An evaluation model for city-scale construction and demolition waste management effectiveness: A case study in China.

The growing generation of construction and demolition waste (CDW) has emerged as a prominent challenge on global environmental agendas. However, the effectiveness of CDW management (CDWM) strategies varies among cities. Existing literature predominantly evaluates the effectiveness of CDWM at the project level, offering a localized perspective that fails to capture a city's comprehensive CDWM profile. This localized focus has certain limitations. To fill this gap in city-scale evaluations, this study introduces a novel model for assessing CDWM effectiveness at the municipal level. An empirical investigation was conducted across 11 cities within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) to operationalize this model. The model defines five distinct levels of CDWM effectiveness. Findings indicate that Hong Kong consistently achieves the highest level (level I), while the majority of cities fall within levels III and IV. This pattern suggests that CDWM effectiveness in the GBA is moderately developed, with uneven progress in CDW management outcomes and supporting systems. Essentially, there is a lack of synchronous development of CDWM results and guarantee systems. The proposed evaluation model enriches existing CDWM research field and offers a framework that may inform future studies in other countries.

A new multi-criteria decision making method for the selection of construction contractors using interval valued fuzzy set.

OBJECTIVE: This article introduces a novel approach called Digital Weighted Multi Criteria Decision Making (DWMCDM) that employs interval valued fuzzy sets to select the best contractor for building projects. The contractor is chosen based on the pre-qualification and bid evaluation phases. In the first phase, the distance between the actual and required skills of the significant criteria is determined, and it is then converted into digital weighted distances to identify the maximum number of criteria related to the specific project of each contractor. The second step ranks the best contractor based on the bid price and digital weighted distances. RESULTS: The suggested technique integrates the pre-qualification and bid review phases to address project award delays and other restrictions. Finally, a real-world application is addressed to demonstrate the applicability of the proposed approach to any type of interval valued fuzzy inputs.

Optimally leveraging depth features to enhance segmentation of recyclables from cluttered construction and demolition waste streams.

This paper addresses the critical environmental issue of effectively managing construction and demolition waste (CDW), which has seen a global surge due to rapid urbanization. With the advent of deep learning-based computer vision, this study focuses on improving intelligent identification of valuable recyclables from cluttered and heterogeneous CDW streams in material recovery facilities (MRFs) by optimally leveraging both visual and spatial features (depth). A high-quality CDW RGB-D dataset was curated to capture MRF stream complexities often overlooked in prior studies, and comprises over 3500 images for each modality and more than 160,000 dense object instances of diverse CDW materials with high resource value. In contrast to former studies which directly concatenate RGB and depth features, this study introduces a new depth fusion strategy that utilizes computationally efficient convolutional operations at the end of the conventional waste segmentation architecture to effectively fuse colour and depth information. This avoids cross-modal interference and maximizes the use of distinct information present in the two different modalities. Despite the high clutter and diversity of waste objects, the proposed RGB-DL architecture achieves a 13% increase in segmentation accuracy and a 36% reduction in inference time when compared to the direct concatenation of features. The findings of this study emphasize the benefit of effectively incorporating geometrical features to complement visual cues. This approach helps to deal with the cluttered and varied nature of CDW streams, enhancing automated waste recognition accuracy to improve resource recovery in MRFs. This, in turn, promotes intelligent solid waste management for efficiently managing environmental concerns.

Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild.

The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms of waste. Deep learning (DL) models have made remarkable strides in automating domestic waste recognition and sorting. However, the application of DL models to recognize the waste derived from construction, renovation, and demolition (CRD) activities remains limited due to the context-specific studies conducted in previous research. This paper aims to realistically capture the complexity of waste streams in the CRD context. The study encompasses collecting and annotating CRD waste images in real-world, uncontrolled environments. It then evaluates the performance of state-of-the-art DL models for automatically recognizing CRD waste in-the-wild. Several pre-trained networks are utilized to perform effectual feature extraction and transfer learning during DL model training. The results demonstrated that DL models, whether integrated with larger or lightweight backbone networks can recognize the composition of CRD waste streams in-the-wild which is useful for automated waste sorting. The outcome of the study emphasized the applicability of DL models in recognizing and sorting solid waste across various industrial domains, thereby contributing to resource recovery and encouraging environmental management efforts.

A comprehensive model for quantifying construction waste in high-rise buildings in India.

