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Production forecast, comprehensive utilization, management measures and visualization analysis of construction waste
... For building rubbish, landfill and open-pile treatment have caused ecological damage. The development of new types of buildings has also led to the overuse of natural materials [3,4]. Thus, improving the use of solid waste and promoting the green treatment of construction waste are key issues in construction [5,6]. ...
A green, low-carbon concrete is a top way to recycle waste in construction. This study uses industrial solid waste slag powder (S95) and fly ash (FA) as binders to completely replace cement. This study used recycled coarse aggregate (RCA) instead of natural coarse aggregate (NCA). This is to prepare alkali-activated recycled concrete (AARC) with different cementitious material systems. Alkali-activated concrete (AAC) mixtures are modified for strength and performance based on the mechanical qualities and durability of AARC. Also, the time-varying effects of the environment on AARC properties are explored. The results show that with the performance enhancement of RCA, the mechanical performance of AARC is significantly improved. As RCA’s quality improves, so does AARC’s compressive strength. At a cementitious material content of 550 kg/m3, AARC’s 28d compressive strengths using I-, II-, and III-class RCA were reduced by 2.2%, 12.7%, and 21.8%, respectively. I-class AARC has characteristics similar to natural aggregate concrete (NAC) in terms of shrinkage, resistance to chloride penetration, carbonization, and frost resistance. AARC is a new type of green building material that uses industrial solid waste to prepare alkali-activated cementitious materials. It can effectively reduce the amount of cement and alleviate energy consumption. This is conducive to the reuse of resources, environmental protection, and sustainable development.
With the continuous increase in the demand for electricity in our country, the reliability requirements of the power distribution network are gradually increasing, and the difficulty of predicting the state of key equipment in the power distribution network is also increasing. In response to this issue, this paper proposes a typical reliability prediction model for key equipment in the power distribution network based on an unbiased grey correlation model. Firstly, a grey model is established to model the reliability of typical key equipment in the power distribution network, and to conduct reliability analysis and life prediction. Secondly, an unbiased grey model is introduced to avoid the problem of the grey model leading to less than ideal life prediction accuracy when the growth rate of the original data sequence is high. Finally, taking the No. 1 main transformer of the 220 kV Lanshan substation under the jurisdiction of Ningxia Shizuishan Power Supply Company as an example, the oil chromatography data is cleaned using the KNN interpolation method and input into the prediction model to analyze and predict the failure rate during actual operation. The results show that this model has certain advantages in terms of life prediction accuracy and calculation time, and can play an auxiliary role in the process of predicting the state of key equipment in the power distribution network.
The production of tight gas wells decreases rapidly, and the traditional method is difficult to accurately predict the production of tight gas wells. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some technical problems. For example, the traditional error back propagation neural network (BP) still has the problem of finding the local optimal value, resulting in low prediction accuracy. In order to solve this problem, this paper establishes the output prediction method of BP neural network optimized with the sparrow search algorithm (SSA), and optimizes the hyperparameters of BP network such as activation function, training function, hidden layer, and node number based on examples, and constructs a high-precision SSA-BP neural network model. Data from 20 tight gas wells, the SSA-BP neural network model, Hongyuan model, and Arps model are predicted and compared. The results indicate that when the proportion of the predicted data is 20%, the SSA-BP model predicts an average absolute mean percentage error of 20.16%. When the proportion of forecast data is 10% of the total data, the SSA-BP algorithm has high accuracy and high stability. When the proportion of predicted data is 10%, the mean absolute average percentage error is 3.97%, which provides a new method for tight gas well productivity prediction.
Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: ‘delayed graft function’, ‘kidney transplantation’, ‘renal transplantation’, ‘survival’, and ‘recipients’. These emerging trends include ‘brain death donors’, ‘blood absence re-injection injuries’, ‘tacrolimus’, ‘older donors and recipients’, and ‘artificial intelligence and DGF’. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF.
With the development of economic globalization, the issue of industrial upgrading has gradually become the focus of scholars. This paper provides a systematic review of academic research focused on industrial upgrading. Using CiteSpace 6.1.R 5 visualization tools, we collected 1535 articles from the Web of Science database over 30 years. This paper identified the most influential publications, authors, journals, institutions, and countries in this field. Additionally, this paper analysed the structures of cited references, cited authors, and cited journals to gain insights into theoretical foundations. Lastly, the collective results obtained using CiteSpace will contribute to a better understanding of industrial upgrading in the form of a “Scientific and technological innovation evaluation index system in China” for both theorists and practitioners. This paper combines bibliometric methods with systematic reviews to help researchers and practitioners better understand the development of industrial upgrading.
