Search Results (5,249)

Traffic calming measures are implemented more and more often in residential districts as part of home zone sustainability projects. For economic reasons, road humps are the most commonly used traffic calming measures to slow down the traffic within the home zone. Prefabricated units or concrete pavers are the materials of choice for their construction. The studies carried out so far on many different road hump types covered the effect of height, approach/departure ramp inclination(s), and intervals between successive humps on the final speed and the safety of road traffic. The impacts of braking before and acceleration after passing a hump on the pavement and the effect of the associated shocks on the riding comfort of both drivers and passengers and vehicle suspension were also investigated. What is missing in the available literature is information on the slowing effect of road humps depending on the longitudinal gradient of the street and the street’s landscaping. This article is intended to fill this gap by presenting the results of speed surveys carried out on three selected two-way streets located in home zones with different longitudinal gradients and a few humps of different designs that are placed at different intervals. Speeds were measured both before and after each of the successive humps. The “after” speeds were found to depend not only on the hump type and parameters but also on the direction of travel, vertical alignment of the street, parking location, and orientation of the parking space relative to the road axis. Full article

This study aims to define specific transferable engineering capabilities needed for the implementation (design and practices) of circular economy (CE) within a smart city setting. We conducted a critical literature review of over 100 studies on the core values of CE and smart cities to investigate the knowledge gap in this topic and understand what specific skillset is employed by industry experts that can be harnessed on a wider scale, which can allow for the optimization of CE. There is a lack of research on the skillsets needed to implement a circular economy in any setting, and there are very few studies on circularity practices in a smart city setting. Primary data collection allows us to bridge this knowledge gap, yielding new findings that do not already exist concerning the skillset employed by experts in the field, which can positively impact the smart city settings in which a circular economy is implemented. We conducted a qualitative analysis based on expert interviews of 21 participants who have experience in the circular economy. This information will benefit the industry by informing businesses and councils about the key skillsets and capabilities to look out for when employing people to implement any aspect of circular practices in a smart city setting, with an emphasis on enhancing efficiency, achieving deliverables, and thinking systemically to address complex challenges they may face during the implementation. We also investigated the implementation of CE in smart cities to provide a well-rounded view of the different achievements and challenges faced during the process. This mainly focuses on the work of governance in smart circular cities, a factor that has many important implications and externalities in different sectors. This study describes the methodology adopted to formulate a detailed questionnaire for expert interviews with respect to the skill gap and capabilities necessary for working in the industry, the results of which aid discussions regarding the different challenges faced in CE implementation. Our findings reveal that background knowledge in engineering and sustainability is the most ‘highly critical’ hard skill according to the experts, while communication and stakeholder engagement are the essential soft skills required to ensure the success of a circular economy within smart city settings. Full article

Most construction projects encounter safety issues that may affect project effectiveness and the lives of workers. Although various studies have investigated these factors, in some countries, such as Rwanda, there is still little empirical evidence regarding the important aspects that contribute to safety effectiveness. Therefore, this study was carried out to predict the resilient safety effectiveness in the Rwandan building construction sector via the artificial neural network (ANN) model. Through a literature review, resilient safety variables that may be relevant in the Rwandan construction sector were identified. Data were collected through questionnaires. Moreover, the levels of importance of resilient-safety-effectiveness-related factors were pinpointed and assessed using the analytical hierarchy process (AHP). Consecutively, an ANN model that could predict the effectiveness of resilient safety was developed. This study contributes to the awareness of key factors that may affect the effectiveness of resilient safety, and it helps to forecast the effectiveness of resilient safety not only in Rwanda, but also in other low- and middle-income countries with different conditions by stressing the importance of reducing safety-related risks in building construction projects. Full article

