This study examines the impact of virtual reality (VR) training on enhancing the skills of tower crane operators in Aerial Construction Factories (ACFs). As ACF operations become increasingly complex with higher safety demands, traditional training methods struggle to simulate high-risk scenarios and provide sufficient hands-on experience. By leveraging VR technology, this study offers an immersive and interactive training environment where operators can practice in risk-free, controlled virtual scenarios. VR-based experiments were conducted to measure performance in task completion time, operational accuracy, self-efficacy, and the frequency of collision warnings during simulated tasks. The results demonstrated significant improvements in both task accuracy and operator confidence, along with reductions in task completion time and collision warnings. These findings suggest that VR training addresses the limitations of conventional methods, offering a more cost-effective, scalable, and safe solution for improving crane operator skills within ACFs. Ultimately, the study highlights the transformative potential of VR technology in enhancing operator proficiency, safety awareness, and accident prevention in the challenging and hazardous environment of ACFs.

Hong Kong faces critical challenges in the maintenance and redevelopment of aged buildings. Recently, advancements in multi-modal generative AI (GenAI) and high-definition urban geospatial data, such as point clouds, have offered new opportunities to the architectural, engineering, and construction industry. This paper defines, assesses, and maps a Building Condition Index (BCI) as the condition of aged building fabrics using GenAI and high-definition geospatial data. First, a BCI is defined as a numerical scale of multi-dimensional factors, including floor area, building age, management quality, and the presence of unauthorized building works. Then, multiple data sources, including building exterior photos, airborne point clouds, and government building datasets, are processed and trained for the BCI using multiple regression and image embedding with ChatGPT4. Finally, a comprehensive BCI map and focused BCI hot spots can be visualized for an urban area. Experiments with over 1,200 building data points in Kowloon City, Hong Kong, indicated the robustness of the BCI in explaining the exogenous factors causing decayed buildings while accurately reflecting the building condition of buildings.

Air pollution on urban sidewalks poses a huge threat to human health. We conducted an air pollution mobile monitoring campaign to measure PM2.5 levels on sidewalks in the Futian CBD area of Shenzhen, using portable air pollution sensors. The campaign involved collecting data during peak commuting times to capture variations in air quality. Calibration of the AirBeam3 devices against a standard instrument ensured data accuracy. Using GIS spatial analysis, we mapped air pollution patterns and identified notable differences between weekdays and weekends, with concentrations ranging from 3-9 μg/m³. Our findings indicate higher pollution levels on weekdays, particularly in the afternoon, correlating with increased traffic and economic activity. The study also highlights spatio-temporal heterogeneity, with morning pollution concentrations more pronounced in the northern financial district and afternoon levels higher in the southern area, characterized by commercial facilities. These patterns suggest that regional industrial distribution and traffic flow significantly influence air quality. By understanding the dynamics of pollution in urban environments, this research contributes to the development of effective strategies for improving air quality and public health in densely populated city centers.

Low back pain (LBP) is a prevalent issue among construction workers, with prevalence rates ranging from 27% to 52%. This significant burden not only affects worker health but also incurs substantial economic costs, exemplified by Brazil's annual expenditure of approximately USD $500 million and Spain's EUR €8945.6 million in related costs. To mitigate LBP, back exoskeletons have emerged as promising solutions, designed to reduce low back load during repetitive lifting tasks. This study compares the human-exoskeleton interaction forces in three back exoskeletons - SV Exosuit (a soft active exosuit integrated with a safety vest), Laevo V2 (a rigid passive exoskeleton), and MATE-XB (also rigid and passive) - through experimental tasks involving bending and squatting. Three healthy male participants performed these tasks while wearing each exoskeleton, during which human-exoskeleton interaction forces at the thigh and shoulder/chest were measured. Results demonstrated that SV Exosuit produced higher contact forces at both body regions, attributed to its smaller contact area and less cushioning material. Conversely, Laevo V2 exhibited two peaks in contact forces during a motion cycle due to its torque generation mechanism, highlighting the influence of supporting torque design on user comfort. This research underscores the critical need for optimizing exoskeleton designs to enhance comfort and usability in construction settings. Future studies should investigate a larger sample size and additional body regions to comprehensively assess the relationship between supportive torque, contact forces, joint angle, and user comfort.

Installed fire safety equipment within buildings plays a crucial role in ensuring the safety of personnel and minimizing losses. Nevertheless, if not maintained appropriately, these devices may fail to function optimally in emergency situations. As building sizes continue to grow, traditional manual inspection methods encounter significant challenges, including a heavy workload and complex information recording tasks. To tackle these issues, advanced emergency equipment detection frameworks and improvement plans have been put forward. This framework is specifically designed to overcome the problem of remote inspection being unable to accurately locate objects by establishing spatial relationships among devices, cameras, and trajectories. Firstly, the improved detection algorithm is utilized to detect objects of interest. Subsequently, these objects are located through a tracking algorithm and Visual Simultaneous Localization and Mapping (vSLAM). The on-site experimental results clearly show that the framework can effectively solve various types of equipment detection problems in a wide range of complex scenarios and holds great promise for replacing manual labor.