Aim:
This study was conducted to understand the research hotspots, research trends and research frontiers for hypothermia in surgical patients and provide a reference for developing effective measures to reduce the incidence of hypothermia in these patients.
Design:
Using CiteSpace software, a visual analysis of 1288 included articles related to hypothermia in surgical patients was performed.
Methods:
CiteSpace software is a Java application that supports knowledge research, data visualization and analysis of literature databases. The following data were retrieved: title, abstract, year, keywords, author, academic institution, journal and citations. The included literature was analysed using CiteSpace visualization software, knowledge mapping, collaborative network analysis, cluster analysis and highlighting keywords for visualization.
Results:
A total of 1288 articles related to hypothermia in surgical patients were included, with the largest number of articles published in the United States (448), followed by in Germany (104) and China (102). The top three institutions, journals and authors were from the United States. The most common keywords were perioperative 'hypothermia', 'risk factors' and 'anesthesia'.
There have been efforts to use building demolition waste as an alternative aggregate in concrete to decrease the use of natural resources for construction. The World Green Building Council estimates that the construction industry is responsible for more than 50% of all material extracted globally and that construction and demolition waste makes up 35% of global landfills. As a result, incorporating recycled aggregate (RA) in concrete production is a prudent course of action to reduce the environmental impact. This study reviews prior research on using recycled aggregate instead of conventional ingredients in concrete. The composition and morphology of different types of RA, the behavior of RA in fresh and hardened states, keyword co-occurrence and evolution analysis, and the various additives used to enhance the inferior properties of RA are discussed. The RA showed different physical properties when compared with natural aggregate. However, the addition of pozzolanic materials and various pretreatment techniques is desirable for improving the inferior properties of RA. While building waste has been utilized as a substitute for fine and coarse aggregate, prior research has demonstrated that a modified mixing approach, an adequate mixing proportion, and the optimum replacement of cementitious materials are necessary. Based on the review, the recommendation is to use RA at a replacement level of up to 30% and the addition of precoated and pozzolanic materials as a treatment to provide concrete with adequate workability, strength, and durability for structural applications.
In the current age of enhanced environmental awareness, transformation to sustainable management in the construction sector is needed. China currently produces the largest amount of construction and demolition (C&D) waste around the world, but the average recovery rate of the waste was only about 5% in 2017. In order to investigate problems in current C&D waste management in China, a cross-national comparative analysis is conducted among China and seven selected countries (Japan, South Korea, Germany, Italy, Austria, the Netherlands and the United Kingdom), to compare legal texts of national policies and laws which relate to C&D waste management and are currently being used. Through the comparison, problems in management of C&D waste in China are investigated. The problems could be concluded to: (a) inadequate guidance on recycling, (b) lack of market incentives in utilising recycled materials, (c) incomplete knowledge of stakeholders’ responsibilities, (d) lack of penalty for other stakeholders, and (e) inefficient supervision system. By understanding these problems, this paper further provides recommendations to enhance the performance of C&D waste management in China.
The rapidly increased generation of construction and demolition (C&D) waste hinders the sustainable development of cities. Establishing an effective C&D waste management system is of great importance for achieving sustainable development goals. The quantification and prediction of C&D waste, forming the basis of waste management, are worthy of further exploration. C&D waste generation is time series data in which future waste generation is closely correlated with past ones. This study proposes a time-series waste prediction framework to predict C&D waste generation with less data volume by coupling generation rate calculation (GRC) and autoregressive integrated moving average (ARIMA) model. It is demonstrated in Sichuan, China, as a case study. The prediction result reveals that C&D waste generation in Sichuan shows an overall increasing trend, and the waste is mainly generated in the central of Sichuan. Chengdu accounts for over 40% of the total generation in the province, followed by Luzhou, Nanchong, and Mianyang. C&D waste generation shows a significant continual rise in Yibin and Zigong. Overall, most cities in Sichuan have issued related policies and tried to strengthen control over the transportation phase. This study provides an alternative to predict and analyze C&D waste generation from spatiotemporal perspectives. It enriches the C&D waste generation data and provides quantification support for C&D waste management at the regional level.
Urban material stock (UMS) represents an elegant thinking by perceiving cities as a repository of construction materials that can be reused in the future, rather than a burdensome generator of construction and demolition waste. Many studies have attempted to quantify UMS but they often fall short in accuracy, primarily owing to the lack of proper quantification methods or good data available at a micro level. This research aims to develop a simple but satisfactory model for UMS quantification by focusing on individual buildings. Generally, it is a ‘bottom-up’ approach that uses building features to proximate the material stocks of individual buildings. The research benefits from a set of valuable, ‘post-mortem’ ground truth data related to 71 buildings that have been demolished in Hong Kong. By comparing a series of machine learning-based models, a multiple linear regression model with six building features, namely building type, building year, height, perimeter, total floor area, and total floor number, is found to yield a satisfactory estimate of building material stocks with a mean absolute percentage error of 9.1%, root-mean-square error of 474.13, and R-square of 0.93. The major contribution of this research is to predict a building’s material stock based on several easy-to-obtain building features. The methodology of machine learning regression is novel. The model provides a useful reference for quantifying UMS in other regions. Future explorations are recommended to calibrate the model when data in these regions is available.
