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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]

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]

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]

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.

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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]

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.

This paper focuses on literature analyses of the impact of granite powder waste on the properties of cementitious mixes and composites. The influence of weather conditions on the properties of granite rocks is also analysed. The grains of granite powder waste are characterized. It was noticed that the results of the literature do not answer the question of whether granite powder has a positive or negative effect on cementitious mixes. Typically, granite powder allows for increasing the mechanical and functional properties of composites. The article also describes the literature gaps related to the topic and indicates future research perspectives on the functionalization of granite powder waste. The author discusses correlations and the impact of granite powder on the properties of cementitious composites and compares it with the literature. This paper compares the results of different authors using standard testing procedures but with the application of different modifications of compositions of cementitious mixes. The possibility of reducing the production costs and CO2 emissions of cement composites with the use of granite powder waste was indicated. An increase in packing density connected with using of granite powder as an admixture in cementitious systems is described. Recommendations related to the use of granite powder in cementitious composites are listed.

Introduction/purpose: The construction industry is one of the main producers of dust, greenhouse gases and air pollutants. Effective operation and management of construction site operations can significantly reduce projects' carbon footprints and other environmental impacts. Through the cooperation of a scientific and research institution and a construction company, the real-time monitoring of air quality at a construction site was implemented using IoT technologies. Methods: An IoT-based system framework that integrates a distributed sensor network to collect real-time data and demonstrates air quality at a construction site was implemented. Different types of sensors were used to collect data related to NO2, PM2.5, and PM10 particles, as well as meteorological parameters – wind speed and direction, humidity, pressure, and temperature. Results: The results of real-time measurements provide a picture of the state of air pollution at the construction site and the connection with construction activities that can be managed in order to reduce the concentration of polluting gases and suspended particles. Through on-site monitoring of a construction site in Belgrade City, this study found that the dust level due to construction activities is relatively high. Conclusion: It can be concluded that the construction activity had a significant impact on the air quality in the construction surrounding areas. Regarding the main factors affecting the building construction dust emission, the correlations show that building construction dust emission was not significantly correlated with meteorological factors.

Activities on construction sites inevitably generate large amounts of dust, which can pose a significant threat to air quality and public health. Detection of construction dust has received increasing attention. In this study, we proposed an object detection method for visible dust and relevant prevention measures on construction sites based on YOLOv7 detector. Deformable Convolutional Networks and Wise-IoU were introduced to improve the precision of the detector in detecting non-rigid objects, such as visible dust. We constructed a publicly available dataset containing 7,500 realistic images covering various construction site conditions. The results indicate that the improved detector achieved high precision with mean average precision of 68.1% and detection speed of 80.6 frames per second. The proposed method enables real-time and effective detection of visible dust and relevant prevention measures on construction sites, which will help advance the research of dust detection and prevention in open space environments.

High-rise building machines (HBMs) play a crucial role in the construction of super-tall buildings, with their working states directly impacting safety, quality, and progress. Given their extensive floor coverage and complex internal structures, monitoring priorities should shift according to specific workflows. However, existing research has primarily focused on monitoring key HBM components during specific stages, neglecting the automated recognition of HBM workflows, which hinders adaptive monitoring strategies. This study investigates the critical states of HBM construction across various structural layers and proposes a method rooted in vibration signal analysis to determine the HBM's working state. The method involves collecting vibration signals with a triaxial accelerometer, extracting five distinct vibration signal features, classifying these signals using a k-Nearest Neighbors (kNN) classifier, and finally, outputting the results through a classification rule that aligns with the actual workflow of the HBM. The method was implemented in super-high-rise buildings exceeding 350 m, achieving a measured accuracy of 97.4% in HBM working state recognition. This demonstrates its proficiency in accurately determining the construction state and facilitating timely feedback. Utilizing vibration signal analysis can enhance the efficiency and safety, with potential applications in monitoring large-scale formwork equipment construction processes. This approach provides a versatile solution for a wide range of climbing equipment used in the construction of super-tall buildings and towering structures. [ABSTRACT FROM AUTHOR]