The accurate and efficient calculation of floor areas is a critical aspect of building design processes, yet it remains a challenging task due to the intricate rules and regulations that must be adhered to. Traditional methods, which rely on manual calculations, are not only time-consuming but also prone to human error, highlighting the need for an automated solution. Building Information Modeling (BIM) can support the automated solution by providing digital representations of building designs. The openBIM workflow enables collaborative work among various stakeholders, allowing them to share and integrate project information seamlessly without being locked into proprietary systems. While Industry Foundation Classes (IFC) files serve as a robust digital representation of BIM, facilitating data exchange across different software platforms, the complexity associated with processing these files poses a significant barrier to ordinary users. To address these gaps, we propose an innovative semi-automated floor area calculation system that leverages state-of-the-art Large Language Model (LLM) agent. This system is designed to efficiently and accurately extract relevant information from IFC files applying specific filtering conditions and then perform the necessary calculations to determine floor areas according to the required standards. Due to LLMs’ characteristic of interacting using natural language, our approach significantly reduces the technical barriers faced by non-expert users, making the process of floor area calculation more accessible and user-friendly. We validate the proposed method through the practice of simplified floor area calculation of an academic building, offering a promising solution to streamline BIM workflows and enhance productivity in the architecture, engineering, and construction (AEC) industries.

The rapid growth of urbanization and traffic complexity in smart cities has led to heightened safety risks, particularly for vulnerable road users (VRUs) like pedestrians and cyclists. This study aims to enhance VRU safety by investigating their perceived risk and decision-making processes in various traffic scenarios. Traditional methods, such as surveys and observational studies, have limitations in capturing VRUs' cognitive processes and real-time behavior. This research proposes a multimodal approach combining electroencephalography (EEG) and eye-tracking technology to analyze VRUs' cognitive and physiological responses in simulated urban environments. By integrating data on attention, vigilance, and emotional states, this approach provides a comprehensive assessment of VRUs' risk perception mechanisms. Utilizing the CARLA simulator, a platform for pedestrian-vehicle interaction is developed to simulate realistic urban scenarios, while physiological and behavioral data are collected to establish a model for predicting VRU behavior. The study contributes to theoretical insights on risk perception and practical implications for designing safer urban infrastructure in smart cities, ultimately aiming to reduce VRU accidents and enhance traffic safety.

Quality inspection in outdoor prefabricated storage yards is highly challenging due to the large volume, diversity, and complex management demands of components. Currently, these inspections are conducted manually, which is insufficient to fully meet industry needs. This study proposes an innovative approach to enable intelligent inspection for multiple prefabricated components in large-scale prefabricated storage yards by integrating Building Information Modeling (BIM), LiDAR, Unmanned Aerial Vehicles (UAVs), and Unmanned Ground Vehicles (UGVs). First, an intelligent sensing environment and stepwise collaboration mechanism are established, where UAVs are used to reconstruct a 3D comprehensive environment of the prefabrication site, providing a map to plan the optimal scanning path for LiDAR-equipped UGVs. Next, a point cloud-driven integrated geometric quality inspection method is introduced, where UGVs autonomously collect, and process point cloud data to extract precise geometric features of large components within expansive spaces. To verify the effectiveness of the proposed method, an experiment at a large-prefabricated component factory that produces a variety of types of prefabricated components is conducted. By integrating point cloud processing results with BIM model design information, this research achieves high-precision, large-scale quality inspections of the non-structural performance of prefabricated components, significantly enhancing inspection efficiency and accuracy.

In the field of construction engineering, particularly in steel structure construction, welding operations are of great importance. However, this activity carries significant safety risks, such as severe accidents like eye burns. Therefore, safety management and protective measures for these high-risk activities are especially crucial. Although computer vision-based object detection algorithms have already been applied in the construction sector, these algorithms generally lack the capability to process high-level semantic information. They can only detect objects, but cannot understand the state of the objects. To address this issue, this paper proposes an improved welding safety inspection method based on gaze estimation and object detection. First, from a human-machine interaction perspective, the method combines gaze estimation and object detection to determine the worker's operational status. Second, it performs real-time checks on the personal protective equipment (PPE) used by the welders. Experiments demonstrate the feasibility of this approach, enhancing its ability to respond to complex scenarios and contributing to improved safety levels in construction projects.

Blockchain (BC) and smart contract (SC), known for their decentralized and secure frameworks, have been successfully applied across industries to enhance security, efficiency, and transparency in technical and engineering aspects such as automated transactions, data management, and supply chain monitoring. In recent years, the construction industry has seen significant advancements in adopting BC and SC. This review paper examines the latest BC and SC prototypes, case studies, and technical applications published over the past four years, focusing on how these academic developments can be transitioned into industry practice. By categorizing the collected studies using Technology Readiness Levels (TRLs) and conducting an in-depth analysis, the paper seeks to identify strategies for accelerating the adoption of these technologies in the construction sector. Reviewing recent advancements, this paper uncovers key trends, benefits, and challenges associated with implementing BC and SC in construction. The findings suggest that these technologies can significantly enhance project efficiency, contract management, and supply chain transparency while fostering trust among stakeholders. While these technologies offer significant potential, critical challenges, including scalability, privacy, and integration complexities, hinder their widespread adoption. Moreover, specific limitations, such as the difficulty in establishing regulatory compliance and technical barriers in data management, remain unresolved. By providing actionable insights and highlighting pathways to bridge the adoption gaps, the paper aims to accelerate the integration of BC and SC into the construction industry, driving innovation, transparency, and operational efficiency.