Construction-related Activities Research Articles

Construction Industry Causes of Injuries, Management of EMS and Its Impact on Public Health

Introduction: Injuries in construction are not a rare occurrence and cause a strain on the health care service. By having education on workplace hazards, trained first aiders, and an effective accident emergency response system, the occurrence of illness/injuries will reduce and hence the number of fatalities.
 The purpose of this research is the evaluation, epidemiology, management, diagnosis, treatment, systematization, and the results obtained about the construction industry for injury in life-threatening cases with trauma, reducing morbidity, disability, and mortality while increasing survival. Solving problems for severe traumas in the health system of Kosovo, such as with professional staff, doctors and nurses, dedicated spaces, medical equipment, drugs, consumables, poor diagnostics, non-decisional consultations, as well as their timely and timely delivery. seeks political health solutions to solve this problem.
 Material and Methods; In this retrospective study, we researched and analyzed the data of patients with construction-related injuries in ED treated from January-December 2021 
 Results; During the study period, we analyzed 75,899 ED patients. Of these, 420 cases, or 0.06% were injured. In the research, 420 cases of injuries were investigated, investigating all age groups arising from construction-related activities for 2021.
 Conclusion; To reduce workplace injuries an organization must carry out regular risk assessments, conduct physical assessments for demanding roles, provide safety and wellness training regularly, train and appoint first aid marshals, hire qualified workers, hire enough workers, keep workspaces clean and walkways clear, post proper signage, Provide adequate lighting, education and training on staff emergency response systems via courses BLS-D BTLS, PHTLS ATLS. 

Определение готовности территорий Арктической зоны к цифровизации строительной отрасли на основе рейтингования регионов Российской Федерации

The relevance of developing the construction sector in Russia stems from the imperative to 1) enhance productivity, 2) reduce costs and improve quality and safety, and 3) address future challenges such as climate change and economic diversification. This article assesses the readiness of regions for digitalizing the construction sector through a ranking system based on the geometric mean. Digitalization readiness in the construction industry is the current state of territorial socio-economic systems defined across four areas of development: the socio-economic domain, research and innovations,the digital sphere, and the industrial domain. The study aims to establish a regional ranking to assess digitalization readiness in the construction sector, with a specific focus on the Arctic zone of the Russian Federation. Regional specialization in construction-related economic activities is identified, revealing that the Yamalo-Nenets Autonomous Okrug and the Republic of Sakha (Yakutia) present opportunities for creating an industrial cluster. A benchmark ranking is proposed, indicating that the Yamalo-Nenets Autonomous Okrug and the Republic of Sakha (Yakutia) exhibit attractiveness for strategic initiatives aimed at digitalizing the construction industry.

Влияние пандемии COVID-19 на цифровизацию экономики арктических регионов России

The relevance of developing the construction sector in Russia stems from the imperative to 1) enhance productivity, 2) reduce costs and improve quality and safety, and 3) address future challenges such as climate change and economic diversification. This article assesses the readiness of regions for digitalizing the construction sector through a ranking system based on the geometric mean. Digitalization readiness in the construction industry is the current state of territorial socio-economic systems defined across four areas of development: the socio-economic domain, research and innovations, the digital sphere, and the industrial domain. The study aims to establish a regional ranking to assess digitalization readiness in the construction sector, with a specific focus on the Arctic zone of the Russian Federation. Regional specialization in construction-related economic activities is identified, revealing that the Yamalo-Nenets Autonomous Okrug and the Republic of Sakha (Yakutia) present opportunities for creating an industrial cluster. A benchmark ranking is proposed,indicating that the Yamalo-Nenets Autonomous Okrug and the Republic of Sakha (Yakutia) exhibit attractiveness for strategic initiatives aimed at digitalizing the construction industry.

A multi-level safety culture maturity model for (new) building projects in Hong Kong