The construction industry plays a vital role in the economic development of any country. Concurrently, the sector also generates enormous quantities of construction and demolition waste (CDW) that damages the ecology causing environmental pollution and deteriorating human health. Recently, various governments and other organizations realized the importance of implementing construction waste management (CWM) practices to attain sustainability in construction. The current decade can be called a pathway for achieving the 2030 agenda for sustainable development goals in which CWM plays an inevitable role. However, accurately quantifying construction waste is necessary to successfully implement any CDW management plan. A detailed literature review for the current research revealed that limited information on the magnitude of construction waste is available in India. Therefore, the current paper proposes a practically viable model to estimate the waste generation index (construction waste generated per total floor area) of high-rise residential buildings in India. The waste quantification is being done based on the project documents and expert interviews. The methodology is later validated through a high-rise building with G + 18 stories located in Kerala, India. The study indicated that a high-rise concrete framed structure generates 122.3 kg m-2 of waste during construction. It was also noted that, concrete, aggregates and blocks constitute 92% of the total waste generated in the project. The developed model can also be used as a cornerstone for establishing a construction waste database at the regional level.

Automatic identification of illegal construction and demolition waste landfills: A computer vision approach.

Dozens of landslide accidents are reported at construction and demolition waste (CDW) landfills worldwide every year. Those accidents could be avoided via timely inspection in which the identification of illegal CDW landfills at a large scale plays a critical role. Traditional field surveys are time-consuming, labor-intensive, which is not effective in large-scale detection of landfills. To address this issue, a methodology is proposed in this study for the automatic identification of CDW landfills in large-scale areas by utilizing semantic segmentation of remote sensing imagery. Deep learning is employed to achieve automatic identification and a case study is conducted to showcase the models. The results shown that: (1) The model proposed in this study can effectively identify CDW landfills, with an accuracy of 96.30 % and an IoU of 74.60 %. (2) DeepLabV3+ demonstrated superior performance over Pspnet and HRNet, though HRNet approached DeepLabV3+ in performance with appropriate optimizations. (3) Case study results indicate the potential existence of 52 CDW landfills in Shenzhen, includng 4 official landfills and 48 suspected illegal CDW landfills, mainly in Longhua, Guangming, and Baoan districts. The method proposed in this study provides an effective approache to identify large-scale illegal CDW landfills and has great significance for supervising CDW landfills.

Modeling construction and demolition waste quantities in Tanta City, Egypt: a synergistic approach of remote sensing, geographic information system, and hybrid fuzzy neural networks.

A waste management strategy needs accurate data on the generation rates of construction and demolition waste (CDW). The objective of this study is to provide a robust methodology for predicting CDW generation in Tanta City, one of the largest and most civilized cities in Egypt, based on socioeconomic and waste generation statistics from 1965 to 2021. The main contribution of this research involves the fusion of remote sensing and geographic information systems to construct a geographical database, which is employed using machine learning for modeling and predicting the quantities of generated waste. The land use/land cover map is determined by integrating topographic maps and remotely sensed data to extract the built-up, vacant, and agricultural areas. The application of a self-organizing fuzzy neural network (SOFNN) based on an adaptive quantum particle swarm optimization algorithm and a hierarchical pruning scheme is introduced to predict the waste quantities. The performance of the proposed models is compared against that of the FNN with error backpropagation and the group method of data handling using five evaluation measures. The results of the proposed models are satisfactory, with mean absolute percentage error (MAPE), normalized root mean square error (NRMSE), determination coefficient, Kling-Gupta efficiency, and index of agreement ranging between 0.70 and 1.56%, 0.01 and 0.03, 0.99 and 1.00, 0.99, and 1.00. Compared to other models, the proposed models reduce the MAPE and NRMSE by more than 92.90% and 90.64% based on fivefold cross-validation. The research findings are beneficial for utilizing limited data in developing effective strategies for quantifying waste generation. The simulation outcomes can be applied to monitor the urban metabolism, measure carbon emissions from the generated waste, develop waste management facilities, and build a circular economy in the study area.

Critical review of the recovery rates of construction and demolition waste in the European Union - An analysis of influencing factors in selected EU countries.