In tunnel engineering, joint shear slip caused by external disturbances is a key factor contributing to landslides, instability of surrounding rock masses, and related hazards. Therefore, accurately characterizing the macromechanical properties of joints is essential for ensuring engineering safety. Given the significant influence of rock joint morphology on mechanical behavior, this study employs the frequency spectrum fractal dimension (D) and the frequency domain amplitude integral (Rq) as quantitative descriptors of joint morphology. Using Fourier transform techniques, a reconstruction method is developed to model joints with arbitrary shape characteristics. The numerical model is calibrated through 3D printing and direct shear tests. Systematic parameter analysis validates the selected quantitative indices as effective descriptors of joint morphology. Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. Machine learning, recognized as a rapidly advancing field, plays a pivotal role in data-driven engineering applications due to its powerful analytical capabilities. In this study, six algorithms—Random Forest (RF), Support Vector Regression (SVR), BP Neural Network, GA-BP Neural Network, Genetic Programming (GP), and ANN-based MCD—are evaluated using 300 samples. The performance of each algorithm is assessed through comparative analysis of their predictive accuracy based on correlation coefficients. The results demonstrate that all six algorithms achieve satisfactory predictive performance. Notably, the Random Forest (RF) algorithm excels in rapid and accurate predictions when handling similar training data, while the ANN-based MCD algorithm consistently delivers stable and precise results across diverse datasets. Full article

According to South Korea’s Ministry of Employment and Labor, approximately 25,000 construction workers suffered from various injuries between 2015 and 2019. Additionally, about 500 fatalities occur annually, and multiple studies are being conducted to prevent these accidents and quickly identify their occurrence to secure the golden time for the injured. Recently, AI-based video analysis systems for detecting safety accidents have been introduced. However, these systems are limited to areas where CCTV is installed, and in locations like construction sites, numerous blind spots exist due to the limitations of CCTV coverage. To address this issue, there is active research on the use of MEMS (micro-electromechanical systems) sensors to detect abnormal conditions in workers. In particular, methods such as using accelerometers and gyroscopes within MEMS sensors to acquire data based on workers’ angles, utilizing three-axis accelerometers and barometric pressure sensors to improve the accuracy of fall detection systems, and measuring the wearer’s gait using the x-, y-, and z-axis data from accelerometers and gyroscopes are being studied. However, most methods involve use of MEMS sensors embedded in smartphones, typically attaching the sensors to one or two specific body parts. Therefore, in this study, we developed a novel miniaturized IMU (inertial measurement unit) sensor that can be simultaneously attached to multiple body parts of construction workers (head, body, hands, and legs). The sensor integrates accelerometers, gyroscopes, and barometric pressure sensors to measure various worker movements in real time (e.g., walking, jumping, standing, and working at heights). Additionally, incorporating PPG (photoplethysmography), body temperature, and acoustic sensors, enables the comprehensive observation of both physiological signals and environmental changes. The collected sensor data are preprocessed using Kalman and extended Kalman filters, among others, and an algorithm was proposed to evaluate workers’ safety status and update health-related data in real time. Experimental results demonstrated that the proposed IMU sensor can classify work activities with over 90% accuracy even at a low sampling rate of 15 Hz. Furthermore, by integrating internal filtering, communication modules, and server connectivity within an application, we established a cyber–physical system (CPS), enabling real-time monitoring and immediate alert transmission to safety managers. Through this approach, we verified improved performance in terms of miniaturization, measurement accuracy, and server integration compared to existing commercial sensors. Full article

One of the technical problems that must be solved in coal mine production is when the coalface rapidly crosses the fault. Based on the occurrence characteristics of the F₆ fault (maximum throw: 13.5 m) in the #3_up1101 fully mechanized caving coalface at Gaozhuang Coal Mine, two different solutions allowing the coalface to pass through this fault were proposed, and the solution of pre-driven roadways with rock pillars was optimally determined. The main implementation steps of the method include designing the layout parameters of the pre-driven roadways, determining the width for rock pillars between the adjacent pre-driven roadways, construction of pre-driven roadways by smooth wall blasting, and controlling the surrounding rock deformation of the pre-driven roadways. The results of engineering practice show that it took only 23 days for this coalface to pass through fault F₆, about one month shorter than the time required by traditional methods (e.g., proactively taking a detour). Moreover, this method helped achieve stable coal production (an 8.5 × 10⁴ t increase), prevented much gangue from mixing with coal, reduced wear and tear on the mining equipment, and enhanced safety. The economic benefits delivered totaled about CNY 71.1 million. Therefore, this method can ensure continuous, safe, and efficient mining at the coalface, alleviating the tight situation of mine production succession. The results of this study can provide a good reference to help coalfaces rapidly move across faults under similar geological conditions in other mines. Full article