Ninety-two per cent of industrial fatal accidents in Hong Kong in 2021 were attributed to the construction industry. Previous construction safety culture studies focused on projects as a singular organisation. Studies in building projects from a multitier perspective merit further examination. This study develops a safety culture maturity (SCM) framework to assess (new) building projects in Hong Kong at three levels: Client Safety Culture (CSC), Main Contractor Safety Culture (MSC) and Subcontractor Safety Culture (SSC). A closed question survey (N-31) incorporating subculture constructs (Informed, Just, Reporting, Learning and Flexible Cultures) with Hudson’s safety culture ladder (Pathogenic, Reactive, Calculative, Proactive and Generative Cultures) is conducted. The findings reveal: CSC (Mean: 3.55, SD: 0.512), MSC (Mean: 3.71, SD: 0.311) and SSC (Mean: 1.90, SD: 0.605). Kruskal-Wallis One-Way-ANOVA shows that the mean maturity value of three organisations are unequal (χ² = 53.8, df: 2, p: < 0.001). A SCM framework is designed for (new) building projects related to near misses and accidents. It would not be applicable to health-related, workplace violence, burglary and accidents outside construction-related activities. This is the first study to examine the safety culture maturity at three organisational levels. The study recommends improving project-based SCM by intervening and aligning individual levels between CSC, MSC and SSC.

Effect of information load and cognitive style on cognitive load of visualized dashboards for construction-related activities

A well-designed visualized dashboard could provide intuitive information to construction project managers for effective decision-making. However, the impact of information load of dashboards on cognitive load has rarely been investigated. The roles of user cognitive styles were also ignored. This study examined the effect of dashboard information load and user cognitive style on cognitive load when using dashboards in the construction industry. We designed a visual search experiment with five information load levels. Fifty-six participants, divided into field-dependent (FD) and field-independent (FI) groups, participated in the experiment. The results showed that the FI group had a lower cognitive load and was more efficient than the FD group. The effect of information load on cognitive load followed a piecewise linear correlation with the two prominent knots. The findings underscore the importance of assessing the information load and providing psychological underpinnings for cognitive-fit dashboard design for smart construction management.

The Influence of Intrinsic and Extrinsic Motivation in Workers’ Productivity: Empirical Evidence from the Construction Industry

The human resource remains the most critical productive asset of any organization as it is the human element that gives direction and dynamism to the organization. This study investigated the effect intrinsic motivations and extrinsic motivations have on employee productivity of four selected construction companies in Delta State. To achieve its objectives, the study adopted a quantitative research approach with a survey research design. From a population figure of 242 low-level permanent employees of the four selected construction companies in Delta State, 151 constituted the sample as data were collected through a structured questionnaire using the cross-sectional approach. Descriptive statistics and simple regression analysis were used to analyze the data gathered and test the study hypotheses. Findings revealed that intrinsic motivation has a significant positive effect on employee productivity and extrinsic motivation also exerts a significant positive effect on employee productivity. Also, the most effective tools of extrinsic motivation are competitive salaries, extra work bonuses, and opportunities for promotion. Based on these findings, it was concluded amongst others, that the desire to gain mastery and contributes to societal development through construction-related activities gives low-level construction workers some level of satisfaction and financial-related incentives have an immediate and powerful effect on their performance. The key recommendation of the study is that to make a foster high level of productivity from the employee, top management should adopt a performance-based payment system and sell the benefits of contributing to societal development to the construction workers, acknowledging their contribution through noble initiatives like recognition from an authority, opportunity for mentoring, participation in decision making.

Social Impact Assessment of Circular Construction: Case of Living Lab Ghent

The construction industry is considered to have a high potential in achieving the sustainable development goals. The circular economy is a promising framework that supports the shift from a linear-construction industry to an environmental-friendly and efficient sector. On the other hand, there is a lack of effort in measuring the impact of construction-related activities on users and society. The gap is greater when the context of social impacts is related to circular and bio-based construction. For this purpose, a social impact assessment framework was developed in the Interreg 2 seas CBCI project and tested on a residential prototype: Living Lab (LL) Ghent. Under 13 impact categories relevant to 4 stakeholder categories, circular and bio-based construction materials and methods were assessed for production and construction phases. Qualitative and quantitative data were collected through expert workshops and questionnaires. The results include identification of new indicators (urban mining, social economy, and post-intervention manuals) for several circular construction methods. The social impacts of the LL were discussed depending on each stakeholder category. It was seen that there are several positive impacts related to workers and the local community. Certain recommendations were also provided specifically on a construction-sector basis which may be integrated into existing social impact assessment guidelines.