Background: This study investigates the potential of digital construction to enhance the planning competence of project managers in dealing with the complexities of mega construction projects. Traditional project strategies often struggle to adapt in dynamic situations, particularly evident in mega construction endeavours. Drawing inspiration from successful digital strategies in manufacturing, this research proposes that adopting digital techniques could bolster project managers' ability to navigate complexity during construction, leading to improved infrastructure delivery within budget and on schedule. Methods: Employing a quantitative approach, this study utilized an online questionnaire to gather insights from project managers. The proposed hypothesis was assessed using a one-sample t-test. Additionally, Pearson's correlation coefficient was employed to gauge the strength of the relationship between various constructs. This approach aimed to determine the extent to which digital construction can support effective complexity management during mega construction projects. Results: The results indicate that digital construction equips project managers with enhanced capabilities to efficiently coordinate and allocate resources in real-time within complex construction environments, thereby optimizing overall project performance. Despite these advantages, the findings also reveal that managers continue to encounter challenges overseeing numerous participants during infrastructure construction. This suggests that while digital construction contributes to improved planning against complexity, addressing the management of multiple stakeholders remains an ongoing challenge. Conclusions: This study presents a novel contribution to the construction industry by demonstrating the potential of synergizing various digital tools throughout construction processes to empower project managers in effectively addressing the complexities inherent in mega construction planning. Furthermore, it underscores how digital construction confers a dynamic advantage for project managers in navigating complexities and enhancing overall project performance. [ABSTRACT FROM AUTHOR]

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers' unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites. [ABSTRACT FROM AUTHOR]

To ensure the safety of construction site personnel and to improve the efficiency of emergency safety evacuation of site personnel, this study analyzes the risk reasons for fire accidents and the characteristics of combustion fires on construction sites. Based on a refined BIM model, a numerical simulation of the fire situation is performed using PyroSim (2019 version) software on a construction site. In the Pyrosim fire simulation model, fire scenarios with distinct construction stages and fire source locations are set up to simulate, compare, and analyze the varying pattern of each fire product in various fire scenarios. Using this information with the Pathfinder (2019 version) simulation model, a coupled simulation test of fire evacuation is conducted to assess the safety of evacuating individuals in each fire scenario. The results show that flammable materials in open spaces are more risky to burn than in confined spaces. After optimizing the utilization of safety exits and the density of people in the second simulation, it was found that the required safety evacuation time was reduced to 267 s, which is lower than the available safety evacuation time of 318.5 s for each scenario. All fire scenarios meet the safe evacuation criteria. The study results can provide a theoretical basis for developing fire response strategies for construction units and contribute to site safety management. [ABSTRACT FROM AUTHOR]

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.

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.

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.

Introduction/purpose: The construction industry is one of the main producers of dust, greenhouse gases and air pollutants. Effective operation and management of construction site operations can significantly reduce projects' carbon footprints and other environmental impacts. Through the cooperation of a scientific and research institution and a construction company, the real-time monitoring of air quality at a construction site was implemented using IoT technologies. Methods: An IoT-based system framework that integrates a distributed sensor network to collect real-time data and demonstrates air quality at a construction site was implemented. Different types of sensors were used to collect data related to NO2, PM2.5, and PM10 particles, as well as meteorological parameters – wind speed and direction, humidity, pressure, and temperature. Results: The results of real-time measurements provide a picture of the state of air pollution at the construction site and the connection with construction activities that can be managed in order to reduce the concentration of polluting gases and suspended particles. Through on-site monitoring of a construction site in Belgrade City, this study found that the dust level due to construction activities is relatively high. Conclusion: It can be concluded that the construction activity had a significant impact on the air quality in the construction surrounding areas. Regarding the main factors affecting the building construction dust emission, the correlations show that building construction dust emission was not significantly correlated with meteorological factors. [ABSTRACT FROM AUTHOR]

