Prefabricated construction has attracted worldwide concern because of its significant role in the creation of sustainable urbanization. In Mainland China, the practice of applying prefabrication ...technology in the construction industry still lags behind. In fact, the economic benefit is a key concern of various stakeholders involved in the construction process and is expected to influence the delivery of prefabricated buildings significantly. Therefore, this study established a cost–benefit analysis framework to explore the basic cost composition of prefabrication and examined the effect of adopting prefabrication on the total cost of real building projects. Results show that the concrete and steel used in the typical prefabricated components were responsible for 26% to 60% of the total cost, followed by labor cost (17%–30%) and transportation (10%). The average incremental cost is highly linearly correlated with the prefabrication rate, which ranged from 237 yuan/m2 to 437 yuan/m2, in eight building projects. To fully gain the economic benefits from the precast construction, the future focus should lie in providing financial support for promoting the development of prefabrication technology, optimizing the structure integrity of prefabricated buildings, and improving the maturity of the precast market.
•We developed a cost–benefit analysis framework to explore cost composition.•We examined the effect of adopting prefabrication on the total cost of buildings.•We explored the relationship between prefabrication rate and incremental cost.•We identified the cost difference between precast and conventional construction.•We explored the driving factors causing the cost increment in prefabrication domain.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•System dynamics approach is effective in evaluating the impact of prefabrication.•Providing subsidy has the most significant effect on promoting the use of prefabrication.•Interaction exists among ...different management measures.
Prefabrication has been widely regarded as a sustainable construction method in terms of its impact on environmental protection. One important aspect of this perspective is the influence of prefabrication on construction waste reduction and the subsequent waste handling activities, including waste sorting, reuse, recycle, and disposal. Nevertheless, it would appear that existing research with regard to this topic has failed to take into account its innate dynamic character of the process of construction waste minimization; integrating all essential waste handling activities has never been achieved thus far. This paper proposes a dynamic model for quantitatively evaluating the possible impacts arising from the application of prefabrication technology on construction waste reduction and the subsequent waste handling activities. The resulting model was validated based on an actual building project in Shenzhen, China.
The simulation results of the design scenarios indicate that the policy on providing subsidy for each square meter of the prefabrication adopted in the construction would have more significant effect on promoting the use of prefabrication and improving the performance of construction waste reduction compared to the increase of income tax benefits. The results also show that (1) interaction exists among different management measures, and (2) the combined effect of multiple policies is larger than the simple sum of their individual impacts, indicating the need for comprehensive consideration on the combined effect of these potential polices. This paper demonstrates the potential benefits of using a system dynamics approach in understanding the behavior of real-world processes. The developed model not only serves as a practical tool for assessing the impact of off-site prefabrication on construction waste reduction and the corresponding waste handling activities, but also help provide a valuable reference to policy makers through the comparison of simulation results generated under various scenarios such that the best policy mix can be identified prior to production.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper ...proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial-temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
•This paper presents a set of significant indicators for successfully reducing construction waste at the design stage.•Six significant indicators are deprived for effective implementation of waste ...minimization at the design stage.•The applicability and significance of the identified significant indicators are explored.•These significant indicators provide designers and project managers a useful criterion.•These can also sever as valuable references for the government to formulate related construction regulations.
Construction waste minimization at the design stage is a key strategy in effective waste reduction. However, it seems that few studies focus on exploratory factors that can significantly improve the design of construction waste minimization. This paper addresses this research gap by presenting a set of critical factors that inform and improve the practice of waste minimization design, particularly in the context of Shenzhen, China. Nineteen potential factors which can influence effective waste minimization are presented based on related official guidelines, reports and literature. Top institutions in Shenzhen that have received a Grade A building design certification were surveyed through a questionnaire. From this survey, six critical factors are derived: (1) large-panel metal formworks, (2) prefabricated components, (3) fewer design modifications, (4) modular design, (5) waste reduction investment and (6) economic incentive. The applicability and significance of the identified critical factors for effectively designing waste minimization are also explored. These critical factors not only provide designers and project managers with a useful set of criteria for effective design strategies to reduce construction waste, but also serve as valuable references for the government to formulate related construction waste minimization regulations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Humans show micro-expressions (MEs) under some circumstances. MEs are a display of emotions that a human wants to conceal. The recognition of MEs has been applied in various fields. However, ...automatic ME recognition remains a challenging problem due to two major obstacles. As MEs are typically of short duration and low intensity, it is hard to extract discriminative features from ME videos. Moreover, it is tedious to collect ME data. Existing ME datasets usually contain insufficient video samples. In this paper, we propose a deep learning model, double-stream 3D convolutional neural network (DS-3DCNN), for recognizing MEs captured in video. The recognition framework contains two streams of 3D-CNN. The first extracts spatiotemporal features from the raw ME videos. The second extracts variations of the facial motions within the spatiotemporal domain. To facilitate feature extraction, the subtle motion embedded in a ME is amplified. To address the insufficient ME data, a macro-expression dataset is employed to expand the training sample size. Supervised domain adaptation is adopted in model training in order to bridge the difference between ME and macro-expression datasets. The DS-3DCNN model is evaluated on two publicly available ME datasets. The results show that the model outperforms various state-of-the-art models; in particular, the model outperformed the best model presented in MEGC2019 by more than 6%.
