 0.0389 and a signal-to-noise ratio of 24.09 for anomaly detection. Overall, our smart data approach enables improved decision-making and management of safety risks.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Construction & Building Technology
Peter E. D. Love, Jane Matthews
Summary: Despite extensive research, construction organizations still struggle to reduce rework. This paper presents a longitudinal study that examines rework causation in a transport megaproject using a sense-making lens. The study finds that rework often arises from hold-ups caused by misunderstandings, misinterpretations, role ambiguity, and breakdowns in communications and interactions between project participants. The research provides a nuanced understanding of errors and rework, offering a new theoretical framing for understanding rework causation in construction.
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
(2023)
Article
Engineering, Industrial
Derek H. T. Walker, Peter E. D. Love, Jane Matthews
Summary: Collaboration is crucial in program alliances, and the 'pain-gain share' regime serves as an incentive for teamwork and trust. However, the value and maintenance of dialogue for superior project outcomes in program alliances is not well understood. In this paper, we use a sense-making lens to explore the value of effective dialogue in two infrastructure projects. Our findings show that effective dialogue mitigated issues like rework, as participants shared a common purpose. This paper contributes by providing new value principles for dialogue in program alliances and empirical evidence for practitioners in a alliance delivery strategy.
PRODUCTION PLANNING & CONTROL
(2023)
Article
Engineering, Industrial
Wenli Liu, Ang Li, Weili Fang, Peter E. D. Love, Timo Hartmann, Hanbin Luo
Summary: This study proposes a hybrid data-driven model that effectively and accurately quantifies the risks of tunnel-induced ground settlement under uncertain parameters in complex geological conditions, by considering prior domain knowledge. The model incorporates a deep neural network for ground settlement prediction, physical knowledge, and a Markov-chain-based importance sampling for settlement reliability analysis. Evaluation using the San-yang Road tunnel project in Wuhan, China, demonstrates the model's ability to accurately predict tunnel-induced ground settlement and quantify failure probability for geotechnical reliability under uncertain parameters.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Construction & Building Technology
Peter E. D. Love, Lavagnon A. Ika, Jeff K. Pinto
Summary: Fast-and-frugal heuristics play an important role in decision-making, providing better outcomes than statistical models in uncertain and complex settings or with limited samples. However, they have not been adequately studied in the construction literature. This paper aims to raise awareness about the importance of fast-and-frugal heuristics and suggests the need for research to develop an adaptive toolbox of ecologically rational heuristics for decision-making in construction. The paper contributes by challenging reliance on statistical approaches under uncertainty and proposing the use of fast-and-frugal heuristics for decision-making.
DEVELOPMENTS IN THE BUILT ENVIRONMENT
(2023)
Article
Business
Peter E. D. Love, Lavagnon A. Ika, Jane Matthews, Weili Fang
Summary: This article explores the background and significance of understanding cost deviations in large-scale transport projects. By comparing two light rail transit systems and a road project, it examines the impact of procurement approaches and worldviews on project cost performance. The research highlights the uncertainty associated with cost assessment and calls for the use of standardized definitions and terminologies.
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
(2023)
Article
Engineering, Industrial
Peter E. D. Love, Lavagnon A. Ika, Jeffrey K. Pinto
Summary: This article introduces a method for estimating cost contingency in large-scale transport projects called smart heuristics. Smart heuristics are simple decision strategies that are superior to statistical reasoning when dealing with uncertainty. The article sets forth a research agenda for building and using smart heuristics and identifies methodological considerations for adapting and discovering new heuristics in the contingency estimation process.
PRODUCTION PLANNING & CONTROL
(2023)
Article
Engineering, Industrial
Jane Matthews, Peter E. D. Love, Stuart Porter, Weili Fang
Summary: This paper examines the challenges and experiences encountered in creating and maintaining a domain rework ontology in construction, using a real-life transport infrastructure mega-project as a case study. Through an explanatory case study approach and the lens of pragmatism, the paper aims to assess and manage rework risk pathways using an ontology to support an alliance's continuous improvement strategy. The main contribution of this paper is the dissemination of a knowledge repository that others can learn from when developing an ontology and addressing the common issue of rework in construction practice.
PRODUCTION PLANNING & CONTROL
(2023)
Article
Engineering, Industrial
Peter E. D. Love, Jane Matthews, Lavagnon A. Ika
Summary: This article explores how fast-and-frugal heuristics can effectively assess the uncertainty of rework in construction. By providing a theoretical framework and specific case studies, the research findings suggest that fast-and-frugal heuristics can be utilized for effective assessment in uncertain environments.
PRODUCTION PLANNING & CONTROL
(2023)
Article
Business
Regis Signor, Peter E. D. Love, Pablo Ballesteros-Perez
Summary: This paper analyzes the key criteria for developing a reference scenario for detecting bid rigging and presents a procedure for composing robust reference scenarios. Using data from Brazil and testing its generalizability in four countries and two auction formats, the research aims to assist public agencies in detecting and avoiding bid rigging.