Due to its intense use of resources, the construction sector was identified as a priority sector in the European Green Deal. Construction and demolition waste (CDW) is one of the largest waste streams of the European Union. As it shows a high potential for recycling, the European Commission set a recovery target of 70% under the Waste Framework Directive. To control the performance and achievements of the member states, annual national reports to the EU are required. However, there exist different ways to specify and report these rates. Recovery rates published by EUROSTAT are calculated on the basis of waste treatment data of non-hazardous mineral CDW, generated according to the EU Waste Statistics Regulation. Limiting factors like non-unified methods of data collection, different waste coding systems and misinterpretations of the term 'backfilling' impede a cross-country comparison of the published EU recovery rates. In this study, factors that may have a misleading impact on reporting EUROSTAT CDW recovery rates were compiled and a detailed analysis using national quality reports of twelve selected EU countries as a data base were performed. The development of the reported recycling rates over five years was examined and the influence of the various factors was determined. The findings may enhance a more purposeful (scientific) discussion of CDW data and for evidence-based reporting of national recovery rates, and may assist the development towards an improved, uniform EU-wide data set. Finally, this will provide support to decision makers for future policy and governmental requirements.

Real-time construction demolition waste detection using state-of-the-art deep learning methods; single-stage vs two-stage detectors.

Central to the development of a successful waste sorting robot lies an accurate and fast object detection system. This study assesses the performance of the most representative deep-learning models for the real-time localisation and classification of Construction and Demolition Waste (CDW). For the investigation, both single-stage (SSD, YOLO) and two-stage (Faster-RCNN) detector architectures coupled with various backbone feature extractors (ResNet, MobileNetV2, efficientDet) were considered. A total of 18 models of variable depth were trained and tested on the first openly accessible CDW dataset developed by the authors of this study. This dataset consists of images of 6600 samples of CDW belonging to three object categories: brick, concrete, and tile. For an in-depth examination of the performance of the developed models under working conditions, two testing datasets containing normally and heavily stacked and adhered samples of CDW were developed. A comprehensive comparison between the different models yields that the latest version of the YOLO series (YoloV7) attains the best accuracy (mAP50:95 ≈ 70%) at the highest inference speed (<30 ms), while also exhibiting enough precision to deal with severely stacked and adhered samples of CDW. Additionally, it was observed that despite the rising popularity of single-stage detectors, apart from YoloV7, Faster-RCNN models remain the most robust in terms of exhibiting the least mAP fluctuations over the testing datasets considered.

Management of the construction and demolition waste (CDW) and determination of the best disposal alternative by FAHP (Fuzzy Analytic Hierarchy Process): A case study of Tehran, Iran.

The study focused on the management of the final disposal of Construction and Demolition Waste (CDW) generated in Tehran and the determination of the best disposal method. For this purpose, three of final disposal alternatives such as reusing, recycling and landfilling were selected. Moreover, three effective criteria (environmental, economic and socio-cultural) and 16 sub-criteria were considered. A group of experts participated in the questionnaire in order to create a database. FAHP (Fuzzy Analytic Hierarchy Process) was used to determine the final disposal alternative considering to the sustainable development approach. The results obtained from the FAHP model showed that the weight of the environmental, economic and socio-cultural criteria was 0.330, 0.544 and 0.126, respectively. In the view of the environmental, the weight value of the sub-criteria of recyclable, water pollution, air pollution, soil pollution and natural resources protection were 0.035, 0.127, 0.069, 0.042 and 0.055, respectively. In terms of economic, the weight value of the sub-criteria of raw materials cost, land occupancy rate, profitability, mutual interests, exploitation cost and initial investment were 0.108, 0.045, 0.063, 0.083, 0.094 and 0.149 respectively. In addition, from the point of view of socio-cultural aspect, the weight value of the sub-criteria of community acceptance, government cooperation, people's awareness level, security in construction and employment were 0.015, 0.050, 0.011, 0.022, and 0.026, respectively. The reuse alternative with a weight of 0.439 was selected as the best disposal method and the recycling (0.312) and landfilling (0.250) were second and third alternatives, respectively. The results also indicated that the generated CDW in Tehran was mostly composed of reusable components such as metals, plastics, wood, glass and gypsum. Therefore, with the selection of this alternative as a final disposal method, the costs of raw materials and the pollution originated from landfilling can be significantly reduced.Implication: The main purpose of this study is to provide a framework in which the priority of criteria and sub-criteria and alternatives in CDW management in Tehran, Iran is determined. The novelty of this method included providing an efficient management method in CDW management, because the production of this type of waste in Iran has become a major problem. The most important part of this method was the decision of local experts to provide the best disposal alternative, since solving problems related to CDW management is achieved by participation and collaboration with experts working in the same system. The obtained results showed that reusing is the first priority in terms of all the studied criteria and sanitary landfilling is the last priority. Sanitary landfilling is currently in place in the study area and respondents are well aware of its disadvantages. The results in terms of each criterion show that economic criteria are the most important criteria. Investment cost in terms of economic criteria, public acceptance in terms of social criteria and water pollution in terms of environmental criteria as the most effective sub-criteria according to the main goal. Various complex factors affect CDW management systems and therefore the use of practical decision-making techniques such as FAHP to deal with the complexity of CDW management will be useful and valuable.

