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Construction sites have shown the highest incidence of safety accidents across industries in recent times. Small-scale sites, in particular, often operate without on-site safety managers, leading to significant safety oversights. In this study, we developed a method of identifying risk areas during construction procedures by using bird's-eye-view image data throughout the construction cycle. Actual construction site images were collected and specific target objects were selected to create an AI training dataset. The segmentation model's performance was validated, and a system was developed to identify fall risk areas by establishing interconnections between these target objects within the images. The findings of this study can help enhance compliance assessment with construction procedures and improve safety management oversight at small-scale construction sites. [ABSTRACT FROM AUTHOR]

Construction in cities and agglomerations is one of the main sources of air pollution in most countries in the world. Fine dust particles, PM0.5–PM10, which form as a result of construction processes, are among the most dangerous pollutants. With the increase in the volume of point-pattern housing development in cities, the task of maintaining clean air and environmental conditions becomes important. This requires research, the monitoring of dust emissions throughout the entire construction period and the development of design solutions based on the results obtained. The study examines the determination of the dispersed composition of dust generated on a construction site. A graphical representation of the dispersed composition is given by constructing integral curves on a logarithmic grid and approximating them using two-link and three-link splines. The gravimetric measurement method was used to analyze the concentration of dust in the air released during construction work near residential areas. Dust analysis at the construction site revealed significant differences in particle size that cannot be explained by statistical errors alone. The reasons for this are both working conditions and climatic factors, including humidity and wind intensity. In this regard, it is preferable to use models that take into account random processes instead of traditional deterministic methods to study the dust that shapes during construction. [ABSTRACT FROM AUTHOR]

With the growing trend of urbanisation and the growing number of people migrating to cities, the demand for the development and construction of new buildings and infrastructure has risen, meaning that the construction industry must adapt to these trends. Growing demands with shorter deadlines for an industry already known for its high costs and late delivery means that productivity must be increased without increasing costs. The solution for this might lie in the application of the Lean philosophy to the construction industry. This paper analyses the application of the Lean philosophy in order to increase the productivity of construction work for an airport project. This paper highlights the potential for enhancing productivity in construction workplaces by concurrently fostering continuous improvement and sustainability through the implementation of the A3 methodology and Lean principles, resulting in waste reduction and increased value. [ABSTRACT FROM AUTHOR]

Tracking the postures of construction workers can provide precious information for safety management, occupational illness prevention, and productivity investigation. However, the posture data of construction workers is rarely utilized due to a lack of appropriate methods to track it. This research proposes a real-time multiworker posture tracking (MWPT) method to accurately track the postures of multiple workers onsite from video streams. It consists of three elements: image enhancement to adapt varying light conditions, posture detection for obtaining workers' postures, and matching for tracking and retracking postures. In the field experiment, MWPT performed satisfactorily with an average of two ID switches (IDS), an average frame per second (FPS) of 11.0, and an average precision (AP@50) of 86.33. The results prove the capability of MWPT for tracking multiworker postures in real construction environments with high robustness and effectiveness. This research not only contributes an innovative tracking algorithm but also lays a stepping stone toward further worker posture-related research. Practical Applications: This research introduces a posture tracking method for multiple construction workers. The motivation behind this research stems from the absence of an effective tracking method tailored to the demanding conditions of construction sites, such as variable lighting, extensive occlusions, and the challenge of distinguishing workers with similar appearances. The proposed solution leverages posture similarity for tracking workers and incorporates a retracking mechanism to address visual occlusions. The contrast limited adaptive histogram equalization method is employed to recover high-contrast visual features from dimly lit images, effectively addressing the issue of insufficient lighting. This method has been validated as both effective and efficient through customized test videos from actual construction sites. It shows promise for use in unsafe behavior detection, occupational disease prevention, and other posture-related research or applications as an effective tool. [ABSTRACT FROM AUTHOR]

