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The unstructured construction site with a complex and changeable environment brings challenges to human–machine interaction. In this study, the earthwork digital twin (DT) is proposed to realize accurate and real‐time perception, which can support the teleoperation of an automated bulldozer. The overall framework includes anterior‐time DT, real‐time DT, and posterior‐time DT, which integrates three‐dimensional modeling, edge detection, and blade trajectory algorithms. Multidimensional heterogeneous data in bulldozer teleoperation can be displayed in real‐time to ensure construction safety. Big data during teleoperation of the construction process can be collected, stored, and analyzed. The DT proposed in this study was successfully applied in a major construction project in China, and testing results show its universality, robustness, and advanced performance. The key technologies proposed in this study can be applied to solve the common problems in the construction industry, which is promising for future intelligent construction. [ABSTRACT FROM AUTHOR]

• Systematically investigating spatial heterogeneity and temporal trends of CCE. • Exploring relationship between urbanization and CCE with four patterns identified. • Revealing critical driving factors for CCE across different patterns. • The results help to provide precise policies with regional heterogeneity. The construction industry fuels China's rapid urbanization, which also escalates carbon emissions. This study aims to investigate the spatio-temporal patterns and impact mechanisms of construction carbon emissions (CCE) under urbanization to inform low-carbon policies. We investigate the spatial heterogeneity and temporal trends of China's CCE employing the carbon emission coefficient method and the backpropagation neural network, identify various development patterns of CCE under urbanization using the K-means clustering method, and further comprehensively explore the key influencing factors through the logarithmic mean Divisia index decomposition method. Our findings indicate that: (1) CCE presents a notable spatio-temporal polarization and aggregation effect during 2009–2018 in China. Compared with rough development scenario, the green development scenario is predicted to effectively reduce CCE by 5.17 % in 2030. (2) The relationship between urbanization and CCE is revealed to be an "inverted U-shape" and four different development patterns are identified. (3) Overall, the output intensity and structural effect of fixed asset inputs are critical factors contributing to CCE. Importantly, the impact mechanisms of CCE vary under different development patterns. This study provides essential evidence-based information and scientific support for CCE reduction in China, which highlights targeted and tailored strategies for reducing CCE considering different patterns under urbanization. [ABSTRACT FROM AUTHOR]

Epidemics and other public health emergencies significantly impact medical and health service systems. The exploitation of some advanced technologies in Fangcang shelter hospitals—emergency medical service solutions adopted in China during the Corona Virus Disease 2019 (COVID-19) epidemic—was found to successfully relieve the treatment pressures. However, the implementation of the intelligent Fangcang shelter hospital system remains in the exploratory stage. Herein, this paper aims to obtain experience in the development and utilization of an intelligent system in a Fangcang shelter hospital. To achieve this, a single case study method was adopted to analyze a real case in the Optics Valley Fangcang Shelter Hospital. There were two data collection methods in the case study: (1) interviews; and (2) investigation, including some documents, models, sensors, and the platform. The main contribution of this paper is that it provides qualitative insights on how to implement an intelligent Fangcang shelter hospital system, including the development process and challenges and identifying lessons learned through the utilization of intelligent Fangcang shelter hospital systems. This paper can give a solid reference for the operation of COVID-19 field hospitals and provide clear theoretical guidance for the implementation and development of intelligent Fangcang shelter hospitals. This will help better respond to COVID-19 and similar major public health emergencies in the future. [ABSTRACT FROM AUTHOR]