CONSTRUCTION MANAGEMENT AND ECONOMICS
(2023)
Article
Engineering, Industrial
Wenli Liu, Fenghua Liu, Weili Fang, Peter E. D. Love
Summary: This paper addresses the issue of interpretability and transparency in machine learning models used for evaluating safety risks in tunnel construction. By utilizing the concept of 'eXplainable AI' (XAI) and causal discovery and reasoning, the authors develop a method to analyze and interpret geotechnical risks in tunnel construction. The proposed approach includes a sparse nonparametric and nonlinear directed acyclic diagram (DAG), a multiple linear regression model, and a probability-based reasoning model. The feasibility and effectiveness of the approach are validated through a case study on a tunnel project in Wuhan, China, showing accurate explanation of data-driven risk assessment results.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Construction & Building Technology
Jia Liang, Qipeng Zhang, Xingyu Gu
Summary: A lightweight PCSNet-based segmentation model is developed to address the issues of insufficient performance in feature extraction and boundary loss information. The introduction of generalized Dice loss improves prediction performance, and the visualization of class activation mapping enhances model interpretability.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Gilsu Jeong, Minhyuk Jung, Seongeun Park, Moonseo Park, Changbum Ryan Ahn
Summary: This study introduces a contextual audio-visual approach to recognize multi-equipment activities in tunnel construction sites, improving monitoring effectiveness. Tested against real-world operation data, the model achieved remarkable results, emphasizing the potential of contextual multimodal models in enhancing operational efficiency in complex construction sites.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Jin Wang, Zhigao Zeng, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Jianming Zhang, Lei Wang
Summary: This study presents a dual-path network for pavement crack segmentation, combining Convolutional Neural Network (CNN) and transformer. A lightweight CNN encoder is used for local feature extraction, while a novel transformer encoder integrates high-low frequency attention mechanism and efficient feedforward network for global feature extraction. Additionally, a complementary fusion module is introduced to aggregate intermediate features extracted from both encoders. Evaluation on three datasets confirms the superior performance of the proposed network.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Pierre Gilibert, Romain Mesnil, Olivier Baverel
Summary: This paper introduces a flexible method for crafting 2D assemblies adaptable to various geometric assumptions in the realm of sustainable construction. By utilizing digital fabrication technologies and optimization approaches, precise control over demountable buildings can be achieved, improving mechanical performance and sustainability.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Jorge Loy-Benitez, Myung Kyu Song, Yo-Hyun Choi, Je-Kyum Lee, Sean Seungwon Lee
Summary: This paper discusses the advancement of tunnel boring machines (TBM) through the application of artificial intelligence. It highlights the significance of AI-based management subsystems for automatic TBM operations and presents recent contributions in this field. The paper evaluates modeling, monitoring, and control subsystems and suggests research paths for integrating existing management subsystems into TBM automation.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Alireza Shamshiri, Kyeong Rok Ryu, June Young Park
Summary: This paper reviews the application of text mining and natural language processing in the construction field, highlighting the need for automation and minimizing manual tasks. The study identifies potential research opportunities in strengthening overlooked construction aspects, coupling diverse data formats, and leveraging pre-trained language models and reinforcement learning.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zhengyi Chen, Hao Wang, Keyu Chen, Changhao Song, Xiao Zhang, Boyu Wang, Jack C. P. Cheng
Summary: This study proposes an improved coverage path planning system that leverages building information modeling and robotic configurations to optimize coverage performance in indoor environments. Experimental validation shows the effectiveness and applicability of the system. Future research will focus on further enhancing coverage ratio and optimizing computation time.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Yonglin Fu, Junjie Chen, Weisheng Lu
Summary: This study presents a review of human-robot collaboration (HRC) in modular construction manufacturing (MCM), focusing on tasks, human roles, and interaction levels. The review found that HRC solutions are applicable to various MCM tasks, with a primary focus on timber component production. It also revealed the diverse collaborative roles humans can play and the varying levels of interaction with robots.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Qiong Liu, Shengbo Cheng, Chang Sun, Kailun Chen, Wengui Li, Vivian W. Y. Tam
Summary: This paper presents an approach to enhance the path-following capability of concrete printing by integrating steel cables into the printed mortar strips, and validates the feasibility and effectiveness of this approach through experiments.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Honghu Chu, Lu Deng, Huaqing Yuan, Lizhi Long, Jingjing Guo
Summary: The study proposes a method called Cascade CATransUNet for high-resolution crack image segmentation. This method combines the coordinate attention mechanism and self-cascaded design to accurately segment cracks. Through a customized feature extraction architecture and an optimized boundary loss function, the proposed method achieves impressive segmentation performance on HR images and demonstrates its practicality in UAV crack detection tasks.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Daniel Lamas, Andres Justo, Mario Soilan, Belen Riveiro
Summary: This paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Kahyun Jeon, Ghang Lee, Seongmin Yang, Yonghan Kim, Seungah Suh
Summary: This study proposes two enhanced unsupervised text classification methods for domain-specific non-English text. The results of the tests show that these methods achieve excellent performance on Korean building defect complaints, outperforming state-of-the-art zero-shot and few-shot text classification methods, with minimal data preparation effort and computing resources.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Yoonhwa Jung, Julia Hockenmaier, Mani Golparvar-Fard
Summary: This study introduces a transformer-based natural language processing model, UNIfORMATBRIDGE, that automatically labels activities in a project schedule with Uniformat classification. Experimental results show that the model performs well in matching unstructured schedule data to Uniformat classifications. Additionally, the study highlights the importance of this method in developing new techniques.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
De-Graft Joe Opoku, Srinath Perera, Robert Osei-Kyei, Maria Rashidi, Keivan Bamdad, Tosin Famakinwa
Summary: This paper introduces a digital twin technology combining Building Information Modelling and the Internet of Things for the construction industry, aiming to optimize building conditions. The technology is implemented in a university library, successfully achieving real-time data capture and visual representation of internal conditions.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zaolin Pan, Yantao Yu
Summary: The construction industry faces safety and workforce shortages globally, and worker-robot collaboration is seen as a solution. However, robots face challenges in recognizing worker intentions in construction. This study tackles these challenges by proposing a fusion method and investigating the best granularity for recognizing worker intentions. The results show that the proposed method can recognize multi-granular worker intentions effectively, contributing to seamless worker-robot collaboration in construction.
AUTOMATION IN CONSTRUCTION
(2024)