Mineral resource-based cities have formed a large number of goafs due to the long-term mining of coal. It is of great significance to make full use of the abandoned land resources above the goaf to promote the transformation and development of resource-based cities. In order to avoid the threat of surface residual deformation to the proposed construction project, it is an urgent problem to obtain the stability results of the construction site accurately. First of all, based on the principles of relevance, hierarchy, representativeness and feasibility of index selection, 10 indexes are selected to construct the stability evaluation index system. Then the subjective weight and objective weight of evaluation indexes are determined based on improved AHP, rough set and CRITIC methods, which improves the accuracy of the determination of the index weights. In addition, the membership degree of each index is determined using the cloud model. Finally, the stability grade can be obtained according to the maximum membership degree theory. The above researches are applied to evaluate the stability of the Mianluan expressway construction site, and the results show that the stability level of the study area is not uniform and that there are two states: stable and basically stable. Finally, a sensitivity analysis of the subjective weight of each index is carried out, the index stopping time has the highest sensitivity to weight (12.44%), which is far lower than the corresponding weight change rate of 100%, indicating that the determination of weight is scientific and reasonable. These things considered, the reliability of the evaluation result is indirectly verified according to the field leveling. This research can provide a reference for the effective utilization of land resources above an old goaf. [ABSTRACT FROM AUTHOR]

Utilizing Unmanned Aerial Vehicles (UAV) and instance segmentation for construction site monitoring(such as construction machinery and operation surfaces) offers a significant leap in management efficiency over traditional manual supervision methods. However, in UAV-based remote sensing images, the subtle presence of construction machinery and the image features resemblances among various operational surfaces make it difficult to segment instances. To address these challenges, this study proposed a novel instance segmentation model based on the YOLOv8-seg model. Given the unique challenges, the proposed model makes three improvements to the original YOLOv8-seg model. First, the paper incorporates the FocalNext module, which extends the sense field of the convolutional kernel to capture contextual data and integrates multilevel features, enhancing the perception of local details. Second, the paper incorporates the Efficient Multiscale Attention (EMA) module, which refines image features by emphasizing spatial-channel interactions and adeptly contrasts patterns across scales to detect nuances overlooked by conventional models, aiding in distinguishing similar construction operation surfaces. Last, given the intricate nature of construction site images, this paper incorporates the Context Aggregation module, which enhances pixel analysis by intelligently modulating feature weights to highlight essential global contexts. The ablation experiment demonstrates that the enhancements perform well on the YOLOv8-seg two variants model. Comparative experimental results show that the improved model significantly outperforms existing instance segmentation models regarding model performance, complexity, and inference speed. Overall, the improved YOLOv8-seg model balances model performance and computational complexity to meet the needs of edge device deployment in field monitoring.

ObjectiveTo determine the epidemiological characteristics of a cluster of SARS-CoV-2 Omicron variant at a construction site and provide evidence for further COVID-19 prevention and control.MethodsDemographic data of all COVID-19 cases at a construction site in Qingpu District， Shanghai， and basic information of the construction site were retrospectively collected through filed investigation. Descriptive epidemiology was used for the analysis. Basic reproduction number （R0） and time-dependent reproduction number （Rt） were calculated using R program.ResultsDuring April 12 and May 8， 2022， a total of 314 cases were reported at the construction site， with an attack rate of 60.62%. The attack rate significantly differed between workers and managers （χ2=10.868， P

Introduction: Nearly half of occupational accidents in Iran occur in construction sites. Therefore, modeling of occupational accidents in these sites is one of the solutions to design safety strategies to reduce occupational accidents in the field of construction. This study was designed and conducted with the aim of modeling the cause-consequence of accidents in construction sites. Material and Methods: This study was conducted based on a retrospective analysis of 10-year accident data (2010-2019) in Iranian construction sites in 2020. The main variable included the types of occupational accidents in construction sites. The study tool included accidents checklist as well as a detailed report of the studiedaccidents. The required data were collected based on a conceptual model designed to model the cause-consequence of accidents in the construction sites. Cause-consequence modeling of the studied accidents has been done based on the structural equation modeling and using IBM SPSS AMOS v. 22.0. Results: The frequency of the studied accidents was 3854 accidents. The annual averages of AFR and ASR indices were 17.27 ± 8.54 and 322.42 ± 44.23 days, respectively. The results of cause-consequence modeling of these construction accidents showed that individual and occupational, safety training and risk assessment factors as well as variables related to these factors have a negative and significant relationship with the indicators of the construction accidents, and the factors of environmental conditions and unsafe acts and variables belonged to these factors have a positive and significant relationship with these indicators (p < 0.05). Conclusion: The findings of the study revealed that the highest impact factors on accident indicators were related to safety training, risk assessment and unsafe acts and their variables. Therefore, the results of this modeling can help to design safety strategies in construction sites.