Perception of masonry workers on health and safety risks in informal construction sites in Dar es Salaam, Tanzania

Informal construction (IC) is coined from an informal economy (IE). It involves unlicensed and unregulated individuals engaged in construction-related activities. Because it is unregulated, IC poses health and safety risks (HSR). This study identified and ranked the perception of HSR by masonry workers (MW) in informal construction sites (ICS) in Tanzania. A number of 19 HSRs were found in the literature, and then, the data were collected through questionnaires from 304 MW in ICS and 10 semi-structured interviews. The study employed a convergence concurrent mixed methods approach, whereby for quantitative data, descriptive statistics and inferential statistics (one-sample t-tests) were used, and for qualitative data, directed content analysis was employed. The results showed that ‘skin sanitizers, irritants’, ‘sickness due to poor drinking water or poor toilet facilities’ and ‘injury or death due to falling from height’ were the most perceived HSRs by workers in IC. In contrast, ‘bullying and stress’, ‘diseases caused by exposure to airborne fibres and toxic materials’ and ‘hand and vibration syndrome’ are the least perceived HSRs. The study provides useful insights into the perception of MW on HSR in IC, which can be used as a baseline for good practice for policy directions in developing countries.

A novel spatio-temporally stratified heterogeneity model for identifying factors influencing carbon emissions

Understanding factors influencing carbon emissions is important for achieving carbon abatement goals. Traditionally, decomposition approaches, e.g. Logarithmic Mean Divisia Index (LMDI) method, are used to uncover factors influencing the change of carbon emissions. This study aims to develop a novel spatio-temporally stratified heterogeneity (STSH) model to better identify, analyze and understand influencing factors of carbon emissions. Compared with LMDI, this model can analyze more independent intensity and quantity factors and reveal the complex interactions between factors. Using China’s construction industry as a case study, the model can successfully identify top influencing factors in the same order of importance similar to LMDI, although a significantly larger number of factors are considered. In the whole construction stage, cement usage, steel usage, completed floor area and construction output value have the highest contributions of 0.599, 0.528, 0.448, 0.446 respectively. In construction and construction-related transportation activities, the top influencing factors are fixed capital assets, construction output value, completed floor area, and electricity usage, with contributions of 0.632, 0.613, 0.599, and 0.597, respectively. The model can also reveal the complex interactions between factors, including bi-variate enhanced interaction and nonlinearly enhanced interaction. The results demonstrate that the model is more useful to evaluate the individual and aggregate impact of a large number of independent factors on carbon emissions when compared with previous models.

On-site building construction workers perspective on environmental impacts of construction-related activities: a relative importance index (RII) and exploratory factor analysis (EFA) approach

The construction sector has massive direct and indirect impacts on the environment; as such, improving the environmental performance of the construction industry requires knowledge about the nature of environmental impacts. On-site construction workers get exposed to construction-related environmental impacts, but have received limited research attention on impact identification. This study examined on-site workers’ perspectives on the major environmental impacts of building construction processes. Data was collected from 221 on-site building construction workers using a structured questionnaire. The data was analyzed using a relative importance index (RII) to determine the importance levels of environmental impacts and exploratory factor analysis (EFA) for dimension reduction. The RII revealed that dust generation, noise, vibration, and raw material consumption were the impacts of the highest importance levels of severity. The EFA result showed that local issues were the most influential component. The perceived severity of environmental impacts associated with construction-related activities by on-site construction workers were influenced by their exposure experience. The environmental impacts that directly affect or serve as occupational hazards were ranked as the most severe. This study recommends establishing control measures at construction sites considering the health impact of dust and noise pollution on the well-being of on-site construction workers and the local community.

Discussion on the effects of carbon trading for construction-related activities based on hybrid defuzzification strategy

This paper constructs and develops a bi-layer programming model (BPM) in order to validate the various advantages of combining construction-related activities (CRAs) with carbon trading mechanisms under uncertainty. Besides, a hybrid defuzzification strategy (HDS) is applied to manage the uncertainties; for example, the uncertainties regarding with carbon price are mitigated by machine learning. As well, the uncertainties surrounding construction-related costs and carbon emissions are quantified by introducing the subjective risk propensity of decision makers. Subsequently, the effectiveness of the HDS is demonstrated with sensitivity indicators by conducting simulations in different scenarios based on real data. The results show that carbon trading can cut emissions from CRAs by up to 31.9234 %, 8.1393 %, and 8.7023 % during procurement, transportation, and usage respectively, while lowering the costs by up to 0.9866 %, 0.2004 %, and 1.1267 % respectively. While carbon trading can effectively facilitate emission reduction from CRAs, the profits are not burdened. Furthermore, the key factors in the green transition process of CRAs are summarized, and it is argued that the implementation of carbon trading can lead to higher affordability of CRAs. Finally, the paper suggests that the level of carbon prices affects the schedule of CRAs, with higher construction desire in periods of low carbon prices.