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This article proposes a new satellite-based framework for global-scale remote sensing that is integrated with on-orbit cloud computing and artificial intelligence (AI) services. These spaced-based ...services cover the entire earth surfaces using massive low earth orbit (LEO) satellite constellation. Global-scale sensing of earth resources must be supported by massive number of LEO satellites equipped with cloud/AI computing services in real time. New satellite computer architectural features are presented along with some satellite constellation deployment topologies. We design satellite-based computers to support on-orbit remote sensing and AI scene analysis. This demands real-time performance without transmitting the sensed data back to earth for delayed processing. Notable space data services include on-orbit data sensing of large areas, machine learning from earth resources data, earth scene/event analysis, geomorphology observation, smart city management, disaster relief, global healthcare Internet of Things, environmental ecology protection, etc. We attempt to achieve high-efficiency earth resources utilization along with green energy, low cost, and robustness in real-life services.
Ag/ZnO metal–semiconductor nanocomposites with hierarchical micro/nanostructure have been prepared by the hydrothermal synthesis in the presence of bovine serum albumin (BSA). The results suggest ...that this biomolecule-assisted hydrothermal method is an efficient route for the fabrication of Ag/ZnO nanocomposites by using BSA both a shape controller and a reducing agent of Ag
+ ions. Moreover, Ag nanoparticles on the ZnO act as electron sinks, improving the separation of photogenerated electrons and holes, increasing the surface hydroxyl contents of ZnO, facilitating trapping the photoinduced electrons and holes to form more active hydroxyl radicals, and thus, enhancing the photocatalytic efficiency of ZnO. This is a good example for the organic combination of green chemistry and functional materials.
A green strategy is report to construct Ag/ZnO metal–semiconductor nanocomposites with hierarchical micro/nanostructure and enhanced photocatalytic activity.
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► Hierarchical micro/nanostructured Ag/ZnO nanocomposites have been prepared via a green route. ► Ag nanoparticles improve the separation of photogenerated electrons and holes. ► This facilitates trapping the photoinduced electrons and holes to form more hydroxyl radicals. Therefore, it enhances the photocatalytic efficiency of ZnO.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Construction is a major contributor to pollution in the natural environment. Among all construction pollutants, construction dust is the most significant pollutant that endangers human health. To ...expand the limited scope of studies on construction dust exposure, this study investigates current dust control practices in the construction industry in Hong Kong through dust monitoring data compilation. The database constructed and compiled by this study is larger than any other previous dust-related datasets in the construction industry in Hong Kong. A total of 837 samples are collected from 33 construction sites and 16 contractors, among which 783 valid samples are analyzed. Descriptive statistics are reported in terms of “construction process”, “trade”, “tools”, “data source type”, and “dust control measure”. Overall geometric mean for personal exposures are 0.314 (geometric standard deviations: 3.929) mg/m3 for respirable dust and 0.003 (geometric standard deviations: 5.105) mg/m3 for quartz concentration. It is found that the top three dust respirable exposures are from cement mixing, concrete breaking, and manual demolition, whereas grinding and rock breaking are recorded the two highest processes in terms of quartz exposure. Analytical results indicate that respiratory protection commonly used on construction sites is often inadequate for exposures encountered. Data variability within task and tool is large, with high exposures reported for a broad spectrum of tools. This study attains a large dataset to represent as many dust production construction activities as possible, determining the most influential factors for predicting dust exposure and evaluating the effectiveness of current dust control practices. This knowledge should, in turn, lead to enhanced practices of dust control in Hong Kong and highlight areas that need improvements.
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Enhanced photovoltaic performance of a DSSC using graphene-TiO
2
photoelectrodes prepared by our recent
in situ
simultaneous reduction-hydrolysis technique (
Adv. Funct. Mater.
, 2012, DOI: ...10.1002/adfm.201202349, in press) was achieved. The DSSCs based on the G-TiO
2
nanocomposites improved their overall energy conversion efficiency to 7.1%. The results prove that the promoting effect of graphene is strongly dependent on its content; namely, the efficiency of DSSCs increases and then decreases with increasing graphene content in TiO
2
-graphene composites. Excessive graphene in the nanocomposite leads to a decrease of the light harvest of dye molecules and thus a negative effect on the power conversion efficiency of DSSCs.
Enhanced photovoltaic performance of a DSSC using graphene-TiO
2
photoelectrodes prepared by our recent
in situ
simultaneous reduction-hydrolysis technique was achieved.
Global navigation satellite systems (GNSSs) applied to intelligent transport systems in urban areas suffer from multipath and non-line-of-sight (NLOS) effects due to the signal reflections from ...high-rise buildings, which seriously degrade the accuracy and reliability of vehicles in real-time applications. Accordingly, the integration between GNSS and inertial navigation systems (INSs) could be utilized to improve positioning performance. However, the fixed GNSS solution uncertainty of the conventional integration method cannot determine the fluctuating GNSS reliability in fast-changing urban environments. This weakness becomes solvable using a deep learning model for sensing the ambient environment intelligently, and it can be further mitigated using factor graph optimization (FGO), which is capable of generating robust solutions based on historical data. This paper mainly develops the adaptive GNSS/INS loosely coupled system on FGO, along with the fixed-gain Kalman filter (KF) and adaptive KF (AKF) being taken as comparisons. The adaptation is aided by a convolutional neural network (CNN), and the feasibility is verified using data from different grades of receivers. Compared with the integration using fixed-gain KF, the proposed adaptive FGO (AFGO) maintains the 100% positioning availability and reduces the overall 2D positioning error by up to 70% in the aspects of both root mean square error (RMSE) and standard deviation (STD).
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