Beetles are one of the most diverse and often highly specialized groups among saproxylic organisms and play a key role in forest dynamics. To develop conservation plans in forests threatened by human activities, such as construction sites, it is crucial to identify key parameters characterizing forest structure in turn influencing saproxylic beetle diversity and abundance. Here, we investigate the difference in forest structure parameters and their cascading effect on saproxylic beetle communities between a forest site affected by the construction site expansion of the Turin–Lyon High-Speed Railway Line and a nearby second forest site. Our study showed differences in forest structure parameters between the two sites, in particular in the overall volume and diameter of coarse woody debris and in standing dead tree abundance. Even saproxylic beetle community structure was different between the two sites and this difference was linked to the different abundance and species richness of standing dead trees. Our findings provide information for the development of a local conservation plan for the saproxylic beetle community within forest habitats. Increasing the volume of deadwood and enriching tree diversity can locally sustain abundant and diverse communities and, eventually, support those species that are threatened by the building site expansion. [ABSTRACT FROM AUTHOR]

Considering a large number of Small and Medium-Scale Contractors (SMSCs) dominating the construction industries of many developing nations and the increasingly high rate of tower crane accidents in the industry, this paper seeks to methodically determine the safety risk factors that are significant in influencing construction site safety, especially on sites where the SMSCs operate tower cranes. The paper will further assess the extent to which each safety risk factor affects safety on construction sites. Data for the study was obtained through a literature search, site visual observations, discussions with site operators, and structured questioning of safety and equipment managers of leading construction companies. The study's findings reveal that the operator's low experience level was the most significant factor influencing construction site safety, particularly when operating tower cranes. This paper systematically investigates the key safety risk factors that influence construction site safety when operating tower cranes. The paper presents a clear methodology for identifying and prioritizing the safety risk factors, which may readily apply to other construction equipment. The findings of this paper are expected to play an important role in promoting and enhancing a safety culture for operating tower cranes in construction sites, particularly the project sites operated by the SMSCs. [ABSTRACT FROM AUTHOR]

The article deals with the issues of global integration of the accumulated arrays of information models of capital construction objects into a common space of information models, created according to the principles of organization and big data processing. The problems of geoinformation binding of information models, organization of means and places of their storage, methods of processing and providing access to interested subjects are considered. It is proposed to create a common space of information models according to a distributed decentralized scheme with direct interaction between state and commercial participants, both in terms of business integration, and in terms of joint data management and implementation of replication technologies.

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.

In historic centers, construction works consist of complex activities that must balance the operative requirements and lower the impacts on a delicate and sensible environment. In this urban system, especially regarding relevant reconstruction processes such as post-natural disaster scenarios, construction operations are performed through the traditional construction processes, using fuel-based generators and vehicles with limited efficiency and with relevant impacts in terms of the consumed energy, noise and vibrations. In the global transition of the construction sectors towards a zero-emission and fuel-free future, construction sites in historic centers represent a particular opportunity where the application of fuel-free strategies is particularly feasible and can provide additional value in terms of the environmental impact, productivity and health and safety. This work addresses the need for a framework to provide the basis for the application of fuel-free principles in construction within historic city centers dealing with two major concepts: the adaptive construction site as a way to reduce the energy demand and the potential adoption of fuel-free machines. The former is derived from the analysis of a real project in the historic city of L'Aquila, while the latter is defined through the identification and categorization of the applicable electric machines, equipment and vehicles and the discussion of the limits, opportunities and added value of the fuel-free strategies. [ABSTRACT FROM AUTHOR]

By reducing construction times and thereby shortening the times of construction site operation, it is possible to contribute to ensuring the social, economic, and environmental pillars of sustainability, which are necessary to meet the 2030 climate target plan set by the European Commission. This paper deals with an analysis of the time of site operation in construction of residential buildings. The site operation time in construction of fourteen residential buildings in Slovakia was examined. The research offers findings that can help clients make more rational decisions about the duration of construction site operation they request from contractors in construction contracts. Defining the mathematical dependence between the size of building and needed time of construction site operation in the pre-project planning phase was one of the results of this analysis. Based on the relationship expressed in this way, contractors can predict whether it will be possible to obtain a score in an environmental assessment of a building by minimizing undesirable effects of the construction site (such as dust, noise, and vibrations) through reducing the time of construction site operation. The study also produced a methodology in the form of steps or actions for the possible reduction of construction site operation time. The equation of dependence that resulted from the present study may be a good basis for planning a sustainable construction site that only affects its environment during the necessary construction time. [ABSTRACT FROM AUTHOR]