Construction waste management is a global concern not only because it impacts the financial efficiency of construction projects, but also because of its negative influence on the environment. The construction industry is a major contributor to environmental pollution due to its carbon footprint and greenhouse gas emissions. Thus, effective construction waste management solutions are required for sustainable development by preventing material waste. Hence, this study employed the Analytical Hierarchy Process to prioritize the possible construction waste, factors leading to its generation and the most effective IR 4.0 solution. A questionnaire was constructed, and after refinement, it was then distributed among the engineers, contractors, professors, and other industry professionals. The results from the analysis provide us with the list of factors ranked on their comparative weightage and score. The wastage of cement due to moisture is found to be the highest ranked potential waste. Moreover, changes in orders by the client are termed as the most highly ranked cause of rework and material wastage. Similarly, the Industrial Building Systems are the best solution for efficient construction waste management that Industrial Revolution 4.0 can provide. The results of this study can help to enhance project control by providing information on possible construction wastes and the factors that lead to their generation.
Urbanization, population growth, increased consumption, infrastructure, and housing needs are some of the factors that result in increased waste. Recycling has been a crucial way to reduce the amount of materials that end up in disposal sites and is how citizens, more aware of the impacts on the environment, participate in some of the schemes to reduce waste. Zero waste is an approach developed to preserve the finite resources available, but major barriers are hindering its efficient and effective implementation. This study intended to unveil those barriers in the Costa Rican construction sector and to propose measures to increase the recycling rates. In order to achieve the objective, construction companies, cement producers, waste managers, personnel of refuse material facilities, waste transformers, construction material distributors, and a director of the Ministry of Health were interviewed to determine the challenges in the valorization of concrete, wood, metal, and packaging waste materials. This article reports the findings, which include, among others, the fact that most construction companies dispose the waste without any separation, except for metals. The cement producer companies do not participate in any form of collection system in spite of the available technology and equipment and the absence of innovative technologies for the transformation of materials.
With the continuous promotion of urbanization, the generation of construction and demolition waste (CDW) is increasing. The environmental problems and safety hazards caused as a result need to be resolved. In this paper, based on the system dynamics (SD) theory, the modeling, the cost, and the environmental benefit of CDW resource management under the life cycle assessment (LCA) are proposed. Specifically, we propose a combined policy derived through three variables, namely, fines, subsidies, and charges. The target is to reduce illegal dumping behavior and landfill volume and to improve the recycling volume and environmental benefits. The model is constructed with the help of the software VENSIM, and the validity and feasibility of the model are demonstrated with data from Nantong City. The results show that a single policy cannot simultaneously improve environmental benefits, illegal dumping, recycling, and landfill behavior. A combined policy combines the advantages of three single policies, fines, subsidies, and charges, which not only can effectively curb illegal dumping and landfill disposal under the premise of prioritizing environmental benefits, but can also promote the recycling of CDW. The reasonable range for the fine is 300–350 CNY/ton; the rational range for subsidies is 30–40 CNY/ton; and the flexible range for treatment charge is 40–80 CNY/ton. The model can be used for the quantitative assessment of urban CDW management costs and environmental benefits and can also provide a theoretical basis for the government.
Conditional Direction Dependence Analysis (CDDA) has recently been proposed as a statistical framework to test reverse causation (x → y vs. y → x) and potential of confounding (x ← c → y) of variable relations in linear models when moderation is present. Similar to standard DDA, CDDA assumes that the “true” predictor is a continuous, non-normal, exogenous variable. Under non-normality, a conditional causal effect of one variable does not only change means, variances, and covariances, but also the distributional shape (i.e., skewness, kurtosis, co-skewness, and co-kurtosis) of another variable given the moderator. Such distributional changes can be used to study underlying mechanisms of heterogenous causal effects. The present study introduces conditional direction of dependence modeling and presents SPSS macros to make CDDA easily accessible to applied researchers. A real-world data example from the field of gambling addiction research is used to introduce the functionality of CDDA SPSS macros. Limitations of CDDA due to violated assumptions and poor data quality are discussed. The CDDA installation package is available at no charge from www.ddaproject.com.