Efficiency of convolutional neural networks (CNN) based image classification for monitoring construction related activities: A case study on aggregate mining for concrete production

Monitoring construction activities is an important task for efficiency in construction site operations thus the topic received a fair amount of attention in the literature. Optimizing construction site operations by monitoring and detecting various tasks is dependent on the size of the construction field, which determines the tools that can be used for the job. A monitoring task can be performed with high efficiency through image classification algorithms by training the algorithms to detect construction machinery. If the area of monitoring is larger, such as the task of detecting construction related operations in a large infrastructural construction, using drone images might become inefficient. We aimed to take a first step towards a cost-efficient monitoring system specifically for construction activities that cover large territories. Consequently, satellite image classification has been performed for construction machinery detection in this study. We utilized different versions of well-established convolutional neural network architectures as backbone for the transfer learning method and their performances are evaluated. Finally, the best performing models are determined as DenseNet161 and ResNet101 with 0.919 and 0.903 test accuracies, respectively. DenseNet161 model was discussed in terms of its accuracy and efficiency in a case study to detect illegal aggregate mining activity through the basin of Thamirabarani River.

Contribution of Surface Treatment on Physical and Mechanical Properties of Recycled Concrete Aggregates

The demand for virgin aggregates for concrete production worldwide has been increasing. At the same time, there is an increase in the production of rubbles from construction-related activities. The residues are produced either due to leftovers or from the demolished structures. However, worldwide, the utilization of recycled concrete aggregate (RCA) material for structural concrete has been limited, often considered inferior due to distorted surfaces. The problems with RCA arise because it is a composite aggregate made of natural aggregate and cement, and adhered surface mortar is the discontinuities on the surface that arise from the production technique or the strength of the original concrete. The development of hydrated calcium silicate binder in the concrete matrix has been the critical desire of researchers to make better concrete material. This research focuses on the treatment of RCA from locally available rice husk ash with minimum pozzolanic cement. The objective was achieved by conducting tests on the properties of aggregates that include specific gravity and water absorption, aggregate crushing value (ACV), and aggregate impact value (AIV). The effectiveness of the treatment technique is assessed by the analysis of SEM imagery and XRD analysis of the microstructure cement paste around the RCA. At 20% pozzolan concentration, RCA treatment yields comparable specific gravities, water absorption, ACV, and AIV. Furthermore, the replacement of 5% rice husk ash (in 20% concentration) provided the optimal proportion of treatment for RCA, resulting in a reduction of ACV by 31.4%, AIV by 30.0%, and water absorption by 12.7% compared to the untreated RCA. XRD showed that calcite (CaCO₃), quartz, and portlandite phases were the majority in the untreated RCA. The study indicates that pozzolanic cement with 15% can be used with 5% RHA to produce RCA with characteristics almost similar to virgin aggregates. This research presents a consistent methodology to achieve modified RCA for application in construction.

ASSESSMENT OF SPATIO-TEMPORAL VARIATION ANDPOTENTIAL HUMAN HEALTH RISK OF AMBIENT NITROGENDIOXIDE AND SULFUR DIOXIDE AROUND INDUSTRIAL AREA OFMUNDRA, GUJARAT USING GIS AND AERMOD MODEL

Coastal area of Mundra in Gujarat is the crucial power generating area in India, which contains coal based mega thermal power plants. In order to assess the pollutant levels the continuous monitoring of the gaseous and particulate pollutants was observed from 2008 to 2015. The trend was initially increasing when the plant construction started due to construction related activities and then decreasing as can be seen from the regression analysis for various parameters. For briefness, the trend has been shown here for SO2 and NO2 at stations for 5 locations within the 3kmradius of plant area in upwind and downwind direction of the power plants. The study has been conducted for the seasonal and temporal alteration in the ambient SO2 and NO2 concentration of in for a period of eight years in the industrial area of coastal Gujrat in India. Geographical information system (GIS) tool has been used to identify the study area land-use pattern of the coastal Mundra region. Pollution dispersion and trend has been observed with AERMOD Model and statistically. Multiple regression model and regression line analysis has been analyzed in this study for metrological parameters and dispersion of SO2 and NO2 concentration in this industrial area. Health risk assessment has been performed association Indian National Ambient Air Quality standard. Gaseous pollutant emission and dispersion can be influenced by meteorological variables such as temperature, humidity, wind characteristics and the children are also sensitive receptors for air pollution.