This study intends to explore the available green site management practices for construction stakeholders. Green site management practices have been introduced to the construction industry to mitigate the negative impacts of construction activities if construction stakeholders start to implement them. Therefore, green site management practices such as land control, construction site waste management, dust, noise, and vibration control, and a green and conducive environment shall be explored to promote green development in the construction industry. A qualitative method was employed since this study focused on the views of construction stakeholders on green site management practices. Five contractors were randomly selected from the construction sites in Malaysia based on the convenience sampling method. All respondents were qualified to give opinions as they hold management posts. The process collecting of opinions from the respondents stopped when no new issues were found in their feedback. The results reveal that land control, construction site waste management, dust, noise, and vibration control, and a green and conducive environment were considered green development in the construction industry. However, these practices were not systematically practised as there were no clear guidelines to lead the practitioners. The results also showed that the government has the power to enforce green practices on construction sites. Green site management practices will bring another new era to the construction industry, which is intended to protect the environment while ensuring that the practitioners can be financially sustainable. The results will shed light on green construction development, whereby these green practices can be promoted to construction sites. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the rapidly advancing field of construction, digital site management and Building Information Modeling (BIM) are pivotal. This study explores the integration of drone imagery into the digital construction site management process, aiming to create BIM models with enhanced object recognition capabilities. Initially, the research sought to achieve photorealistic rendering of point cloud models (PCMs) using blur/sharpen filters and generative adversarial network (GAN) models. However, these techniques did not fully meet the desired outcomes for photorealistic rendering. The research then shifted to investigating additional methods, such as fine-tuning object recognition algorithms with real-world datasets, to improve object recognition accuracy. The study's findings present a nuanced understanding of the limitations and potential pathways for achieving photorealistic rendering in PCM, underscoring the complexity of the task and laying the groundwork for future innovations in this area. Although the study faced challenges in attaining the original goal of photorealistic rendering for object detection, it contributes valuable insights that may inform future research and technological development in digital construction site management. [ABSTRACT FROM AUTHOR]

Addressing the inherent hazards of on-site construction work and stagnant labor productivity is crucial in the construction industry. To tackle these challenges, automated monitoring of construction sites and analysis of workers' actions play a pivotal role. In this study, we developed a method for classifying actions at a construction site from video, using deep learning. Specifically, we used two image processing techniques, pose assessment and object detection, and found that the accuracy of action classification was improved by extracting information on the proximity of workers to equipment installed at the construction site, and also by considering the pose information. For classification, LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), and XGBoost models were used, and the presence of proximity information improved average recall by 7.0% to 8.5% for all models used. The final model was developed as an ensemble of these methods, offering accuracy and average recall that are higher than with conventional methods. The methodology developed in this research enables quantification and visualization of work content at construction sites, contributing to the overall enhancement of safety and productivity within the construction industry. [ABSTRACT FROM AUTHOR]

The integration of computer vision technology into construction sites poses various challenges due to the complex environment. Prior studies on computer vision related to heavy construction equipment has primarily focused on a limited range of equipment types provided in standard databases, such as the Microsoft Common Objects in Context (MS COCO) data set. The conventional approach has limitations in capturing the diverse working conditions and dynamic environments encountered in real construction sites. To overcome the challenge, this study proposes a new benchmark model for the automated detection and classification of a wide range of heavy construction equipment (i.e., nine representative types) commonly used in construction sites by using a deep convolution neural network. This study was conducted in four steps: (1) data collection and preparation, (2) data transformation, (3) model training, and (4) model validation. The proposed you only look once (YOLO)v5l (large, YOLOv5 with a larger network) model demonstrated high reliability, achieving a mean average precision (mAP)_0.5∶0.95 of 90.26%. This study makes a significant contribution to the domain of construction engineering and management by providing a more efficient and systematic management system to proactively prevent heavy equipment–related safety accidents with diverse working conditions and dynamic environments encountered at construction sites. Moreover, the proposed approach can be extended to integrate advanced techniques such as case-based reasoning, digital twin, and blockchain, allowing for the automated activity recognition in various occlusions, the carbon emissions monitoring and diagnostics of heavy equipment, and a robust real-time construction management system with enhanced security. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Visual inspection of the workplace and timely reminders of unsafe behaviors (e.g, not wearing a helmet) are particularly significant for avoiding injuries to workers on the construction site. Video surveillance systems generate large amounts of non-structure image data on site for this purpose; however, they require real-time recognition automation solutions based on computer vision. Although various deep-learning-based models have recently provided new ideas for identifying helmets in traffic monitoring, few solutions suitable for industry applications have been discussed due to the complex scenarios of construction sites. In this paper, a fast and robust network based on a mutilscale Swin Transformer is proposed for safety helmet detection (FRSHNet) at construction sites, which contains the following contributions. Firstly, MAE-NAS with the variant of MobileNetV3's MobBlock as a basic block is applied to implement feature extraction. Simultaneously, a multiscale Swin Transformer module is utilized to obtain the spatial and contexture relationships in the multiscale features. Subsequently, in order to meet the scheme requirements of real-time helmet detection, efficient RepGFPN are adopted to integrate refined multiscale features to form a pyramid structure. Extensive experiments were conducted on the publicly available Pictor-v3 and SHWD datasets. The experimental results show that FRSHNet consistently provided a favorable performance, outperforming the existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the influence of sand quality, water-to-cement ratio, binder properties, mix design methods, and mixing techniques on the fresh and hardened properties of concrete. The physicochemical characteristics of coarse aggregates, sands, and binders were analyzed. The experimental results show that the binders and coarse aggregates met standard specifications. However, none of the sands meet construction standards. Corrections were necessary for the dune sands to meet construction standards in terms of grain size distribution and fineness modulus. The results also show that the concretes formulated using the Dreux-Gorisse method exhibited higher quality than the locally formulated concretes. Furthermore, it was found that hand mixing resulted in inadequate mixing, material wastage, lower strength, and increased porosity, whereas machine mixing produced concretes with a more homogeneous microstructure, uniform particle distribution, lower porosity, and higher strength. The batch variability and compressive strength of the hand-mixed concretes were also found to be influenced by the expertise level of the batch mixer and the number of successive hand batches. It was also found that both the soluble silica and the inert methods are reliable for determining binder content in machine-mixed concrete. However, the soluble silica method occasionally exhibited significant variations in hand-mixed concrete compared to the inert method. A combined approach utilizing the average of both methods enhances the overall reliability of the binder content values. Observations on construction sites revealed widespread deviations from recommended guidelines. Issues such as lack of material inspection, proper stockpiling, ingredient contamination, and inadequate batch mixing contributed to variations in concrete workability, porosity, and compressive strength. [ABSTRACT FROM AUTHOR]