• A visibility graph model implemented in a complex network theory of shield tunneling is proposed. • Degree distributions of the reconstructed networks from time series of shield parameters follow the power law. • The characteristics of shield tunneling parameters' network are scale-free and small-world. • Findings reveal turning points in the time series of shield parameters with vital node analysis. • It is tested and validated in the first Yangtze river crossing metro tunnel project in China. Shield tunneling operation affects project scheduling, cost estimation, and safety risk during metro construction. Given the importance of this process, tunnel engineers should understand the complexities of shield tunneling parameters. This study addresses the characteristics of shield tunneling parameters by introducing a visibility graph model implemented in a complex network of shield tunneling in metro construction. Time series data are collected for the analysis of six parameters generated in each segment ring tunneling cycle. These parameters are total thrust, torque, penetration rate, rotation rate, advance speed, and working chamber pressure. Bridging time series and the complex network with visibility graph algorithm indicates that all degree distributions of the construct networks follow the power law, establishing the scale-free time series of the parameters. Small-world features and hierarchical structures also indicate that the fluctuation and variance of the parameters are attracted, limited, or affected by a previous shield operation of segment rings. In addition, findings reveal turning points in the parameters' time series with vital node identification based on complex network analysis. Implications relevant for shield operators and managers are proposed to improve shield tunneling performance, efficiency, and safety. [ABSTRACT FROM AUTHOR]

Highlights: [•] A systematicapproach for Overall Process Safety Management in tunnel construction. [•] Safety system design, analysis, control and effect assessment are covered. [•] Experience to deal with challenging technical issues in complex environments. [ABSTRACT FROM AUTHOR]

l People's mental health can be significantly influenced by environmental factors interacting with one another. l A novel and robust tool is provided to mine coupled relationships between multiple risk factors. l The absence of green spaces and environmental degradation are found to be the main environmental risk factors. l Built environment professionals and local authorities can take effective measures to design or manage the built environment. There is a degree of ambiguity about the specific factors of the built environment that impact people's mental health. This paper aims to examine the relations between an individual's mental health and the environmental risk factors. Drawing on clinical data and an outpatient survey from a hospital in China, 993 samples over 18 years old were available. A Boruta algorithm is deployed to determine the factors that affect people's mental health and an association rule algorithm is then introduced to ascertain the associations between risk factors. The analysis reveals the main risk environmental factors (i.e., environmental degradation and the absence of green spaces) and their coupling effects on mental health. Environmental degradation may be a key mediator in the green space-mental health relationship. Another key risk pattern is that physically unhealthy people who reside in places lacking green spaces are prone to having mental health problems. Considering the identified risks, there is a need for built environment professionals (e.g., architects and planners) to place greater emphasis on incorporating green spaces and environment management strategies into housing developments. This paper will contribute to the development of urban planning and management policies to create environments that support people's positive mental health. [ABSTRACT FROM AUTHOR]

Evidence of the effects of long-term household air pollution (HAP) on human mental health is limited. This study aimed to explore the longitudinal relationship between long-term household air pollution exposure from solid fuel use and depression based on nationally representative follow-up dataset. A total of 7005 middle- and old-age adults from the latest four waves (2011, 2013, 2015, and 2018) of China Health and Retirement Longitudinal Study (CHARLS) were involved. The Center for Epidemiologic Studies Depression scale (CES-D 10) was used to measure depressive symptoms and individuals who got more than 12 points were considered to have depression symptoms. We conducted Cox proportional hazards regression models to examine the association between household air pollution and depression in overall population, and subgroup stratified by socio-demographic factors, lifestyle behaviors, chronic diseases, and residential environments. We found long-term household air pollution exposure from solid fuel use was significantly associated with higher depression risk among Chinese older adults (HR 1.27, 95% CI 1.14–1.42 in heating; 1.26, 1.13–1.40 in cooking). Longer duration of household air pollution exposure (1.47, 1.28–1.68 in heating; 1.36, 1.19–1.56 in cooking) and household air pollution from crop residue/wood burning (1.66, 1.41–1.94 in heating; 1.37, 1.23–1.53 in cooking) was correlated with higher depression risk. For subgroups analysis, the effect of household air pollution from solid fuel on depression varied. Compared with those who using clean fuel, older adults living in small size houses or houses with small number rooms had increasing depression risks if they used solid fuel for heating or cooking. Our findings indicate long-term household air pollution exposure from solid fuel use is associated with higher depression risk. Reducing household air pollution by restricting solid fuel use can be effective ways to prevent depression for Chinese older adults and decrease related public health burden. [Display omitted] • This study explored longitudinal association between household air pollution (HAP) and depression. • Nationally representative follow-up data of middle and old aged adults from China was used. • Long-term HAP exposure from solid fuel use is associated with higher depression risk. • Variations by socio-demographic factors, lifestyles, and chronic diseases were observed. • HAP has different effects on depression for individuals in different residential environment. Long-term household air pollution exposure from solid fuel use has significant hazardous effects on mental health for older adults. [ABSTRACT FROM AUTHOR]