The highly intensive construction activities in the process of urbanization have led to the risk of soil loss, which is due to the disturbance of urbanization on the soil; this makes the soil more vulnerable to erosion by rain and other factors, thus causing soil loss to the urban drainage pipe network or the river channels around the city. This process is affected by both natural and human factors. Based on engineering experience and existing research, 13 influencing factors were identified and classified into four dimensions: Natural Conditions (NC), Construction Activities (CA), Conservation Measures (CM) and Management Measures (MM). Fifteen experts from Shanghai, Guangzhou and Zhengzhou, three main cities in China, were invited to assess the weight of each influencing factor through pairwise comparison. Based on the analytic hierarchy process, the soil erosion risk evaluation model of construction sites in megacities was established, and the weight of each influencing factor was determined. According to the weights, the weighted summation method can be used to calculate the comprehensive scores of these sites and the soil erosion risks of the construction sites can be ranked according to the comprehensive scores for multiple construction sites. The analysis of the model shows that MM is the most important factor, and improving the management level is the key measure to control the soil erosion of construction site in megacities. In addition, in the four dimensions, the results of the weight of each influencing factor in the NC dimension are quite different; this is due to the different cities where the experts are from, indicating that the natural conditions of the location will affect empirical judgment. By inviting many experts to evaluate, the deviation in judgment results, caused by differences in natural conditions, can be reduced. [ABSTRACT FROM AUTHOR]

Chalk lines are used to print layouts in construction sites to indicate the location of attaching or cutting objects; printing depends on the skills of workers and is suitable for small-scale work. Moreover, this type of work requires a precise measurement process, which is time-consuming, to avoid errors. Thus, discrepancies between blueprints and construction site layouts can occur if construction plans and management are not uniformly aligned. To improve the traditional floor-layout-printing technique on construction sites, this study introduces a manually operated mecanum-wheeled mobile robot in the preliminary stage, i.e., before the development of a full-fledged automated system. This manually operated robot helps determine the technologies required for robotic automation. In the development process, layout-printing technology is classified into a marking toolkit, control system that can be manually operated, and mobile driver. To improve layout-printing quality, this study adopted a mecanum-wheeled design to improve mobility. In this study, applied tests are required to consider the site environment for automatically marking floor layout prints. To determine the applicability of the developed technology, this study conducted a field applicability experiment with a pen-type marking module and laser-type toolkit. The experiment confirmed that layout printing based on environmental changes on the construction site can be manually performed using the mobile robot system. To automatically mark floor-layout-printing work, it is necessary to consider the floor characteristics on the construction site. In addition, this experiment shows that the newly applied laser toolkit technology can be applied to layout printing within 12 mm from the floor. To apply this mechanism to a mobile robot that can automate layout printing, it is necessary to technically enhance the optimization of marking quality, e.g., floor separation distance and marking thickness. [ABSTRACT FROM AUTHOR]

Pollutants from construction activities of building projects can have serious negative impacts on the natural environment and human health. Carrying out monitoring of environmental pollutants during the construction period can effectively mitigate environmental problems caused by construction activities and achieve sustainable development of the construction industry. However, the current environmental monitoring method relying only on various sensors is relatively singlar which is unable to cope with a complex on-site environment We propose a mechanism for environmental pollutants identification combining association rule mining and ontology-based reasoning and using random forest algorithm to improve the accuracy of identification. Firstly, the ontology model of environmental pollutants monitoring indicator in the construction site is built in order to integrate and share the relative knowledge. Secondly, the improved Apriori algorithm with added subjective and objective constraints is used for association rule mining among environmental pollutants monitoring indicators, and the random forest algorithm is applied to further filter the strong association rules. Finally, the ontology database and rule database are loaded into a Jena reasoning machine for inference to establish an identification mechanism of environmental pollutants. The results of running on a real estate development project in Jiangning District, Nanjing, prove that this identification mechanism can effectively tap the potential knowledge in the field of environmental pollutants monitoring, explore the relationship between environmental pollutants monitoring indicators and then overcome the shortcomings of traditional monitoring methods that only rely on sensors to provide new ideas and methods for making intelligent decisions on environmental pollutants in a construction site. [ABSTRACT FROM AUTHOR]