Environmental problems including the depletion of natural resources and energy have drawn a lot of attention from all sectors of society in the context of high-quality global development, and solid waste generated by the construction industry accounts for 36% of the total amount of municipal waste. The generation of large amounts of construction waste not only causes a waste of resources, but also causes great damage to the environment. Reducing the quantity of solid waste produced during a building's new construction period can be greatly aided by construction site solid waste statistics and forecasts. Based on the statistical data of 61 public construction projects in Hainan Province, China, this study uses the Random Forest algorithm to rank the importance of possible factors affecting the amount of solid waste generated, and linearly fits the data to achieve the prediction of solid waste at construction sites. The findings indicate that building area, building height, concrete usage, steel usage and assembly rate are the main factors affecting solid waste in construction sites. In office buildings and exhibition buildings, an increase in ground area, building height, concrete usage, and steel usage increases the generation of each type of solid waste (inorganic non-metallic solid waste, metallic solid waste), with the exception of an increase in concrete usage, which results in a decrease in the generation of metallic solid waste. Furthermore, a higher assembly rate can substantially lower the production of all waste types. These results offer a theoretical foundation for the implementation of assembly construction to support the high-quality development of the construction industry, as well as partial design inspiration for the architectural design stage. [ABSTRACT FROM AUTHOR]

This study intends to explore the available green site management practices for construction stakeholders. Green site management practices have been introduced to the construction industry to mitigate the negative impacts of construction activities if construction stakeholders start to implement them. Therefore, green site management practices such as land control, construction site waste management, dust, noise, and vibration control, and a green and conducive environment shall be explored to promote green development in the construction industry. A qualitative method was employed since this study focused on the views of construction stakeholders on green site management practices. Five contractors were randomly selected from the construction sites in Malaysia based on the convenience sampling method. All respondents were qualified to give opinions as they hold management posts. The process collecting of opinions from the respondents stopped when no new issues were found in their feedback. The results reveal that land control, construction site waste management, dust, noise, and vibration control, and a green and conducive environment were considered green development in the construction industry. However, these practices were not systematically practised as there were no clear guidelines to lead the practitioners. The results also showed that the government has the power to enforce green practices on construction sites. Green site management practices will bring another new era to the construction industry, which is intended to protect the environment while ensuring that the practitioners can be financially sustainable. The results will shed light on green construction development, whereby these green practices can be promoted to construction sites.

Construction workers’ behaviors directly affects labor productivity and their own safety, thereby influencing project quality. Recognizing and monitoring the construction-related behaviors is therefore crucial for high-quality management and orderly construction site operation. Recent strides in computer vision technology suggest its potential to replace traditional manual supervision approaches. This paper explores research on monitoring construction workers’ behaviors using computer vision. Through bibliometrics and content-based analysis, the authors present the latest research in this area from three perspectives: “Detection, Localization, and Tracking for Construction Workers,” “Recognition of Workers’ Construction Activities,” and “Occupational Health and Safety Behavior Monitoring.” In terms of the literature’s volume, there has been a notable increase in this field. Notably, the focus on safety-related literature is predominant, underscoring the concern for occupational health. Vision algorithms have witnessed an increase in the utilization of object detection. The ongoing and future research trajectory is anticipated to involve multi-algorithm integration and an emphasis on enhancing robustness. Then the authors summarize the review from engineering impact and technical suitability, and analyze the limitations of current research from the perspectives of technical approaches and application scenarios. Finally, it discusses future research directions in this field together with generative AI models. Furthermore, the authors hope this paper can serves as a valuable reference for both scholars and engineers.