Risk-based design is an essential strategy for enhancing the safety performance of road infrastructure during its operation stage. However, conventional risk-based design generally considers the risk of a single type of disaster, which is insufficient for assessing the multi-hazard risk of the road infrastructure in an extreme environment. In this study, a synthesis method, the risk-informed knowledge-based analytical method (RKAM) for multi-hazard risk assessment, is proposed for the road infrastructure. The RKAM uses risk mapping techniques and the as low as reasonably practicable (ALARP) principle. Expert knowledge is utilized in order to overcome the scarcity of historical data. The RKAM is applied to the Tuoba–Qamdo Highway project in the Qinghai–Tibet Plateau of China to assess the overall risks of causalities and structural damages caused by traffic accidents, fire accidents, and landslide disasters. The results of this case study show the capabilities of RKAM to generate a synthesis assessment of the expected loss by multi-hazard disasters, which provides valuable information for selecting the most appropriate design of road infrastructure. • A synthesis framework for multi-hazard risk visualization and examination is illustrated. • A knowledge extraction model is proposed based on the noisy-BN structures. • A tail risk examination procedure for extreme disaster scenarios is proposed. • In extreme environments, the use of risk mitigation measures for a single type ofdisaster event may increase another disaster risk. [ABSTRACT FROM AUTHOR]

Highlights • Association rules mining is combined with complex network to perform risk analysis. • An improved Apriori algorithm is developed to unearth abnormal monitoring types. • CN is used to reveal coupled relationships between abnormal monitoring types. • Coupled risks are analyzed using a large monitoring dataset. • The new approach for risk management is applied in a real subway project. Abstract Due to geotechnical uncertainties, existing underground infrastructure, the construction of deep-pit foundations in dense urban areas is particularly challenging as there is a propensity for building and structural settlement to occur. Recognizing the need to proactively manage safety risks during construction, a new risk analysis approach that combines complex networks and association rules mining (ARM) is proposed. An improved Apriori algorithm is developed to unearth abnormal monitoring types. Then, complex network theory is introduced to examine the characteristics of the coupled relationships existing between different types of abnormal monitoring types. This research identifies and examines complex network measures to understand the topology of settlement networks. It is revealed that settlement networks confirm to both scale-free and small-word properties indicating that risks are not random events. This new approach of combining ARM with complex network is applied to examine deep foundation pits that are constructed for a subway project in Wuhan, China. It is demonstrated that proposed approach can successfully reveal the association rules between safety risk monitoring types and the coupling of risks. Preventative actions can therefore be undertaken in advance to mitigate against potential risks that are identified from the abnormal monitoring combinations. [ABSTRACT FROM AUTHOR]

Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling. • A new method for predicting the attitude and position of shield machine was proposed. • A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task. • The on-site data collected from the largest river-crossing tunnel in China has been tested. • The prediction results comparing with other three models and the potential practice value were discussed. [ABSTRACT FROM AUTHOR]