Timely and accurate recognition of construction waste (CW) composition can provide yardstick information for its subsequent management (e.g., segregation, determining proper disposal destination). Increasingly, smart technologies such as computer vision (CV), robotics, and artificial intelligence (AI) are deployed to automate waste composition recognition. Existing studies focus on individual waste objects in well-controlled environments, but do not consider the complexity of the real-life scenarios. This research takes the challenges of the mixture and clutter nature of CW as a departure point and attempts to automate CW composition recognition by using CV technologies. Firstly, meticulous data collection, cleansing, and annotation efforts are made to create a high-quality CW dataset comprising 5,366 images. Then, a state-of-the-art CV semantic segmentation technique, DeepLabv3+, is introduced to develop a CW segmentation model. Finally, several training hyperparameters are tested via orthogonal experiments to calibrate the model performance. The proposed approach achieved a mean Intersection over Union (mIoU) of 0.56 in segmenting nine types of materials/objects with a time performance of 0.51 s per image. The approach was found to be robust to variation of illumination and vehicle types. The study contributes to the important problem of material composition recognition, formalizing a deep learning-based semantic segmentation approach for CW composition recognition in complex environments. It paves the way for better CW management, particularly in engaging robotics, in the future. The trained models are hosted on GitHub, based on which researchers can further finetune for their specific applications.
China’s 14th Five-Year Plan, for the period 2021–25, presents a real opportunity for China to link its long-term climate goals with its short-to medium-term social and economic development plans. China’s recent commitment to achieving carbon neutrality by 2060 has set a clear direction for its economy, but requires ratcheting up ambition on its near-term climate policy. Against this background, this paper discusses major action areas for China’s 14th Five-Year Plan after COVID-19, especially focusing on three aspects: the energy transition, a new type of sustainable urban development, and investment priorities. China’s role in the world is now of a magnitude that makes its actions in the immediate future critical to how the world goes forward. This decade, 2021–2030, is of fundamental importance to human history. If society locks in dirty and high-carbon capital, it raises profound risks of irreversible damage to the world’s climate. It is crucial for China to peak its emissions in the 14th Five-Year Plan (by 2025), making the transition earlier and cheaper, enhancing its international competitiveness in growing new markets and setting a strong example for the world. The benefits for China and the world as a whole could be immense.
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Prefabrication has long been recognized as a green production technology to minimize construction's adverse environmental impacts such as waste, noise, dust, and air pollution. Previous studies reported the effects of prefabrication on construction waste minimization. However, these studies relied primarily on small data obtained by ethnographic methods such as interviews and questionnaire surveys. Research to evaluate the effects using bigger, more objective quantitative data is highly desired. This research aims to re-evaluate the effects of prefabrication on construction waste minimization by exploiting a quantitative dataset stemmed from 114 sizable high-rise building projects in Hong Kong. It was discovered that the average waste generation rates of conventional and prefabrication building projects were 0.91 and 0.77 ton/m² respectively. Compared with conventional construction, prefabrication logged a 15.38% waste reduction. Further probing into specific prefabricated components adopted in the samples, it is discovered that precast windows and walls are more conducive to waste minimization. This is coincident with the fact that these components are also widely adopted in the sample buildings. This study reconfirms the positive effects of prefabrication on waste minimization and articulates that two types of prefabricated components play relatively bigger role in minimizing construction waste. The strengths of this study lie in its statistical analyses of a valuable and objective quantitative dataset measuring prefabrication and waste generation rates. Future studies are recommended to prove the corollary - it is not what category of prefabricated component, but the actual proportion of prefabrication in the total construction volume that matters to waste minimization.
The rapid development of urbanization has increased the number of urban construction projects, resulting in a large number of construction waste, and improper disposal of construction waste is likely to lead to safety problems and environmental risks. In order to reduce or even eliminate the recurrence of such incidents, this paper, based on the analysis of the current situation at home and abroad, puts forward how to improve the whole process management mode of construction waste from three aspects: large data acquisition and processing, spatial and temporal characteristics analysis and evolution mining of main body behavior rules and model establishment. The improvement of the whole process management mode of construction waste proposed in this paper will further improve the deficiencies in the current management of construction waste, so as to better realize the standardized management of construction waste and make the construction waste fully utilized and reused safely and efficiently.
Rapid development in construction industry had caused the increasing of construction waste. This situation brings many negative impacts towards the environment, cost, productivity, time, social and economy. The increasing of construction wastes is mainly due to the inefficient waste management practices in the construction projects. Hence, the effective management practices in handling construction waste should be highlighted. This study seeks to identify the waste management practices that are able to engender on-site waste minimization. As much as 54 practices had been found from previous research publications. Mapping technique was used to determine the frequency for each practice. This study have found five (5) significant management practices that need to be emphasized in order to achieve on-site waste minimization. The findings of this paper will help researchers to further investigate the significant management practices in minimizing on-site construction waste.