3D point cloud data (PCD) can accurately and efficiently capture the 3D geometric information of a target and exhibits significant potential for construction applications. Although one of the most common approaches for generating PCD is the use of unmanned aerial vehicles (UAV), UAV photogrammetry-based point clouds are erroneous. This study proposes a novel framework for automatically improving the coordinate accuracy of PCD. Image-based deep learning and PCD analysis methods are integrated into a framework that includes the following four phases: GCP (Ground Control Point) detection, GCP global coordinate extraction, transformation matrix estimation, and fine alignment. Two different experiments, as follows, were performed in the case study to validate the proposed framework: (1) experiments on the fine alignment performance of the developed framework, and (2) performance and run time comparison between the fine alignment framework and common registration algorithms such as ICP (Iterative Closest Points). The framework achieved millimeter-level accuracy for each axis. The run time was less than 30 s, which indicated the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Split pins (SPs) are essential for maintaining the structural stability of catenary support devices (CSDs) in high-speed railroads. Excitation and vibration induced by pantograph-catenary interactions would cause SP deterioration, including but not limited to, loosening, breaking, or missing SPs. Current supervised SP inspection systems struggle to meet expectations regarding general anomaly detection. This paper presents an efficient SP inspection system based on unsupervised learning. First, a lightweight and fast object detector is designed and combined with an incremental training strategy to sequentially localize the CSD joints and SPs. Second, an unsupervised autoencoder equipped with a perceptual loss, termed as CSGAN (catenary-style generative adversarial network), is developed to accomplish the encoder-decoder process for SP reconstruction. Finally, an anomaly judgment index is integrated into this system for general SP anomaly indication. Extensive ablation and comparison experiments show the proposed approach surpasses existing state-of-the-art models in accuracy and inference speed. • Developed a cost-effective automated split pin inspection method during track construction and rehabilitation. • The proposed inspection method is based on a fast and lightweight object detector and an incremental training strategy. • Introduced a new unsupervised autoencoder named CSGAN (catenary-style generative adversarial network). • Defined a new anomaly judgment index based on mean absolute error (MAE) and structural similarity index measure (SSIE). [ABSTRACT FROM AUTHOR]

High-rise building machines (HBMs) play a crucial role in the construction of super-tall buildings, with their working states directly impacting safety, quality, and progress. Given their extensive floor coverage and complex internal structures, monitoring priorities should shift according to specific workflows. However, existing research has primarily focused on monitoring key HBM components during specific stages, neglecting the automated recognition of HBM workflows, which hinders adaptive monitoring strategies. This study investigates the critical states of HBM construction across various structural layers and proposes a method rooted in vibration signal analysis to determine the HBM’s working state. The method involves collecting vibration signals with a triaxial accelerometer, extracting five distinct vibration signal features, classifying these signals using a k-Nearest Neighbors (kNN) classifier, and finally, outputting the results through a classification rule that aligns with the actual workflow of the HBM. The method was implemented in super-high-rise buildings exceeding 350 m, achieving a measured accuracy of 97.4% in HBM working state recognition. This demonstrates its proficiency in accurately determining the construction state and facilitating timely feedback. Utilizing vibration signal analysis can enhance the efficiency and safety, with potential applications in monitoring large-scale formwork equipment construction processes. This approach provides a versatile solution for a wide range of climbing equipment used in the construction of super-tall buildings and towering structures.

To ensure the safety of construction site personnel and to improve the efficiency of emergency safety evacuation of site personnel, this study analyzes the risk reasons for fire accidents and the characteristics of combustion fires on construction sites. Based on a refined BIM model, a numerical simulation of the fire situation is performed using PyroSim (2019 version) software on a construction site. In the Pyrosim fire simulation model, fire scenarios with distinct construction stages and fire source locations are set up to simulate, compare, and analyze the varying pattern of each fire product in various fire scenarios. Using this information with the Pathfinder (2019 version) simulation model, a coupled simulation test of fire evacuation is conducted to assess the safety of evacuating individuals in each fire scenario. The results show that flammable materials in open spaces are more risky to burn than in confined spaces. After optimizing the utilization of safety exits and the density of people in the second simulation, it was found that the required safety evacuation time was reduced to 267 s, which is lower than the available safety evacuation time of 318.5 s for each scenario. All fire scenarios meet the safe evacuation criteria. The study results can provide a theoretical basis for developing fire response strategies for construction units and contribute to site safety management.

