Over the years, numerous studies have been conducted to investigate construction and demolition waste (CDW) management problems. However, the massive amount of literature brings challenges to ...scholars because it is difficult and time-consuming to manually identify research emphasis from the literature. Therefore, a method that can informationize literature collection and automatically detect insights from the identified literature is worthy of exploration. This paper attempts to present a comprehensive thematic model by combining Latent Dirichlet Allocation, word2vec, and community detection algorithm on python to detect insights from CDW management literature. Based on the database of
Web of Science
, 641 articles published between 2000 and 2019 are retrieved and used as the sample for analysis. The comprehensive thematic results reveal a four-domain knowledge map in CDW management research, which covers (1) introducing current situation of CDW management, (2) quantifying CDW generation, (3) assessing CDW and by-products, and (4) facilitating waste diversion. Future research directions in CDW management research have also been discussed. The results prove that the comprehensive thematic model is useful in mining insights from CDW management literature.
Dust pollution is a slow-onset process with damaging consequences. While past research mainly deals with the sources of dust, health implications and control measures, very little has been done to ...investigate the behaviour of those who are responsible for its management. The Norm Activation Model is employed in this study to examine the awareness of consequences and self-responsibility of managers with regard to dust pollution control on construction sites. Managers' own experiences with dust outbreaks, type and size of the company and its commitment to dust pollution control are found to positively moderate the awareness of consequences of managers. Perception of dust pollution as a trivial and isolated event which leads to discomfort was found to have negative moderating effects on the responsibility for control. The view that dust pollution control is less important compared to other activities is also found to have a negative moderating effect. Water suppression emerged as the main dust control method employed by majority of contractors. Managers' self-responsibility is severely affected by the use of water as the main method of suppression because wastage of water is deemed to be environmentally damaging. In order to influence the behaviour of managers, construction companies should demonstrate a genuine commitment to dust control; recognize dust pollution control in environmental management plans and project management plans; implement regular monitoring; reduce over-reliance on water suppression; and use innovative dust pollution control techniques. Managers should alter their perception of dust pollution as a ‘condition’ that need monitoring and control.
•Norm Activation Model is employed to examine the behaviour related to dust control.•Activities related to soil and demolition are the main generators of dust.•Experiences and commitment positively moderate the awareness of consequences.•Dust pollution is perceived as isolated event rather than conditions.•There is need to reflect behavioural aspects in environmental policy guidelines.
Despite longstanding traditional construction health and safety management (CHSM) methods, the construction industry continues to face persistent challenges in this field. Neuroscience tools offer ...potential advantages in addressing these safety and health issues by providing objective data to indicate subjects' cognition and behavior. The application of neuroscience tools in the CHSM has received much attention in the construction research community, but comprehensive statistics on the application of neuroscience tools to CHSM is lacking to provide insights for the later scholars. Therefore, this study applied bibliometric analysis to examine the current state of neuroscience tools use in CHSM. The development phases; the most productive journals, regions, and institutions; influential scholars and articles; author collaboration; reference co-citation; and application domains of the tools were identified. It revealed four application domains: monitoring the safety status of construction workers, enhancing the construction hazard recognition ability, reducing work-related musculoskeletal disorders of construction workers, and integrating neuroscience tools with artificial intelligence techniques in enhancing occupational safety and health, where magnetoencephalography (EMG), electroencephalography (EEG), eye-tracking, and electrodermal activity (EDA) are four predominant neuroscience tools. It also shows a growing interest in integrating the neuroscience tools with artificial intelligence techniques to address the safety and health issues. In addition, future studies are suggested to facilitate the applications of these tools in construction workplaces by narrowing the gaps between experimental settings and real situations, enhancing the quality of data collected by neuroscience tools and performance of data processing algorithms, and overcoming user resistance in tools adoption.