Estimation of construction waste generation is critical to construction waste management decisions. However, current construction waste estimation methods have various limitations (e.g. small samples). To address those limitations, this research conducts an empirical study to evaluate the waste generation rate of different types of waste at different construction stages. In this study, construction waste from 148 new-built residential construction sites in China were sorted and weighted on site and their waste generation rates were estimated separately. The results indicated that the amount of inorganic nonmetallic waste with a generation rate of 16.59 kg m ⁻² was the highest among the five types of waste (i.e. inorganic nonmetallic waste, organic waste, metallic waste, composite waste, hazardous waste), while the waste generation rate for the underground construction stage, which was 27.57 kg m ⁻² , was the highest among the three stages (i.e. underground stage, superstructure stage, finishing stage). Compared with previous data, the new waste generation rate proposed in this research can better estimate the actual waste generation situation in construction sites, which provides reliable information for proper decision-making. Furthermore, based on the result of the empirical study, some recommendations for construction waste reduction are proposed.
Rapid growth in construction activities as a result of a growing population and urbanization in many parts of the world generates a large amount of waste from construction. To reduce and manage these wastes, a comprehensive understanding of the construction waste generation factors is needed. The purpose of this study is to identify the contributing factors of construction waste in Thailand’s construction industry. The causes of construction waste were identified through an extensive literature review. A total of 28 causes of construction waste were identified and grouped into the four categories: design and documentation, material and procurement, construction method and planning, and human resources. To determine the significant level of each factor, a structured questionnaire survey was carried out to gather information from contractors about causes of construction material waste. The results show that the categories contributing to construction waste ranks as design and documentation, human resources, construction methods and planning, and material and procurement, respectively. Meanwhile, factors from each category were also determined and ranked. Design change, inattentive working attitudes and behaviors, ineffective planning and scheduling, and material storage were among the highest impact factors on construction waste generation in each category. Identifying the significance levels of waste generation factors will help the industry’s stakeholders build suitable strategies to manage construction waste more effectively.
The environmental impacts caused by construction waste have attracted increasing attention in recent years. The effective management of construction waste is essential in order to reduce negative environmental influences. Construction waste management (CWM) can be viewed as a complex adaptive system, as it involves not only various factors (e.g., social, economic, and environmental), but also different stakeholders (such as developers, contractors, designers, and governmental departments) simultaneously. System dynamics (SD) and agent-based modeling (ABM) are the two most popular approaches to deal with the complexity in CWM systems. However, the two approaches have their own advantages and drawbacks. The aim of this research is to conduct a comprehensive review and develop a novel model for combining the advantages of both SD and ABM. The research findings revealed that two options can be considered when combining SD with ABM; the two options are discussed.
In the construction waste output forecast, it is difficult for forecasting and reducing the accuracy of forecasting. In this paper, a method for estimating the yield of urban construction waste is built on the basis of building area, by using the ARIMA model to predict demolition area and the area of the building was completed, from the empirical coefficient method and indirect method to predict for construction waste production. Haikou as an example to calculate and predict the 2015-2020 years of urban construction waste output. Overvie w of related forecasting methods for construction waste In recent years, with the accelerated pace of urban construction, construction waste will be generated with a large number of production. According to the source of construction waste and the way of producing, construction waste can be divided into three categories, in the construction site of construction waste, public buildings and residential decorative decoration construction waste and housing demolition (1) . The muck from construction sites can basically reach the balance of supply and demand. The processed resources mainly for decoration waste, demolition waste and garbage remaining under construction. At present, the prediction model of construction waste generated mainly includes Multiple Regression Model, Grey Model and Time Series Prediction. Multiple Regression Model is used to combine the urban population, urban residents' income, and then to predict the future construction waste output (2) . Grey system processing is using a certain mathematical method to eliminate the influence which based on the analysis of the observed data the establishment of the system can make a prediction of the Gray Model (3) . Through the process of forecasting target's own time series to study the forecast target itself on the change trends, so the prediction of the future of the data is a Time Series Model Prediction method (4) . These existing estimation method, however, there are factors to consider not to comprehensive, the data quantity demand is high or relatively poor commonality problem and so forth. Therefore, it is necessary to build a set of general method for the estimation of urban construction waste output based on the existing conditions. The ARIMA model based on time series analysis method in this paper, introducing the difference parameters , in order to solve the data loss caused by the imbalance in non-stationary time series.