Construction workers are often subjected to strenuous manual labor, poor working conditions, and prolonged separation from family, leading to psychological symptoms such as stress, depression, and anxiety. These psychological factors, combined with safety climate elements like risk perception and safety attitude, significantly influence workers’ safety behaviors, including risk-taking, safety compliance, and safety participation. To address these issues, this study investigates the interplay between psychological symptoms, safety climate, and safety behaviors among construction workers in Mainland China. Data were collected through a survey of 160 construction workers, with results analyzed using correlation and regression techniques. The findings reveal that: (1) risky behavior is primarily driven by anxiety and risk perception; (2) safety participation is influenced by safety attitudes and levels of depression; and (3) safety compliance is affected by risk perception, safety attitude, stress, and depression. Interestingly, an inverse relationship is observed between depression and both risky behavior and safety compliance. These results underscore the importance of addressing psychological well-being to improve safety outcomes. Practical implications include providing psychological counseling, stress management training, and improving social connections for workers, such as facilitating regular video calls with family members or offering travel reimbursements. Full article

Work safety governance is a critical component of corporate ESG (Environmental, Social, and Governance) performance, particularly in high-risk industries. Effective safety supervision systems not only protect workers’ wellbeing, a key social metric in ESG frameworks, but also enhance corporate governance through improved risk management and regulatory compliance. The supervisory system represents a major institutional innovation in China’s approach to addressing increasingly complex work safety governance challenges. This study constructs an evolutionary game model involving the central government, local government, and high-risk enterprises to analyze the evolutionary characteristics of stakeholder behaviors. Through system simulation, we examine how key parameter changes affect the stability of system equilibrium points. Our findings reveal that (1) the current supervisory system effectively incentivizes both local governments to conduct safety supervision and high-risk enterprises to comply with safety investment requirements. (2) While government penalty levels do not affect strategy combinations, both insufficient and excessive penalties slow the system’s evolution toward optimal states. (3) Local governments tend to choose non-regulatory strategies when transfer payments and enterprise subsidies are inadequate. (4) Insufficient supervision intensity from the central government leads to local government non-regulation, and although this can be addressed by increasing supervision intensity, excessive supervision reduces the system’s evolution speed toward ideal states. Based on these findings, we propose policy recommendations for rational supervision intensity control, scientific reward–punishment mechanisms, and enhanced safety information transparency. This framework provides insights into the relationship between governance mechanisms and corporate long-term sustainability, which has been shown to improve ESG standards. Full article

Population aging and urbanization are two of the most significant social transformations of the 21st century. Against the backdrop of rapid aging in China, developing age-friendly community environments, particularly through the renovation of legacy residential communities, not only supports active and healthy aging but also promotes equity and sustainable development. This study focuses on residential communities built in the 1980s in Hangzhou, exploring strategies for the age-friendly renovation of outdoor public spaces. The residential communities that flourished during the construction boom of the 1980s are now confronting a dual challenge: aging populations and deteriorating facilities. However, existing renovation efforts often pay insufficient attention to the comprehensive age-friendly transformation of outdoor public spaces within these neighborhoods. Following a structured research framework encompassing investigation, evaluation, design, and discussion, this study first analyzes linear grid layouts and usage patterns of these communities. Then, the research team uses post-occupancy evaluation (POE) to assess the age-friendliness of outdoor public spaces. Semi-structured interviews with elderly residents identify key concerns and establish a preliminary evaluation framework, while a Likert-scale questionnaire quantifies the satisfaction with age-friendly features across four communities. The assessment reveals that key age-friendliness issues, including poor traffic safety, dispersed activity spaces, and insufficiently adapted facilities, are closely linked to the linear usage patterns within the spatial framework of the grid layouts. Based on the findings, the study develops tiered renovation goals, renovation principles and implemented an age-friendly design in the Hemu Community. The strengths, weaknesses, and feasibility of the renovation plan are discussed, while three recommendations are made to ensure successful implementation. The study is intended to provide a valuable reference for advancing age-friendly residential renewal efforts in Hangzhou and contributing to the broader objective of sustainable, inclusive city development. Full article