The demystification of the underlying mechanism for architects' knowledge sharing behavior in a project team context is of importance to better appreciate the behavior in a theoretical sense and for ...effective managerial intervention in a practical sense. However, most studies of knowledge sharing mechanism in current literature focus on the simple mediation. The likelihood of multiple mediators for knowledge sharing is yet to be investigated. To bridge this gap, structural equation modeling is applied to test the parallel mediation of team-based self-esteem and team identification between two types of trust and knowledge sharing with survey data. It is found that the relation between affect-based trust and knowledge sharing is completely mediated by team-based self-esteem and team identification. The model implies that project managers should pay attention to the cultivation of members' team-based self-esteem and team identification. Special measures should also be taken to build and strengthen the affect-based trust.
•The relation between AT and KS is completely mediated by TBSE and TI.•There is no significant difference between the above two mediation paths.•The relation between CT and KS is not mediated by the two mediators.•The findings extend the one mediator knowledge sharing model to two mediator model.•Project managers should pay attention to the cultivation of members’ TBSE and TI.
► A model for quantifying waste generation per gross floor area (WGA) is presented. ► The model is applied to a newly constructed residential building project in China. ► The WGA of this project is ...40.7kg/m2, and concrete is the largest contributor. ► Comparisons with the existing data reveal that the model is valid and practical.
The increasing construction and demolition (C&D) waste causes both cost inefficiency and environmental pollution. Many countries have developed regulations to minimize C&D waste. Implementation of these regulations requires an understanding of the magnitude and material composition of waste stream. Construction waste generation index is a useful tool for estimating the amount of construction waste and can be used as a benchmark to enhance the sustainable performance of construction industry. This paper presents a model for quantifying waste generation per gross floor area (WGA) based on mass balance principle for building construction in China. WGAs for major types of material are estimated using purchased amount of major materials and their material waste rate (MWR). The WGA for minor quantities of materials is estimated together as a percentage of total construction waste. The model is applied to a newly constructed residential building in Shenzhen city of South China. The WGA of this project is 40.7kg/m2, and concrete waste is the largest contributor to the index. Comparisons with transportation records in site, empirical index in China and data in other economies reveal that the proposed model is valid and practical. The proposed model can be used to setup a benchmark WGA for Chinese construction industry by carrying out large-scale investigations in the future.
The construction and demolition waste generation rates (C&D WGRs) is an important factor in decision-making and management of material waste in any construction site. The present study investigated ...WGRs by conducting on-site waste sorting and weighing in four ongoing construction projects in Shenzhen city of South China. The results revealed that WGRs ranged from 3.275 to 8.791
kg/m
2 and miscellaneous waste, timber for formwork and falsework, and concrete were the three largest components amongst the generated waste. Based on the WGRs derived from the research, the paper also discussed the main causes of waste in the construction industry and attempted to connect waste generation with specific construction practices. It was recommended that measures mainly including performing waste sorting at source, employing skilful workers, uploading and storing materials properly, promoting waste management capacity, replacing current timber formwork with metal formwork and launching an incentive reward program to encourage waste reduction could be potential solutions to reducing current WGRs in Shenzhen. Although these results were derived from a relatively small sample and so cannot justifiably be generalized, they do however add to the body of knowledge that is currently available for understanding the status of the art of C&D waste management in China.
The increasing hazards caused by construction and demolition (C&D) waste is an inevitable problem in the development of the construction industry. Many countries have successively launched many ...policies to encourage and guide the recycling of C&D waste, which has greatly improved the recycling rate of C&D waste. However, most of these policies only regulate contractors but do not promote C&D waste recycling products enough. It has led to an increase in the production of C&D waste recycling products while the acceptance in the market is generally low. Consumers believe that products made with “garbage” may have problems such as quality defects. In order to explore a measure that can mitigate this problem, this study uses functional near infrared spectroscopy (fNIRS) to investigate whether the influence of media can increase consumers’ willingness to purchase products for recycling construction and demolition waste, and thus increase consumers’ choice to purchase products for C&D recycling waste. This experiment consists of two phases. First, a pre-test experiment to obtain pre-intervention brain images characterizing consumers’ original attitudes toward C&D recycling waste products through a functional near-infrared imaging brain technique and a questionnaire. Second, The post-test builds on the pre-test to investigate the effectiveness of the intervention. The activation mechanism of the consumer purchase decision is further investigated by fNIRS data. The behavioral results showed that the choice of recycled C&D waste products was significantly higher after the intervention. The fNIRS results further revealed the significantly higher activation of the dorsolateral prefrontal cortex (dlPFC), orbitofrontal cortex (OFC), and medial prefrontal cortex (mPFC) after the intervention. These findings suggest that consumers’ purchase willingness is significantly improved after intervention, and their purchase behavior changed substantially. This study also demonstrates the great potential of fNIRS for interdisciplinary research in engineering management and neuroscience.