Construction in cities and agglomerations is one of the main sources of air pollution in most countries in the world. Fine dust particles, PM0.5–PM10, which form as a result of construction processes, are among the most dangerous pollutants. With the increase in the volume of point-pattern housing development in cities, the task of maintaining clean air and environmental conditions becomes important. This requires research, the monitoring of dust emissions throughout the entire construction period and the development of design solutions based on the results obtained. The study examines the determination of the dispersed composition of dust generated on a construction site. A graphical representation of the dispersed composition is given by constructing integral curves on a logarithmic grid and approximating them using two-link and three-link splines. The gravimetric measurement method was used to analyze the concentration of dust in the air released during construction work near residential areas. Dust analysis at the construction site revealed significant differences in particle size that cannot be explained by statistical errors alone. The reasons for this are both working conditions and climatic factors, including humidity and wind intensity. In this regard, it is preferable to use models that take into account random processes instead of traditional deterministic methods to study the dust that shapes during construction.

• Metrology and technical description of the voussoirs of the transverse arch of the nave. • Comparative analysis of tool marks in the cathedral. • Dating by comparison with Early and High Gothic buildings. The fire of April 15, 2019 at the cathedral of Notre-Dame de Paris offered archaeologists and art historians a unique opportunity to work closely with an emblematic building, a World Heritage Site. The recovery of most of the voussoirs from the collapsed transverse arch of the nave has made it possible to study the operating chain of an essential element of the cathedral, from the choice of material to its implementation, including the techniques used by the stonemason or the knowledge of the joiner. This technical analysis has made it possible to establish comparisons with the other elevations of the cathedral, and also with other Early and High Gothic buildings, allowing us to offer a new proposal for the development of knowledge and the overall chronology of construction. [ABSTRACT FROM AUTHOR]

Resident information and feedback system for construction sites In terms of the growing world population and the increasing degree of urbanization, many building construction sites in urban areas are to be expected in the future. Due to their impact on the surrounding area, construction sites are primarily perceived as a nuisance by the public and the surrounding neighborhood. This can negatively impact the acceptance of construction projects and affect internal construction site operations. To address these challenges, a digital resident information and feedback system for construction sites was developed as part of a research study. The usage requirements and perspectives "construction management", "project development", "project management", "construction communication" and "neighborhoods" were empirically investigated and transferred into a participatory software concept. This article examines the challenges and potentials, presents a design variation for a digital system, and provides recommendations for action for the successful use of participation tools on construction sites. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ingeniería e Investigación is the property of Universidad Nacional de Colombia, Facultad de Ingenieraia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to investigate the impacts of green site management on corporate environmental and economic performances by improving the efficiency of energy and water consumption at construction sites. The identified green site management has been proven to improve both environmental and economic performances. This study allows practitioners to study and advocate for appropriate green site management strategies. A qualitative method was carried out since this research focused on the construction players' opinions on green site management practices. Five contractors were selected randomly from the construction sites in Malaysia based on the convenience sampling method. All selected respondents were qualified to give opinions as they held management posts. The results have shown green site management practices can reduce the consumption of resources and improve energy efficiency management and water consumption management, which are considered to have a positive impact on the construction project's environmental and economic performance. The findings also revealed that green site management practices implementation must not affect their economic performance, such as increasing the construction cost with high initial and maintenance costs. As the construction industry consumes a vast amount of water and energy daily, these consumption reductions can help construction sites achieve energy efficiency and utilize natural resources, eventually improving corporate environmental and economic performance. Thus, green practices must be implemented to bring sustainable development to the construction industry. [ABSTRACT FROM AUTHOR]