The problem of the containment of fine dust (especially PM 2.5 and PM 10) emitted into the atmosphere is particularly acute, especially in industrialized countries. However, there are particular areas where it is still not adequately considered. One of these is the construction site sector. The aim of this work is to design a flexible, economical, and easy-to-use system, which allows for the detection of the emissions produced in critical circumstances such as the demolition of a building. To this end, a data logger and five customized nodes were designed through a five-step method. The data logger is able to transmit data to a PC, making them available in real time. The study was conducted on a reconstruction site in L'Aquila, Italy, a city severely affected by the earthquake in 2009, for two working days and a public holiday. Even if not presenting substantial critical issues in relation to the latter, the experimental results show that the emissions of PM 2.5 and PM 10 detected during the demolition activity far exceed, in some moments, the threshold values. In fact, peaks as high as about 123 μg/m3 for PM 2.5 and over 1000 μg/m3 for PM 10 have been detected. [ABSTRACT FROM AUTHOR]

Malaysia has seen the third wave of infection since the start of the global COVID-19 pandemic, with approximately 103 construction sites involving over 14,677 workers reported from April 2020 to February 2021. This has led to limited progress in construction projects or a complete halt, resulting in late project delivery. The purpose of this paper is to investigate the factors influencing the spread of COVID-19 and the strategies taken by the affected construction sites to mitigate the spread of the outbreak. The researchers adopted a case study approach with a multiple-case design and discusses the use of an in-depth interviewing method to collect rich data on the studied phenomenon. Data collected from three construction sites. The sites were mixed development projects in nature and provided in-depth, rigorous, and robust information. Based on the results, two categories of factors influencing the spread of COVID-19 were established. These are primary and secondary factors, such as workers' mobilisation, uncontrolled movement of workers, and the limited practice of social distancing. Furthermore, evidence suggests that the strategies adopted to control the effects of the pandemic were a combination of government enforcement and initiatives taken by construction companies. This paper concludes that an early identification of the causes of the spread will enable appropriate implementation strategies to control the outbreak. This study is an attempt to present the experiences of one developing country as an example of a means of dealing with unexpected pandemics or other intractable diseases that can affect project delivery. [ABSTRACT FROM AUTHOR]

To improve the dust control efficiency of soil for backfilling at construction sites, a novel waterborne polymer was used as a dust suppressant, and the dust emission model was created to control the effect of a large-scale field. The results showed that the waterborne polymer could improve the water retention efficiency of soil for backfilling, and the average water content was 2.18 times that of the watered samples, significantly delaying water evaporation. The compressive strength of soil for backfilling reached 4.91 MPa and improved the wind erosion resistance of the consolidation layer, effectively resisting wind damage. At a construction site, the waterborne polymer was sprayed on soil for backfilling, and the concentration of PM10 was reduced by 67.41%, confirming the effectiveness for large-scale utilization. [ABSTRACT FROM AUTHOR]

Fire at a construction site usually results in serious accidents. Therefore, fire management at the construction site is critical to decreasing possible accidents. However, conventional fire safety management can be problematic in many aspects, such as visualization, multi-stage alarm systems, and dynamic escape route optimization. To solve these issues, this paper develops a platform for a BIM-based fire safety management system that considers the construction site. The developed platform contains four subsystems: a remote monitoring subsystem, a fire visualization subsystem, a multi-stage fire alarm subsystem, and an escape route optimization subsystem. It detects the fire hazard in the early stage of the fire by the remote monitoring subsystem and transmits this information to the fire visualization subsystem for displaying. Furthermore, the multi-stage fire alarm subsystem sends warnings or alarms based on the fire's severity. Moreover, the escape route optimization subsystem dynamically optimizes the evacuation routes by considering the actual number of people at the construction site and the potential crowding as people pass through the escapeway. Results show that this system can provide informative and on-time fire protection measures to different participants at the construction site. This study can also serve as a solution to improve fire safety management at the construction site. [ABSTRACT FROM AUTHOR]