Strategies to promote construction and demolition waste management in the context of local dynamics.

Achieving a broad analysis of construction and demolition waste (CDW) management without considering local scale dynamics, and its detailed characteristics, is a constraint that has made it challenging to optimally engage in an integrated assessment of the circular economy principles in the construction sector. In this sense, this research demonstrates that investing in local strategies is important, involving municipalities and micro and small construction companies. Firstly, the results reveal the importance of having controlled sites, under local responsibility, for the preliminary storage of CDW, creating in waste producers the habit of separating waste onsite, reducing costs and limitations for municipalities. Secondly, frequent supervision actions at construction sites are also important at this scale, as they facilitate progress in terms of encouraging compliance with mandatory legal procedures and good practices for CDW management. But it is easier to improve practice through direct onsite procedures than it is with bureaucratic legal requirements alone. Thirdly, procedural control, implemented by municipal technicians in conjunction with other strategies, also helps to promote CDW management, this being associated with processes of public and private construction works subjected to license or prior control, in opposition to what has been accomplished so far. But the research also demonstrated that regular awareness, training, and supervision actions might increase the likelihood of improvements in behaviour on the local scale, in the sense that stakeholders acquire new habits, which, over time, might lead to better results locally and, as a consequence, influence other scales of intervention.

A Design for Safety (DFS) Framework for Automated Inspection Risks in Metro Stations by Integrating a Knowledge Base and Building Information Modeling.

Safety issues have always been of great concern to the metro construction industry. Numerous studies have shown that safety issues are closely related to the design phase. Many safety problems can be solved or improved by developing the design. This study proposes a structured identification method for safety risks based on the metro design specifications, journal literature, and expert experience. A safety knowledge base (KB) for the design was established to realize safety knowledge sharing and reusing. The KB has been developed into Building Information Modeling (BIM) software as an inspection plug-in to achieve automated analysis and retrieval of safety risks. The designers are provided with a visualization of risk components to locate and improve the pre-control measures of the design. Subsequently, the process of design for safety (DFS) database creation was demonstrated with a metro station project, and the feasibility of applying the KB to safety checking in BIM was verified. In response to the inspection results, safety risks in the construction phases can be eliminated or avoided by standardizing and improving the design.

Is Policy the Necessary or Sufficient Driving Force of Construction and Demolition Waste Recycling Industry Development? Experience from China.

Policies have long been considered the essential driving force in promoting construction and demolition waste (CDW) recycling. However, the policy instruments adopted in different economies have varied greatly, which contributes to the difficulty in quantitative discernment of their effect. This study aims to examine whether the holistic employment of policy measures determines the development of CDW recycling around China. To accurately measure the holistic adoption of CDW policies, this study assessed policy strength via a proposed three-dimensional evaluation model. The spatiotemporal differences in policy strength among the 52 sample cities were further defined using K-means clustering and the Gini coefficient. Next, the driving effect of policy on the initial establishment of CDW recycling industry practices was examined by event history analysis (EHA). Finally, fuzzy set qualitative comparative analysis (fsQCA) was used to analyze the sufficiency and necessity of policy for the initial establishment of CDW recycling practices. The results indicated that the establishment of a first CDW recycling plant is only slightly correlated with policy measures, whereas it is highly correlated with the pilot city and per capita GDP. Furthermore, application of policy is neither a necessary nor sufficient condition for the establishment of a CDW recycling industry facility.

Modelling of demolition waste generation: Application to Greek residential buildings.

The construction sector in Europe is among the biggest waste generators, producing 370 million tonnes of construction and demolition waste (CDW) every year, which contain important secondary materials. Quantification of CDW is important from their circular management and environmental impact point of view. Thus, the overall objective of this study was to develop a modelling methodology for estimating demolition waste (DW) generation. The volumes (m3) of individual construction materials contained in 45 residential buildings in Greece were accurately estimated using computer-aided design (CAD) software and the materials were classified according to European List of Waste. These materials will become waste upon demolition, with a total estimated generation rate of 1590 kg m-2 of top view area and with concrete and bricks representing 74.5% of total. Linear regression models were developed to predict the total and individual amounts of 12 different building materials based on structural building characteristics. To test the accuracy of the models, the materials of two residential buildings were quantified and classified and the results were compared with the model predictions. Depending on the model used, the % differences between models' predictions and CAD estimates for total DW averaged 11.1% ± 7.4% for the first case study and 2.5% ± 1.5% for the second. The models can be used for accurate quantification of total and individual DW and their management within the framework of circular economy.