Construction workers are exposed to heat stress risks due to the combined effects of hot and humid weather conditions (HHWCs) and physically demanding work. A real-time activity intensity identification (AII) is required to measure the impact of HHWCs using a nonintrusive approach. This research developed a real-time AII system based on computer vision analysis (CVA). It then combined the CVA system with real-time video recordings to approximate workers' activity intensity (AI) levels alongside HHWC records. A fundamental activities matrix was developed to build a list of measurable and identifiable features of site activities. These features were used to identify and link different postures to a crew's AI and safety status within a given context. In real-site conditions, the AII system instantly and unobtrusively approximated workers' AI and safety status under HHWCs. The system showed high detection performance with competitive deployment time, cost, and effort, outperforming previous related models. The results showed that formwork and steelwork are mostly moderate activities; however, moderate AI and HHWCs can create heat stress and fatigue and significantly affect workers' safety, resulting in heat-related injuries and accidents. This research gives researchers and practitioners insight into the challenges associated with measurement methods and solving practical site measurement issues. This research promotes innovative methods for real-site measurements and contributes to knowledge in the field of safety and productivity in the construction industry by employing new, innovative CVA technology. This technology has applications in the industry by deploying a practical tool that could support aligned improvement in the safety and productivity of construction workers working under HHWCs. The challenges of automated and real-time measurements have always been of great interest to construction safety and productivity practitioners, particularly measurements of nonintrusive systems and competitive deployment time, cost, and effort. Such problems have also resulted in a substantial delay in making safety- and productivity-related decisions, which are major reasons for the increasing number of hot and humid weather–related injuries and incidents, particularly with the growing threat of global warming. Furthermore, under HHWCs, construction companies have also incurred significant productivity losses. This study offers an automated, nonintrusive, real-time measuring system with competitive deployment time, cost, and effort to monitor activity intensity and weather-related risks. Hence, site decision makers can make timely safety- and productivity-related decisions to improve work safety and productivity. [ABSTRACT FROM AUTHOR]

Operational and occupational safety when using automated manufacturing processes on construction sites The use of automated manufacturing processes on construction sites is based on the principles of industrial manufacturing with industrial robots. As a result of the construction‐specific features, established standards with regard to operational safety and occupational safety can only be applied to a limited extent, although there is an increased potential risk from independently operating construction machines, especially on construction sites. In this context, this essay compiles the most important legal and normative framework conditions in addition to specific basics and the status quo of automated production on construction sites and draws conclusions about the responsibilities and the implementation of protective measures for robotic applications. [ABSTRACT FROM AUTHOR]

This article examines the potential of brownfields to provide developers with construction sites for investment and building projects. Some Bulgarian and international publications on the topic are reviewed. Several key indicators for the housing market in Sofia (Izgrev district) are analyzed. A specific example of a new residential complex built on a brownfield is examined. The study draws conclusions about the potential of brownfields to satisfy growing demand for construction sites and positives/negatives that such projects provide to the market, investors, community, and authorities. Recommendations are made for stimulating investments in similar projects. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The building industry faces a number of prominent challenges in the coming period. In this article, we focus on productivity in construction, which lags behind other industries despite technological developments. There is an urgent need for more efficient production methods. In other words, the potential for increasing productivity in construction is enormous. As in other industries, the key to this lies in process orientation, process standardization, and digitization. Lean construction approaches offer innovative solutions here by aiming to maximize customer value while minimizing waste, applying the principles of lean production to construction processes. However, building products are distinct in nature. Efforts to standardize them have achieved partial success, but only within specific product categories and for certain customer needs. Most construction activities remain highly unique. An alternative solution lies in standardizing work processes and not the final product. By adopting this method, one can considerably decrease individuality in production without compromising the essential uniqueness of the building product. Consequently, it is crucial to gain a deeper understanding of the standardization of production processes and the dynamics within the construction sector. This article introduces a modular construction toolkit designed to standardize production processes at construction sites. This toolkit consists of a series of consistent process steps, each linked to a standard time metric. Using this classification, a production model is constructed from a select number of recurring processes, leading to an ontological representation of production. This modular approach allows diverse production processes to be compared based on productivity, as they are composed of consistent and comparable sub-processes. Such a comparison is crucial for continuous production optimization. This method also enables the pinpointing of the most wasteful processes across various construction sites. While the primary data generation use case is centred on special civil engineering (special foundation engineering), the core concepts can be applied to general building construction. [ABSTRACT FROM AUTHOR]

With the growing trend of urbanisation and the growing number of people migrating to cities, the demand for the development and construction of new buildings and infrastructure has risen, meaning that the construction industry must adapt to these trends. Growing demands with shorter deadlines for an industry already known for its high costs and late delivery means that productivity must be increased without increasing costs. The solution for this might lie in the application of the Lean philosophy to the construction industry. This paper analyses the application of the Lean philosophy in order to increase the productivity of construction work for an airport project. This paper highlights the potential for enhancing productivity in construction workplaces by concurrently fostering continuous improvement and sustainability through the implementation of the A3 methodology and Lean principles, resulting in waste reduction and increased value.