• Complex network is used to evaluate settlement risk in underground construction. • Temporal evolution of risk is measured by network entropy with time windows. • Spatial distribution of settlement risk is qualified by network structure. • The risk assessment for surrounding environments is verified in a real metro project. The rapid development of underground construction results in safety problems in surrounding environments due to ground surface settlement. Comparing accumulated settlement with threshold to evaluate settlement risk is insufficiently accurate. Thus, identifying the location and change in the settlement risk is difficult. This research develops a data-based complex network approach to analyze and assess the risks of surrounding environments from a synthetic and dynamic perspective based on settlement data and address these limitations. In the proposed approach, a complex network is divided into multiple time windows, from which temporal dynamics of settlement can be captured. Then, betweenness centrality is used to describe the spatial distributions of settlement risk, and network entropy is used to quantify the evolution of settlement risk. A case of Wuhan metro project in China is used to validate the effectiveness and feasibility of the developed approach. Results demonstrated that the developed approach can reveal spatio–temporal evolution law of settlement risks of surrounding environments. The developed method not only offers a systematic way to interpret settlement risk of surrounding environment in underground construction but also has the potential to improve safety performance in construction sites. [ABSTRACT FROM AUTHOR]

Extraction of underlying knowledge from monitoring data is beneficial to on-site management during shield tunneling construction. However, it remains a challenge to unsupervisedly learn from these monitoring data due to the nature of high-dimensional, miscellaneous, and highly nonlinear. This study proposes a new systematic approach to classify shield machine monitoring data by integrating spectral clustering (SC) and complex network (CN) theory. In this approach, CN theory is introduced to obtain the topological relations and network structure of machine monitoring data directly. Based on the network topology, SC is employed to classify these data with unbiased similarity measurement. A river-crossing shield tunnel is used in this study to validate the effectiveness and feasibility of the proposed approach. It's demonstrated that the proposed approach outperforms the other SC methods for unsupervised classification of the machine monitoring data. The classification of machine monitoring data with the proposed approach has a potential value in machine performance and geological risk assessment during shield tunneling construction. • A systematic approach to unsupervisedly classify shield machine monitoring data was proposed. • The similarity matrix of spectral clustering is constructed by complex network topology of shield machine monitoring data. • This method was verified in a river-crossing shield tunnel project of Wuhan Metro Line 4 in China. • The classification results have a potential value in machine performance and geological risk assessment in shield tunneling. [ABSTRACT FROM AUTHOR]

Background: High blood pressure (HBP) and diabetes mellitus (DM) are two of the most prevalent cardiometabolic disorders globally, especially among individuals with lower socio-economic status (SES). Studies have linked residential greenness to decreased risks of HBP and DM. However, there has been limited evidence on whether SES may modify the associations of residential greenness with HBP and DM., Methods: Based on a national representative cross-sectional study among 44,876 adults, we generated the normalized difference vegetation index (NDVI) at 1 km spatial resolution to characterize individuals' residential greenness level. Administrative classification (urban/rural), nighttime light index (NLI), individual income, and educational levels were used to characterize regional urbanicity and individual SES levels., Results: We observed weaker inverse associations of NDVI with HBP and DM in rural regions compared to urban regions. For instance, along with per interquartile range (IQR, 0.26) increment in residential NDVI at 0∼5 year moving averages, the ORs of HBP were 1.04 (95%CI: 0.94, 1.15) in rural regions and 0.85 (95%CI: 0.79, 0.93) in urban regions (P = 0.003). Along with the decrease in NLI levels, there were continuously decreasing inverse associations of NDVI with DM prevalence (P for interaction <0.001). In addition, weaker inverse associations of residential NDVI with HBP and DM prevalence were found among individuals with lower income and lower education levels compared to their counterparts., Conclusions: Lower regional urbanicity and individual SES could attenuate the associations of residential greenness with odds of HBP and DM prevalence.

