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"Construction industry Management Data processing."
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Managing construction logistics
Every major industry except construction uses logistics to improve its bottom line…
Poor logistics is costing the construction industry at least £3 billion a year according to a report – ‘Improving Construction Logistics’ – published by the Strategic Forum for Construction. Additional costs arise as a result of operatives waiting for materials, and skilled craftsmen being used for unskilled jobs. Inadequate management of logistics also has an adverse effect on quality, causes delays to projects, and adds to the health and safety risks on site. This practical book highlights the benefits of good logistics as well as the use of consolidation centres on projects. It shows how reduction in transport movements, less money tied up in stock, less waste, and the more efficient use of skilled craftsmen will reduce the cost of projects, reduce construction time, improve quality, reduce risks to health and safety, improve environmental performance and generally improve the image of the industry. The authors offer practical ways of achieving these benefits through integrated project teams and supply chains and the increased adoption of information technology including electronic communications, bar coding, and electronic tagging for tracing products. They also show how specific roles for each part of the industry can help to improve logistics.
• Practical, clear and accessible
• First book to address logistics in construction
• Written by the industry-recognized logistics experts
• Tackles issues of key concern: efficient use of labour; sustainability; waste and supply chain management
eBook
Cyber physical system for safety management in smart construction site
PurposeConstruction safety has been a long-term problem in the development of the construction industry. An increasing number of smart construction sites have been designed using different techniques to reduce injuries caused by construction accidents and achieve proactive risk control. However, comprehensive smart construction site safety management solutions and applications have yet to be developed. Thus, this study proposes a smart construction site framework for safety management.Design/methodology/approachA safety management system based on a cyber-physical system is proposed. The system establishes risk data synchronization mapping between the virtual construction and physical construction sites through scene reconstruction design, data awareness, data communication and data processing modules. Personnel, mechanical and other risks on site will be warned and controlled.FindingsThe results of the case study have proved the management benefits of the system. On-site workers gradually realized that they should enter the construction site based on the standard process. And the number of people close to the construction hazard areas decreased.Research limitations/implicationsThere are some limitations in the technology of smart construction site. The modeling speed can be faster, the data collection can be timelier, and the identification of unsafe behavior can be integrated into the system. Construction quality and efficiency issues in a virtual construction site will also be solved in further research.Practical implicationsIn this paper, this system is actually applied in the mega project management process. More practical projects can use the management ideas and method of this paper to ensure on-site safety.Originality/valueThis study is among the first attempts to build a complete smart construction site based on CPS and apply it in practice. Personnel, mechanical, components, environment information will be displayed on the virtual construction site. It will greatly promote the development of the intellectualized construction industry in the future.
Journal Article
Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment
Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.
Journal Article
Research and Application of YOLOv11-Based Object Segmentation in Intelligent Recognition at Construction Sites
With the increasing complexity of construction site environments, robust object detection and segmentation technologies are essential for enhancing intelligent monitoring and ensuring safety. This study investigates the application of YOLOv11-Seg, an advanced target segmentation technology, for intelligent recognition on construction sites. The research focuses on improving the detection and segmentation of 13 object categories, including excavators, bulldozers, cranes, workers, and other equipment. The methodology involves preparing a high-quality dataset through cleaning, annotation, and augmentation, followed by training the YOLOv11-Seg model over 351 epochs. The loss function analysis indicates stable convergence, demonstrating the model’s effective learning capabilities. The evaluation results show an mAP@0.5 average of 0.808, F1 Score(B) of 0.8212, and F1 Score(M) of 0.8382, with 81.56% of test samples achieving confidence scores above 90%. The model performs effectively in static scenarios, such as equipment detection in Xiong’an New District, and dynamic scenarios, including real-time monitoring of workers and vehicles, maintaining stable performance even at 1080P resolution. Furthermore, it demonstrates robustness under challenging conditions, including nighttime, non-construction scenes, and incomplete images. The study concludes that YOLOv11-Seg exhibits strong generalization capability and practical utility, providing a reliable foundation for enhancing safety and intelligent monitoring at construction sites. Future work may integrate edge computing and UAV technologies to support the digital transformation of construction management.
Journal Article
BIM in facilities management applications: a case study of a large university complex
Purpose – Building information modelling (BIM) in facilities management (FM) applications is an emerging area of research based on the theoretical proposition that BIM information, generated and captured during the lifecycle of a facility, can improve its management. Using this proposition as a starting point, the purpose of this paper is to investigate the value of BIM and the challenges affecting its adoption in FM applications. Design/methodology/approach – Two inter-related research methods are utilised. The literature is utilised to identify the application areas, value and challenges of BIM in FM. Due to the lack of case studies identified in the literature review, and to provide empirical evidence of the value and challenges of BIM in FM, a case study of Northumbria University’s city campus, is used to empirically explore the value and challenges of BIM in FM. Findings – The results demonstrated that BIM value in FM stems from improvement to current manual processes of information handover; improvement to the accuracy of FM data, improvement to the accessibility of FM data and efficiency increase in work order execution. The main challenges were the lack of methodologies that demonstrate the tangible benefits of BIM in FM, the limited knowledge of implementation requirement including BIM for FM modelling requirements, the interoperability between BIM and FM technologies, the presence of disparate operational systems managing the same building and finally, the shortage of BIM skills in the FM industry. Originality/value – There is lack of real-life cases on BIM in FM especially for existing assets despite new constructions representing only 1-2 per cent of the total building stock in a typical year. The originality of this paper stems from both adding a real-life case study of BIM in FM and providing empirical evidence of both the value and challenges of BIM in FM applications.
Journal Article
Digitalisation for nuclear waste management: predisposal and disposal
Data science (digitalisation and artificial intelligence) became more than an important facilitator for many domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly usable for applications related to the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilising the best models, input data and monitoring information to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DTw) able to mirror or predict the performance of its corresponding physical twins. Therefore, the main target of the Topical Collection is exploring how the development of DTw can benefit the development of safe, efficient solutions for the pre-disposal and disposal of radioactive waste. A particular challenge for DTw in radioactive waste management is the combination of concepts from geological modelling and underground construction which will be addressed by linking structural and multi-physics/chemistry process models to building or tunnel information models. As for technical systems, engineered structures a variety of DTw approaches already exist, the development of DTw concepts for geological systems poses a particular challenge when taking the complexities (structures and processes) and uncertainties at extremely varying time and spatial scales of subsurface environments into account.
Journal Article
Highway Construction Safety Analysis Using Large Language Models
The highway construction industry carries substantial safety risks for workers, necessitating thorough accident analyses to implement effective preventive measures. Current research lacks comprehensive investigations into safety incidents, relying heavily on conventional statistical methods and overlooking valuable textual information in publicly available databases. This study leverages a state-of-the-art large language model (LLM), specifically OpenAI’s GPT-3.5 model. The primary focus is to enhance text-based incident analysis that is sourced from OSHA’s Severe Injury Reports (SIR) database. By incorporating novel natural language processing (NLP) techniques, dimensionality reduction, clustering algorithms, and LLM prompting of incident narratives, the study aims to develop an approach to the analysis of major accident causes in highway construction. The resulting cluster analysis, coupled with LLM summarization and cause identification, reveals the major accident types, such as heat-related and struck-by injuries, as well as commonalities between incidents. This research showcases the potential of artificial intelligence (AI) and LLM technology in data-driven analysis. By efficiently processing textual data and providing insightful analysis, the study fosters practical implications for safety professionals and the development of more effective accident prevention and intervention strategies within the industry.
Journal Article
Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis
Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets.
Journal Article