Determinants of contractor's construction and demolition waste recycling intention in China: Integrating theory of planned behavior and norm activation model.

The abundant generation of construction and demolition waste (CDW) poses a threat to the sustainable development in China and recycling plays a vital role in complying with circular economy zero-waste goal. In this study, we first investigate the determinants of contractor's intention to recycle CDW by establishing an integrative model of the theory of planned behavior (TPB) and the norm activation model (NAM) with rational and moral considerations. Based on the questionnaire data of 210 valid responses, structural equation modeling is employed to test the proposed hypotheses and analyze the integrative structural model. The results show that the integrative model with adequate reliability and validity fits the empirical data well and the explanation power is superior to that of initial TPB model and NAM model, demonstrating the appropriateness of merging TPB and NAM in the area of CDW recycling research. Moreover, it is found that personal norms are the most critical determinant enhancing the CDW recycling intention, followed by perceived behavioral control. Although subjective norms fail to directly affect the CDW recycling intention, they can significantly strengthen personal norms and perceived behavioral control. These findings provide useful insights for government to develop effective management strategies to motivate CDW recycling intention of contractors.

Exploring the restrictive factors for the development of the construction waste recycling industry in a second-tier Chinese city: a case study from Jinan.

Recycling construction waste in urban cities has attracted wide attention in the government department and building industry worldwide owing to numerous benefits in economics, the environment, and society for sustainable development. Most of the research was focused on the scope of first-tier/new first-tier cities. At the same time, less attention has been paid to second-tier cities, which should be further considered because of their significant potential for increasing construction waste. Jinan is one of the pilot cities for construction waste treatment in China. This city has been chosen as the case in this study to explore critical restrictive factors for developing the construction waste recycling industry (CWRI) in second-tier cities with the adoption of combined methods of the DEMATEL-ISM-EWM. It was revealed by the results that (1) the government is the main stakeholder. In addition, the legislation, management system, incentive/support for stakeholders, and technical standards were closely associated with CWRI development in Jinan. Low landfill fees and natural ore tax rates are unique and vital restrictive factors that should receive more attention. (2) Other key restrictive factors that should be given priority were (i) the lack of green design for designers, (ii) poor classification and management of construction waste for constructors on site, (iii) tight operating funds and insufficient publicity for recycling enterprises, and (iv) insufficient research funds for research institutions. Finally, a comparison of the similarities and differences of the restrictive factors on CWRI development between second-tier and first-tier/new first-tier cities was conducted, and a series of reasonable suggestions were provided, considering the perspective of stakeholders. The research will offer a valuable reference for industry practitioners and academics interested in CWRI.

Construction and demolition waste recycling in developing cities: management and cost analysis.

In Bolivia, construction and demolition waste (CDW) is unmanaged. The current research implemented a CDW flow analysis and a cost assessment in the city of La Paz, aiming to compare future recycling scenarios. Lack of data is challenging; therefore, a review of 31 international studies has been conducted. Results report that the waste generation rate (WGR) of concrete residential buildings can be of about 91.9-113.3 kg m-2 for construction and about 867.2-1064.8 kg m-2 for demolition. In addition, positive correlation was found in national WGR with number of inhabitants and the GDP. By these results, it was estimated that about 271,051-349,418 tonnes of CDW are potentially generated yearly in La Paz, about two times more than the CDW projected by local authorities. By the scenarios analysis, it was estimated that 56.1-71.1% of the inert aggregates can be recycled. The cost of the system is estimated between 7.8 and 31.1 USD tCDW-1, for a total annual cost (20 years horizon) of about 2.3-9 million USD y-1 depending on the management scenario. In the best scenario, an average fee of less than 1% of the current costs of a flat in La Paz can be required, potentially affordable for the local economy. The most attractive scenario involves the source separation of recyclable materials and the hybrid organization of stationary and mobile recycling facilities. Results show that a CDW management can be implemented in La Paz to foster a circular economy in developing cities.