The stock of building materials has critical impact on sustainable development and on the reduction of the carbon emissions of the construction industry; however, the accumulated stock of building materials and its carbon emissions remain uncertain. To clarify the stock situation regarding existing buildings in China and provide reference material to support reduction of building–related carbon emissions, the accumulated building material stock and associated carbon emissions were visualized and quantified in this study based on building vector data of 33 cities and nighttime light data. In 2017, 88.3 Gt of building materials in 161.1 billion m3 of accumulated building stock produced emissions of 17.8 Gt CO2eq. Concrete and cement represented the main material types and the greatest sources of emissions. Notable disparities were found in material stock among different regions, determined primarily by factors such as areas of urban and rural construction land, population, GDP, and landform. Spatial visualization of material carbon emission intensity in China revealed a pattern of “higher in the southeast, lower in the north and midland,” and showed three aggregation modes. The results of this study could serve as scientific reference for new policies regarding low–carbon building, thereby promoting carbon emission reduction and sustainable utilization of resources in the construction industry.
Energy savings and emission reductions in the construction industry are an important mean of achieving China’s
goals of carbon peaking and carbon neutrality. Management of debris and waste produced during construction is
particularly important to achieve the construction industry’s carbon emission reduction goals. This study constructs
an (SD) model of the management of C&DW carbon emissions under different sorting modes to explore
the relationship among factors affecting carbon emissions produced during C&DW management and to investigate
the carbon emission reduction effects of different policies. The SD model simulates the effects of different
carbon emission reduction policies and proposes optimization suggestions for the simulation results, which show
that: (1) When the subsidy is set at 25CNY/t, there is a significant increase in the propensity of contractors to
choose the resource sorting mode and a significant decrease in total carbon emissions of C&DW treatment. (2)
When using a carbon tax as a policy tool to regulate the choice of C&DW sorting mode, there is a significant
increase in the tendency of contractors to choose the resource-based sorting mode. The findings of the study can
provide a basis for the government to introduce relevant C&DW management and emission reduction policies.
The reduction of construction and demolition waste (C&DW) has a significant impact on the sustainable development of the construction industry. Differentiated reduction management policy is the key to C&DW reduction management. Based on the calculation of C&DW generation, this paper studies the temporal and spatial evolution characteristics, heterogeneity of C&DW with economic growth, and driving factors of C&DW generation in 30 provinces from 2007 to 2018 by using the methods of standard deviation ellipse model, environmental Kuznets curve (EKC) and geographic detector. The results show that the C&DW generation in China shows an increasing trend, “low in the west, and high in the East” and “high in the South and low in the north,” with significant regional differences. The temporal and spatial distribution of C&DW generation shows a “northeast southwest” trend, and this pattern has a trend of changing to “due north - due south”; C&DW generation increases with the economic growth, and the proportion of “inverted N” provinces is large, which is in the stage of continuous increase in the C&DW generation; The economic output effect of construction industry is the most important factor affecting the spatial distribution of C&DW generation. The impact of C&DW generation in each province has shifted from the single core driving effect of economy to the common guidance of economy and environmental protection. The interaction of different factors has a greater impact on the spatial distribution of C&DW generation than that of each factor alone.
The location of a construction waste disposal facility is a vital part of recycling construction waste strategy. Many factors affect the location especially, ignoring uncertain factors can lead to inaccurate results, resulting in an inappropriate location. Therefore, waste supply and transportation cost, as two uncertain factors, were highlighted in current study. Based on the traditional nominal facility location model approach and considered the uncertainty of two parameter intervals. Thus, a novel robust facility location model is established in the hybrid programming of CPLEX and MATLAB. Results show that: (1) the supply uncertainty costs of construction waste transfer stations and transportation significantly affected the total costs of waste disposal, the total costs of location and site allocation scheme of construction waste disposal facilities were more sensitive to changes in waste supply. (2) As the combination value of the uncertainty level parameter increases (2%, 7%, 10%), the optimal total cost sum and α also increases, and its changing trend is from obvious to slow. (3) The optimal cost of the distribution and disposal facility layout of the construction waste transit station was determined taking Guangzhou for example. This study provided a scientific and effective method for the location of construction waste disposal facilities by considering multiple uncertain factors. This can also help stakeholders reduce the costs of the distribution net and choose appropriate interval value according to preference for risk.