Background: During the COVID-19 pandemic, migrant populations remained under-immunized due to limited access to health care, language barriers, and vaccine hesitancy. The USAID-funded MOMENTUM Routine Immunization Transformation and Equity project supported the government in collaborating with various local health and non-health partners to identify and vaccinate migrants. This case study examines the roles of project partners and the strategies each entity implemented to increase COVID-19 vaccine uptake among migrants, as well as the perceptions regarding the effectiveness of these strategies. Methods: We designed a qualitative explanatory case study guided by the Behavioral and Social Drivers framework and RE-AIM implementation science frameworks. We conducted 31 focus group discussions and 50 in-depth interviews with migrants, project partners, community leaders, and government stakeholders in Tamil Nadu and Punjab. Results: In both states, partnerships with health departments, private employers, and community-based organizations were essential for identifying and vaccinating un- and under-vaccinated migrant groups. In Tamil Nadu, collaboration with the Department of Labor and mobile medical units facilitated vaccination camps at construction sites. In Punjab, religious institutions organized sessions at places of worship, and the Border Security Force enabled health workers to reach migrants living near the border. In both states, key strategies—involving influencers to discuss the importance of vaccine safety and value, bringing vaccination services to migrants’ workplaces and homes at flexible times and mandating workplace vaccination to encourage vaccination—shifted perceptions towards vaccination and increased vaccine uptake among migrants. Conclusions: The strategies and partnerships identified in this study highlight the broader implications for future public health interventions, demonstrating that collaboration with the private sector and faith-based organizations can enhance routine immunization efforts, particularly when localized to organizations that understand community needs and can address specific barriers and motivators. Full article

With the acceleration of urbanisation and the increased utilisation of underground space, providing a comfortable and healthy environment in public underground areas has emerged as a significant research topic. This study constructs a comprehensive decision-making framework for underground space environments by integrating human perception evaluations with physical environmental parameters. Using Shanghai Wujiaochang as a case study, field data collection and questionnaire surveys were conducted to evaluate key factors such as temperature (22.63 °C–26.39 °C), wind speed (0.26 m/s–0.67 m/s), and sound levels (59.68 dB–61.21 dB) for commercial-oriented spaces, and 63.15 dB–75.45 dB for transport-oriented spaces) to users’ perceived experiences. The appropriate ranges for key parameters were identified through single-indicator fitted regression analysis and the XGBoost machine-learning model, revealing the relationship between environmental parameters and human perception. The results indicated significant differences in user needs across various functional spaces, with commercial-oriented areas emphasising environmental attractiveness and comfort, while transport-oriented spaces prioritised access efficiency and safety. This study provided quantitative design benchmarks for underground spaces’ dynamic regulation and sustainable management, proposing a precise and adaptive environmental decision-making framework that combines physical parameters with user-perception feedback. Full article