Evaluating carbon emissions of China's waste management strategies for building refurbishment projects: contributing to a circular economy.

This study evaluates carbon emissions of construction and demolition (C&D) waste generated by building refurbishment, using a life cycle assessment approach through a case study project in China. Three waste management scenarios were developed for a building refurbishment project in the city of Suzhou. Scenario 1 is under the business-as-usual C&D waste management practice in China; scenario 2 is based on the open-ended 3R strategy, which focuses on the downstream impact of waste; and scenario 3 considers both the upstream and downstream impact of waste. The results reveal that the composition of the waste generated from building refurbishment projects is different from construction and demolition projects. In the life cycle of C&D waste management of building refurbishment projects, the refurbishment material stage generates the highest carbon emissions compared to the dismantlement, refurbishment construction, and refurbishment material end of life stages. Scenario 1 produces higher carbon emissions than scenario 2, but the difference is not significant in the whole life cycle of the building refurbishment project, whereas carbon emissions for scenario 3 are significantly less than both scenario 1 and scenario 2. The study finds the reason for this difference is that scenario 1 and scenario 2 are based on a linear economy that relies on unsustainable demand for raw materials, whereas scenario 3 is based on a circular economy that uses upcycled materials to substitute for raw materials and considers waste management from a cradle to cradle perspective. This study fills a research gap by evaluating carbon emissions of different waste management strategies for building refurbishment projects, which are expected to be an increasing portion of overall construction activity in China for the foreseeable future.

Evolutionary game model of construction enterprises and construction material manufacturers in the construction and demolition waste resource utilization.

With the continuous advancement of urbanization, a huge amount of construction and demolition waste (CDW) is generated in large-scaled construction activities, which has aggravated the problem of environmental pollution, waste of resources and destruction of city appearance. In the context of waste-free city, the recycling of CDW can reduce environmental pollution and promote the sustainable development of a city. However, only 20-30% of CDW in the world is recycled, showing a low rate of global CDW utilization. In order to improve the utilization rate, this paper selects construction enterprises and construction material manufacturers as main participants, applies evolutionary game theory to construct an evolutionary game model on the two parties' decision-making behaviors in CDW recycling, and uses MATLAB to make a numerical simulation. The aim of the model is to analyze the influence of various factors on the parties' decision-making behavior evolution and propose strategies to promote CDW utilization. The study found that the stable state of the CDW resource utilization system mainly depends on the difference between revenue and costs, the initial strategy, and the strength of the external environment; for the government, a supervision strategy is found to be necessary, and the best supervision level is 0.6. In the early stage of resource utilization of CDW, subsidies to construction material manufacturers should be increased to improve their initial participation; public participation can effectively improve the efficiency of government supervision, and its optimal participation level is greater than or equal to 0.4; under weak supervision, government penalty increases alone cannot prevent construction enterprises from illegally disposing of CDW. Therefore, the greater the difference, the positive the initial strategy, and the stronger the external environment, the more the behavior of the two participants tends to be {participation, use}. The results show that the government should establish effective supervision mechanisms and legal systems, improve supervision hotlines and information platforms, encourage the public to participate in CDW management and supervision, set appropriate rewards and punishments, strengthen supervision and management levels, reduce supervision costs, and ensure the effectiveness of construction management to improve the efficiency of cooperation between construction enterprises and construction material manufacturers.

Emerging Trends of Ergonomic Risk Assessment in Construction Safety Management: A Scientometric Visualization Analysis.

Ergonomic risk assessment is critical for identifying working posture hazardous to the health of construction workers. Work-related musculoskeletal disorders (WMSDs) are predominant non-fatal injuries in the construction industry owing to manual handling activities and poor working conditions. However, there is a lack of scientific synopsis aiming to better understand the emerging research focus in this field. To fill the research gap, this study performed a scientometric evaluation of the bibliometric data on ergonomic risk assessment from the Web of Science database using VOSviewer software. The purpose of this study is to analyze the co-occurrence network of keywords, co-authorship network, most active countries, and the sources of publication. The results indicate that research related to risk assessment in construction has fluctuating growth, peaking in 2020 with significant advancements in the USA, China, and Canada. WMSDs, risk factors, construction workers, and ergonomics are hot research topics in this field. Furthermore, the research gaps of previous studies and suggestions for future research have been provided to bridge the knowledge gap. We believe that this scientometric review provides useful reference points for early-stage researchers as well as beneficial in-depth information to experienced practitioners and scholars in the construction industry.