Integrating clustering with regression has gained great popularity due to its excellent performance for building energy prediction tasks. However, there is a lack of studies on finding suitable ...regression models for integrating clustering and the combination of clustering and regression models that can achieve the best performance. Moreover, there is also a lack of studies on the optimal cluster number in the task of short-term forecasting of building energy consumption. In this paper, a comprehensive study is conducted on the integration of clustering and regression, which includes three types of clustering algorithms (K-means, K-medians, and Hierarchical clustering) and four types of representative regression models (Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Regression (SVR), Artificial Neural Network (ANN), and extreme gradient boosting (XGBoost)). A novel performance evaluation index (PI) dedicated to comparing the performance of two prediction models is proposed, which can comprehensively consider different performance indexes. A larger PI means a larger performance improvement. The results indicate that by integrating clustering, the largest PI for SVR, LASSO, XGBoost, and ANN is 2.41, 1.97, 1.57, and 1.12, respectively. On the other hand, the performance of regression models integrated with clustering algorithms from high to low is XGBoost, SVR, ANN, and LASSO. The results also show that the optimal cluster number determined by clustering evaluation metrics may not be the optimal number for the ensemble model (integration of clustering and regression model).
The rapid increase in the number of online resources and academic articles has created great challenges for researchers and practitioners to efficiently grasp the status quo of building ...energy-related research. Rather than relying on manual inspections, advanced data analytics (such as text mining) can be used to enhance the efficiency and effectiveness in literature reviews. This article proposes a text mining-based approach for the automatic identification of major research trends in the field of building energy management. In total, 5712 articles (from 1972 to 2019) are analyzed. The word2vec model is used to optimize the latent Dirichlet allocation (LDA) results, and social networks are adopted to visualize the inter-topic relationships. The results are presented using the Gephi visualization platform. Based on inter-topic relevance and topic evolutions, in-depth analysis has been conducted to reveal research trends and hot topics in the field of building energy management. The research results indicate that heating, ventilation, and air conditioning (HVAC) is one of the most essential topics. The thermal environment, indoor illumination, and residential building occupant behaviors are important factors affecting building energy consumption. In addition, building energy-saving renovations, green buildings, and intelligent buildings are research hotspots, and potential future directions. The method developed in this article serves as an effective alternative for researchers and practitioners to extract useful insights from massive text data. It provides a prototype for the automatic identification of research trends based on text mining techniques.
While the construction and operation of subways have brought convenience to commuters, it has also caused ground subsidence and cracks of facilities around subways. The industry mainly adopts ...traditional manual detection methods to monitor these settlements and cracks. The current approaches have difficulties in achieving all-weather, all-region dynamic monitoring, increasing the traffic burden of the city during the monitoring work. The study aims to provide a large-scale settlement detection approach based on PS-InSAR for the monitoring of subway facilities. Meanwhile, this paper proposes a crack detection method that is based on UAVs and the VGG16 algorithm to quantify the length and width of cracks. The experimental data of Shenzhen University Section of Metro Line 9 are used to verify the proposed settlement model and to illustrate the monitoring process. The developed model is innovative in that it can monitor the settlement of large-scale facilities around the subway with high accuracy around the clock and automatically identify and quantify the cracks in the settled facilities around the subway.
PDF