Urban rail transit is one of the vital lifeline projects of megacities worldwide. While it brings convenience and economic growth to the city, the construction of urban rail transit projects is often associated with safety accidents. Emergency preparedness plays a significant role in the prevention of safety accidents and emergency rescue in urban rail transit construction projects. However, the extant literature rarely looks into this topic. The aims of this study are to review the emergency preparedness of current urban rail transit construction projects in China, to summarize their key elements, to identify their advantages and limitations, and to make recommendations accordingly. To achieve these goals, this study systematically investigates the emergency preparedness documents implemented by 52 cities in mainland China; from these, five key elements of emergency preparedness are systematically reviewed: organization; monitoring and early warning; emergency response; post-disaster recovery and reconstruction; emergency support. The advantages and limitations of existing emergency preparedness are examined, and recommendations for updates to emergency preparedness are made based on the experience and knowledge of advanced economies. The findings of the study can enhance understanding among authorities and industry practitioners of emergency preparedness as it is implemented in current urban rail construction projects. It can also provide a practical reference for the improvement of emergency preparedness of urban rail transit construction projects in the future, thereby contributing to the resilience and long-term sustainability of urban rail transit systems. Full article

The prediction of structural damage through vibrational analysis is a critical task in the field of composite structures. Structural defects and damage can negatively influence the load-carrying capacity of the beam. Therefore, detecting structural damage early is essential to preventing catastrophic failures. This study addresses the challenge of predicting damage in composite concrete–steel beams using a vibration-based finite element approach. To tackle this complex task, a finite element model to a quasi-static analysis emulating a four-point pure bending experimental test was performed. Notably, the numerical model equations were carefully modified using the Newton–Raphson method to account for the stiffness degradation resulting from material strains. These modified equations were subsequently employed in a modal analysis to compute modal shapes and natural frequencies corresponding to the stressed state. The difference between initial and damaged modal shape curvatures served as the foundation for predicting a damage index. The approach effectively captured stiffness degradation in the model, leading to observable changes in modal responses, including a reduction in natural frequencies and variations in modal shapes. This enabled the accurate prediction of damage instances during construction, service, or accidental load scenarios, thereby enhancing the structural and operational safety of composite system designs. This research contributes to the advancement of vibration-based methods for damage detection, emphasizing the complexities in characterizing damage in composite structural geometries. Further exploration and refinement of this approach are essential for the precise classification of damage types. Full article

The safety and efficiency of assembly lines are critical to manufacturing, but human supervisors cannot oversee all activities simultaneously. This study addresses this challenge by performing a comparative study to construct an initial real-time, semi-supervised temporal action recognition setup for monitoring worker actions on assembly lines. Various feature extractors and localization models were benchmarked using a new assembly dataset, with the I3D model achieving an average mAP@IoU=0.1:0.7 of 85% without optical flow or fine-tuning. The comparative study was extended to self-supervised learning via a modified SPOT model, which achieved a mAP@IoU=0.1:0.7 of 65% with just 10% of the data labeled using extractor architectures from the fully-supervised portion. Milestones include high scores for both fully and semi-supervised learning on this dataset and improved SPOT performance on ANet1.3. This study identified the particularities of the problem, which were leveraged and referenced to explain the results observed in semi-supervised scenarios. The findings highlight the potential for developing a scalable solution in the future, providing labour efficiency and safety compliance for manufacturers. Full article

In recent years, wireless power transfer (WPT) technology has entered the application stage. However, the inevitable presence of foreign objects (FO) in the magnetic coupling area during the operation of WPT systems, especially ferrite FO, may result in system detuning and safety incidents. To address this issue, this paper studies the mechanism by which the ferrite FO affects the WPT systems, providing a foundation for the future development of an FO detection device. Using a series–series topology WPT system as an example, a loop model incorporating ferrite FO is first developed to analyze the impact on the output power (OP) and transmission efficiency (TE) when the ferrite FO enters the magnetic coupling area. A 3D electromagnetic field simulation model of the magnetic coupling mechanism is then developed using finite element simulation software to study the impacts of FO of different sizes and positions within the magnetic coupling area on the WPT system. Finally, an experimental platform is constructed for experimental analysis and validation. An approach to detect the ferrite FO is provided by measuring the input power (IP) and TE. The WPT system is protected immediately by shuting down the transmitter if IP and TE sharply change over 15% in a few seconds. Full article