Background: Achieving universal health coverage (UHC) is one crucial target of the Sustainable Development Goals. However, consistent trends data for UHC evaluation in China are still scarce. The aim of our study was to provide a comprehensive assessment of UHC in China., Methods: In this serial cross-sectional study, we collected nationally representative data from the latest four rounds (2003, 2008, 2013, and 2018) of the National Health Service Surveys, with the aim of evaluating UHC in China. These surveys used multistage stratified cluster sampling covering all 31 provinces, autonomous regions, and municipalities in mainland China. Within each household, all members aged 15 years and older were interviewed. For children and adolescents aged younger than 15 years, their adult family members answered the questions for them. We constructed a UHC index following the WHO-recommended framework that included service coverage (nine prevention indicators and five treatment indicators) and financial protection (three indicators) dimensions. Bayesian regression models were done to investigate the trends in and projections of UHC index and indicators, with average annual percentage change (AAPC) and probabilities of achieving the 2030 WHO targets. Based on trends and projections, we counted population equivalents with UHC coverage. To examine the potential efficiency of financial investment, we quantified UHC performance based on government health expenditure (GHE) per capita. Finally, we explored the association between each UHC indicator and macroeconomic and health systems characteristics by using multiple regression., Findings: We sampled 57 023 households from 95 counties between September and October, 2003; 56 456 households from 94 counties between June and July, 2008; 93 613 households from 156 counties between August and October, 2013; and 94 076 households from 156 counties in September, 2018. A total of 901 182 individuals were involved in this study (193 689 in 2003, 177 501 in 2008, 273 688 in 2013, and 256 304 in 2018). Although the overall UHC increased from 44·0% (95% CI 43·0-44·9) in 2003 to 79·8% (79·2-80·3) in 2018 and is predicted to meet the 80% global target by 2030, the progress towards UHC has steadily slowed down nationally. Based on current projections, an estimated 105·8 (95% CI 27·3-189·1) million population equivalents would still not have UHC coverage in 2030. The treatment index showed a large increase over time from 2003 to 2018 (AAPC 4·9%, 2·9-7·2), with minimal disparities and all subgroups will achieve the target in the treatment domain by 2030 (possibilities higher than 90%). However, the prevention index presented poor performance with a small increase over time from 2003 to 2018 (AAPC 1·4%, 0·3-2·5) and sizable disparities across regions, urban-rural areas, and income (all p<0·0001). To achieve at least 80% UHC coverage, provinces would need to reach at least ¥445 in GHE per capita per year under maximum efficiency. However, large gaps between the observed and frontier UHC index, especially in several western provinces, indicated inefficiency. Furthermore, we found that primary health-care institutions were closely and positively related to UHC indicators especially in the prevention domain., Interpretation: Although considerable achievements have been made, the progress towards UHC is not keeping pace with the rapid society development in China. Considering an increased burden in non-communicable diseases and an ageing population, prioritising the efficacy of financial investment and optimising resource allocation by strengthening primary health care are necessary to achieve UHC in China., Funding: National Natural Science Foundation of China and National Health Commission of the People's Republic of China., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Despite the growing evidence on the relationship between community environment and depressive symptoms, little is known about the longitudinal relationship in older adults. This study aimed to examine the longitudinal association between community environment and depressive symptoms based on a nationally representative survey among Chinese middle-aged and older adults., Methods: A total of 6 872 participants from the latest four waves (2011, 2013, 2015, and 2018) of the China Health and Retirement Longitudinal Study were included. The Center for Epidemiologic Studies Depression Scale was used to measure depressive symptoms, and Cox proportional hazards regression models were conducted to investigate the longitudinal association between community environment and depressive symptoms after adjusting included covariates., Results: Both community infrastructure and organizations were significantly associated with depression, and infrastructure was associated with higher risk reduction in depression (hazard ratio [HR], 0.77; 95% confidence interval [CI], 0.66-0.89). Furthermore, we estimated the heterogeneous effects of community infrastructure and organizations for vulnerable populations. Additionally, family-based elder-care center (HR, 0.63; 95% CI, 0.46-0.86) and outdoor exercise facilities (HR, 0.76; 95% CI, 0.68-0.84) were found as significant correlates associated with lower depressive symptom risk., Conclusions: Our findings indicated that targeted and specific measures should be taken to improve the construction of community infrastructure and strengthen the role of community organizations. Specifically, providing a family-based elder-care center and establishing outdoor exercise facilities might be effective methods to reduce the risk of depression and relieve related public health burdens among Chinese middle-aged and older adults., (© The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)