Integration of deep learning and extended reality technologies in construction engineering and management: a mixed review method

PurposeIn recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.Design/methodology/approachThis study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.FindingsThe proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.Originality/valueThis study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.

Towards enhancement in reliability and safety of construction projects: developing a hybrid multi-dimensional fuzzy-based approach

PurposeIn the study, a five-dimensional-safety risk assessment model (5D-SRAM) is developed to improve the construction safety risk assessment approaches available in the literature. To that purpose, a hybrid multi-dimensional fuzzy-based model is proposed, which provides a comprehensive ranking system for the safety risks existing in a project by considering the contextualization of the construction-related activities resulting in an accident.Design/methodology/approachThe developed 5D-SRAM is based on an amalgamation of different fuzzy-based techniques. Through the proposed fuzzy analytic hierarchy process (AHP) method, the importance weights of essential risk dimensions playing role in defining the magnitude of the construction-related risks are obtained, while a precise prioritized ranking system for the identified safety risks is acquired using the proposed fuzzy technique of order preference similarity to the ideal solution (FTOPSIS).FindingsThrough the application of the proposed 5D-SRAM to a real-life case study – which is the case of green building construction projects located in Hong Kong – contributions are realized as follows: (1) determination of a more complete range of risk dimensions, (2) calculation of importance weightings for each risk dimension and (3) obtainment of a precise and inclusive ranking system for safety risks. Additionally, the supremacy of the developed 5D-SRAM against the other safety assessment approaches that are commonly adopted in the construction industry is proved.Research limitations/implicationsThe developed 5D-SRAM provides the concerned safety decision-makers with not only all the crucial dimensions that play roles toward the magnitude of safety risks posing threats to the workers involved in construction activities, but also they are given hindsight regarding the importance weights of these dimensions. Additionally, the concerned parties are embellished with the final ranking of safety risks in a more comprehensive way than those of existing assessment methods, leading to sagacious adoption of future prudent strategies for dealing with such risks occurring on construction sites.Originality/valueNumerous studies have documented the safety risks faced by construction workers including proposals for risk assessment models. However, the dimensions considered by such models are limited, generally constrained to risk event probability combined with risk impact severity. Overlooking other dimensions that are essential towards the calculation of safety risks' magnitude culminates in overshadowing the further adoption of fruitful mitigative actions. To overcome this shortcoming, this study proposes a novel 5D-SRAM.

Soil Loss Risk Analysis for Construction Activities

Construction-related ground-disturbing activities leave exposed land susceptible to soil loss and increase the risk of polluting adjacent waterbodies with sediment-laden discharge. State and federal regulations require stormwater pollution prevention plans to be implemented during construction to mitigate the impact of stormwater runoff. Areas prone to soil loss can be identified early in site planning using soil loss modeling. Identification of these critical areas could influence the design and placement of erosion and sediment control practices. The Revised Universal Soil Loss Equation (RUSLE) can be applied to estimate the soil loss on construction sites in tonnes per Ha per year (tons/acre/year) by considering factors of rainfall erosivity, soil erodibility, length of slope, erosion control, and sediment control. This study integrates geographic information system (GIS) with RUSLE to create soil loss models for residential, commercial, and highway construction scenarios in the contiguous U.S.A. These three construction types were modeled in various locations throughout the country to assess erosive risk. Soil loss outputs were categorized into five risk tiers ranging from very low to very high. Southeastern states had the highest estimated soil loss during residential, commercial, and highway construction, reaching rates of 1,464, 706, and 1,302 tonnes per Ha per year (653, 315, and 581 tons/acre/year), respectively. This study provides a customizable model for any site-specific slope-length factor outside of the three construction scenarios modeled. Integration of GIS provides a unique opportunity to apply RUSLE across a larger landscape. The presented macro-scale data can be used for the design of erosion and sediment control practices.