Underground landfill, the primary disposal method of construction waste in several areas, negatively affects the surrounding environments. Suitably locating and estimating waste volume in an underground landfill are vital for adequate disposal and recycling of construction wastes. In this study, we investigated the applicability of ground penetrating radar (GPR) technology to estimate waste depth and volume of a construction waste landfill. The results revealed the following. (1) The GPR technology effectively delineated boundaries between underground waste and the surrounding strata; the topographic structure obtained from the analysis of the associated images was consistent with the actual topography. (2) Layer information from GPR images and electromagnetic wave velocity calculated using the complex refractive index model for construction waste burial depth inversion produced highly accurate results. Waste depth in the landfill was estimated using the GPR inversion results and spatial interpolation. Kriging interpolation exhibited the highest accuracy. (3) The trapezoid, Simpson and Simpson 3/8 rules were suitable for estimating construction waste volume. A three-dimensional model created using the spatial interpolation grid precisely depicted the structure of the buried landfill. Our study provides references for the management, recycling and environmental impact assessment of construction waste.
Construction and demolition activities generate huge quantities of waste with substantial impacts on environment. This mini-review article covers the literatures relating to construction and demolition waste management practice in Australia. The Scopus search engine was used in literature search and 26 journal articles relating to construction and demolition waste management in Australia were targeted for analysis. Additionally, various government acts, regulations, policies, and strategy documents were collected and analyzed. This review indicated that the inconsistencies in legislation and landfill levies across states and territories contributed to the cross-jurisdiction waste movement. Given the stakeholders’ attitude and project life cycle, this review reported that the design phase had the greatest potential to minimize waste and that the role of designers had been highlighted in various empirical studies. This review provides practitioners and academics with an understanding of the current construction and demolition waste management research in Australia and recommends directions for future research.
China produced a large amount of construction and demolition (C&D) waste, owing to the rapid development of construction industry. Although a set of policies and regulations are being drafted in China for promoting C&D waste recycling, execution of these policies in practice seems to be far from effective. Currently, approximately 75% of Chinese cities are still surrounded by large volumes of C&D waste. Therefore, identification of challenges in the development of C&D waste management, specially recycling, is essential. This paper employs site visits to 10 recycling plants in 10 Chinese cities (Shanghai, Hangzhou, Suzhou, Chongqing, Chengdu, Xi’an, Changsha, Shenzhen, Nanjing, and Zhoukou) and interviews with 25 industry practitioners for examining the challenges. Eight challenges are identified: (1) unstable source of C&D waste for recycling, (2) absence of subsidies for recycling activities and high cost for land use, (3) insufficient attention paid to design for waste minimisation, (4) absence of regulations on on-site sorting, (5) unregulated landfill activities, (6) a lack of coordination among different government administration departments, (7) a lack of accurate estimation of waste quantity and distribution, and (8) a lack of an effective waste tracing system. Recommendations to address these challenges are presented. The results of this study are expected to aid policy makers in formulation of proper C&D waste management in China and provide a useful reference for researchers who are interested in C&D waste recycling industry.
It has been observed that the massive urbanization has boosted up infinite construction in the developed as well as developing countries. The construction and demolition waste has been correspondingly increased enormously which results in nasty and fatal impacts on urban sustainability and survival in the term of economic values and environmental safety. Considering construction and demolition waste management (CDWM) in the USA and China and its comparison has not been discussed, this study explores some research questions to fill such gaps: What are the existing CDWM policies and regulations in these two countries? What is the market mode for CDWM? What are the key challenges of CDWM? What are the CDWM contribution and limitations toward circular economy? What are the lessons that must be exemplary for the two economies through mutual learning? Our results show that the CD waste generation and its management are influenced by several factors including population, urbanization, gross domestic product (GDP), and CDWM regulatory measures. The USA has more developed CDWM system. Whereas, China is a growing economy and it has some management deficiencies in the construction industry. Key suggestions for improving CDWM include: i. Government supervision along with an economic incentive approach, ii. Interaction between Stakeholders, iii. Mutual coordination among operational departments, iv. Audit and inspection setup, and v. Continuous development and integration of emerging technologies.
In this paper, taking the South China area as an example, a list of construction wastes has been established based on the bill of quantities, and a calculation method for the production of construction waste of new residential projects has been proposed. According to the existing research data, the scrap rate of the main materials is obtained, and the waste production of the newly-built project is estimated, which is 0.326 m³per unit area. The model in this article estimates the amount of construction waste and promotes the classification and reduction of construction waste at the construction site. It is recommended to adopt source reduction measures, implement a classification system and improve relevant laws and regulations, as well as optimize the waste market. Meanwhile, this model includes waste production budget into bidding, providing reference for improving the management level of domestic construction departments.