An Overview about the Current Situation on C&D Waste Management in Italy: Achievements and Challenges

The disposal volume of material for Construction and Demolition in Europe is increasing each year, that the European Union has decided to take control of the matter unifying practices and goals to achieve. This article analyses how waste management works in Italy adjusting its system to the European Union, regarding Construction and Demolition, taking into consideration the disposal of material coming from C&D (Construction and Demolition) production. In Italy, the disposition may differ according to the regions in which it is divided, and this must be taken into consideration when analysing information that reflects the different logistical aspect linked to the territory. It is also necessary to consider how the volume of waste can vary according to the size of the region considered and the type of industrial development to which it belongs. The analysis of the Italian situation shows their achievements regarding reaching a good level of recycling waste volume; indeed, their amount of C&D recycled waste after 2010 was always up to the 70% value established by the European Union, though barriers are still present in the field of waste management. Through data collecting, it has been seen the different volume that is generated in construction-related activities in the country, along with the recovered waste volume. Thus, the goal of this paper is to deepen the general knowledge on waste produced by Construction and Demolition in Italy and waste management practices adopted according to the European Union.

Proposed Multipurpose Project at Pancheshwar in the Western Himalaya affects rich butterfly diversity: a conservation concern

The Pancheshwar Multipurpose Project proposed for the construction of the largest dam in South Asia is located in the biodiversity rich fragile landscape of the Indian Himalayan Region. In the present scenario, the area at Pancheshwar is being perturbed and going through forest clearance on a regular basis for pre-dam construction operations such as tunnel, road building etc. Butterflies that are vital and crucial organisms for the functioning of terrestrial ecosystems get drastically affected by such large changes in their habitats. The present study therefore aims at describing the importance of butterfly conservation with respect to their diversity and composition along different habitat conditions prevailing at the main dam site of Pancheshwar in the Western Himalaya. Previously, the rich biological diversity of butterflies had remained relatively unknown from the region. By adopting the standardized sampling techniques during 2016–2018, a total of 5341 individuals of 98 species under six butterfly families were recorded from the five representative habitat types (sampling sites) with varying degrees of disturbance. The area was found to be enriched with 23.27% of the known species from the Western Himalaya. Based on global distribution 72.44% of the total recorded species were of the Oriental region, 10.20% species showed Palaearctic distribution while seven species were endemic to the Himalaya. Moreover, the richness and abundance of butterflies varied significantly among the habitat types under various threat levels. The highly disturbed habitat (a forest area altered for pre-dam constructions which was formerly covered with canopy) yielded least diversity and richness while the forest edge habitat in a moderately degraded forest showed the highest diversity, richness and uniqueness, rendering it as a key habitat for preservation and conservation of butterflies. The other three sites selected in the riparian zone had the high similarity of butterfly assemblages. A total of 28 butterfly species were identified on the utmost conservation priorities based on their national protection status, diet specialization, local and global geographic distributions. The high diversity and heterogeneity in community composition is attributed to complex vegetation and diverse habitat conditions prevailing in a fairly isolated geographical area proposed for the mega dam construction. At present, declines in the both quality and type of habitats due to the heedless on-going dam construction related activities are causing adverse affects on the rich butterfly diversity and posing paramount threats of habitat loss and extinction to butterfly fauna that include many rare, specialist, legally protected and endemic species of the fragile Himalayan ecosystem. This study thus substantiates the need for adoption of timely specified, decisive and effective conservation practices in such regions of prime biological importance.

Exploring a body of knowledge for promoting the sustainable transition to prefabricated construction

PurposeThis paper aimed to introduce a systematic body of knowledge via a scientometric review, guiding the sustainable transition from conventional construction to prefabricated construction. The construction industry currently faces a challenge to balance sustainable development and the construction of new buildings. In this context, one of the most recent debates is prefabricated construction. As an emerging construction approach, although existing knowledge makes contributions to the implementation of prefabricated construction, there is a lack of a comprehensive and in-depth overview of the critical knowledge themes and gaps.Design/methodology/approachThis study uses the scientometric analysis to review the state-of-the-art knowledge of prefabricated construction. It retrieved data from the Web of Science core collection database. CiteSpace software was used to conduct the analysis and visualization; three analysis methods identify the knowledge hotspots, knowledge domains and knowledge topics. Finally, according to integrating the hidden connections among results, a body of knowledge for prefabricated construction application can be inferred.FindingsThe results show that 120 knowledge hotspots, five critical knowledge domains and five prominent knowledge topics are vital for promoting implementation of prefabricated construction. Based on the afore analysis, a body of knowledge for prefabricated construction that can systematically cover a broad knowledge of prefabricated construction-related research and activities are integrated and proposed in this paper.Originality/valueBody of knowledge systematically covers a broad knowledge of prefabricated construction applications and is vital to guide researchers and practitioners to conduct related research and activities, thereby promoting the sustainable transition to prefabricated construction implementation.