Urban construction production increases significantly in China because of the continuous speed and large scale of urbanization. Accordingly, environmental pollution caused by construction waste intensifies. With the growing significance of ecological civilization construction, urban development projects are under pressure to apply energy-saving and environmentally friendly methods. Recycling of construction waste resources can effectively reduce the environmental pollution of such wastes, thereby achieving sustainable urban development. To further analyse environmental pollution hazards caused by urban construction wastes, construction waste resource recycling measures were proposed. A case study based on central China was conducted and extensive studies on construction waste recycling in the context of developed territories (Europe and America) were reviewed. Environmental pollution damage caused by urban construction wastes was also identified. Then, the environmental pollution status generated by urban construction wastes was analysed and the causes of urban waste recycling barriers were summarized. Finally, measures for urban construction waste recycling were proposed. Results show that America, Japan, and Germany have achieved high construction waste recycling rates. Environmental pollution hazards from urban construction waste are manifested by large-scale occupation of land resources, resulting in intensifying domestic water, soil, and air pollution. High waste production and low comprehensive utilization rate of construction waste caused by urban construction scale are two aspects of current urban construction waste pollution. The main causes of the low recycling rate of urban construction wastes involve a lack of supporting laws and regulations as well as industrial policies, low market shares of construction-waste recycling products, poor coordination of key nodes in the industrial chain, and low benefits of recycling products. Research conclusions provide good references for improving the overall development level of urban construction recycling, facilitating continuous development of construction waste recycling industrialization, and formulating construction waste recycling policies and development plans in other regions in China.
Purpose
Prediction problems raised in uncertain environments require different solution approaches such as grey prediction models, which consider uncertainty in information and also enable the use of small data sets. The purpose of this paper is to investigate the comparative performances of grey prediction models (GM) and Markov chain integrated grey models in a demand prediction problem.
Design/methodology/approach
The modeling process of grey models is initially described, and then an integrated model called the Grey-Markov model is presented for the convenience of applications. The analyses are conducted on a monthly demand prediction problem to demonstrate the modeling accuracies of the GM (1,1), GM (2,1), GM (1,1)-Markov, and GM (2,1)-Markov models.
Findings
Numerical results reveal that the Grey-Markov model based on GM (2,1) achieves better prediction performance than the other models.
Practical implications
It is thought that the methodology and the findings of the study will be a significant reference for both academics and executives who struggle with similar demand prediction problems in their fields of interest.
Originality/value
The novelty of this study comes from the fact that the GM (2,1)-Markov model has been first used for demand prediction. Furthermore, the GM (2,1)-Markov model represents a relatively new approach, and this is the second paper that addresses the GM (2,1)-Markov model in any area.
For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction.
It is difficult to manage collective housing; thus, firefighting equipment and architectural facilities of many collective housing units fail to comply with legal requirements. When a fire disaster occurs, it is impossible for such equipment and facilities to conduct early detection and immediate response. Consequently, the development of a fire has significant influence on the escape and rescue of the inhabitants. To achieve the objectives of reducing the fire incidence of collective housing and protecting the safety of the public, strengthen public safety in collective housing buildings, and consider costs, this study used Statistical Product and Service Solutions (SPSS) to induce and analyze the factors of fire damage to collective housing buildings. This study analyzed and screened the core and critical factors of fire damage as seminar topics in order to improve public awareness of fire prevention and emergency response capabilities, and reduce incidences of fire.
Current waste generation from the construction and demolition industry (C&D industry) in Norway is about 1.25 million tonnes per year. This article presents a procedure for projection of future waste amounts by estimating the activity level in the C&D industry, determining specific waste generation factors related to this activity, and finally calculating projections on flows of waste materials leaving the stocks in use and moving into the waste management system. This is done through a simple model of stocks and flows of buildings and materials. Monte Carlo simulation is used in the calculations to account for uncertainties related to the input parameters in order to make the results more robust. The results show a significant increase in C&D waste for the years to come, especially for the large fractions of concrete/bricks and wood. These projections can be a valuable source of information to predict the future need for waste treatment capacity, the dominant waste fractions, and the challenges in future waste handling systems. The proposed method is used in a forthcoming companion article for eco-efficiency modeling within an evaluation of a C&D waste system.
This paper focuses on the accumulation of construction waste generated throughout the erection of new residential buildings. A special methodology was developed in order to provide a model that will predict the flow of construction waste. The amount of waste and its constituents, produced on 10 relatively large construction sites (7000-32,000 m(2) of built area) was monitored periodically for a limited time. A model that predicts the accumulation of construction waste was developed based on these field observations. According to the model, waste accumulates in an exponential manner, i.e. smaller amounts are generated during the early stages of construction and increasing amounts are generated towards the end of the project. The total amount of waste from these sites was estimated at 0.2m(3) per 1m(2) floor area. A good correlation was found between the model predictions and actual data from the field survey.
Methodological functions of CiteSpace knowledge graphs
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