Search results

Prefabricated construction has proven to be superior in terms of affordability and sustainability over the years. As a result of sustainable production, prefabricated housebuilding has evolved into a distinct industry reliant on supplier companies acting as supply chains (SCs) for housing projects. These companies' performance is critical to the successful implementation of prefabricated housebuilding technologies. However, in comparison to those choosing manufacturing as a strategy in other industries, the life span of these companies, providing innovative housing solutions, is relatively short. This is due to critical factors influencing the performance, but the inter-relationship of the performance dimensions is more significant. This study establishes the inter-relationship of the companies involved in house building with steel prefabricated housebuilding technologies.

The most recent factors were extracted from the literature. The relationships were developed using the interpretive structural modeling (ISM) method with the input from industry experts, and the driving factors were determined using the Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) technique.

Critical performance factors were classified according to performance dimensions, ranked and classified based on driving and dependence power. The inter-relationships among the performance dimensions of time, quality, cost, delivery, features and innovation were determined. Key performance strategies were proposed for prefabricated housebuilding companies involved in manufacturing and/or assembly of steel products.

This study established the interrelationship of performance dimensions for prefabricated house building (PHB) companies to develop strategies against critical challenges to remain competitive in the housing market. Previous research had not looked into interrelationship among the performance dimensions. The proposed performance strategies are applicable to supplier organizations using steel prefabricated technologies in similar markets around the world.

The purpose of this study is to investigate the factors affecting the cost of concrete component prefabrication using the moderating role of organizational readiness, compatibility and competitive pressure.

The information collected in this study was obtained from 188 questionnaires filled out online by civil engineers active in the Iranian construction industry. Data analysis was performed by partial least squares structural equation modeling.

The results reveal that perceived risk has the greatest impact on cost of using precast concrete components (PCC). Furthermore, the organizational readiness, competitive pressure and compatibility variables have moderating role in the relationship between perceived risk and perceived usefulness. The results can help policymakers and managers to apply the appropriate perspective and strategy to promote this method by illustrating the factors affecting the cost of using PCC in the building construction.

Using the variables of perceived risk, perceived usefulness, and perceived ease of use to predict factors affecting the cost of using PCC is considered as an innovation in this research. In addition to identifying the most important factors affecting the cost of PCC prefabrication in the construction industry, this research tries to identify the impact of environmental factors on the model by examining the moderating role of organizational readiness, competitive pressure and compatibility.

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

Modular construction is an innovative method that enhances the performance of building construction projects. However, the performance of steel modular construction has not been systematically understood, and the existing measurement methods exhibit limitations in effectively addressing the features of steel modular building construction. Therefore, this study aims to develop a new performance measurement framework for systematically examining the performance of steel modular construction in building projects.

This study was conducted through a mixed-method research design that combines a comprehensive review of the state-of-the-art practices of construction performance measurement and a case study with a 17-story steel modular apartment building project in Hong Kong. The case project was measured with data collected from the project teams and other reliable channels, and the measurement practices and findings were referenced to establish a systematic performance measurement framework for steel modular construction.

Considering steel modular construction as a complex socio-technical system, a systematic performance measurement framework was developed, which considers the features of steel modular construction, focuses on the construction stage, incorporates the views of various stakeholders, integrates generic and specific key performance indicators and provides a benchmarking process. Multifaceted benefits of adopting steel modular construction were demonstrated with case study, including improved economic efficiency (e.g. nearly 10% cost savings), improved environmental friendliness (e.g. approximately 90% waste reduction) and enhanced social welfare (e.g. over 60% delivery trips reduction).

This paper extends the existing performance measurement methods with a new framework proposed and offers experience for future steel modular construction. The measured performance of the case project also contributes in-depth understanding on steel modular construction with benefits demonstrated. The study is expected to accelerate an effective uptake of steel modular construction in building projects.

Design for safety (DfS), also known as prevention through design (PtD), is a concept that mitigates accidents and hazards through considerations during the design stage of building projects. The literature provides much information on this concept, but such information is only limited to a few developed countries such as the UK, the USA and Australia. There is limited insight into DfS implementation in the construction industry of several countries, including countries in the Gulf Cooperation Council (GCC) such as Kuwait. Therefore, this study investigates DfS implementation among design professionals in the Kuwait construction industry.

The study used a questionnaire survey to obtain data from design professionals. The data were analysed using descriptive and inferential statistics (i.e. analysis of variance and t-test).

The results revealed that DfS awareness among design professionals is very high; there is a very high willingness among design professionals to apply the concept, and design professionals generally view DfS implementation as important. Despite these, the frequency of implementation of DfS practices is generally moderate. In addition, the results revealed that though there is a high interest in DfS training among the design professionals, their actual engagement in training is low. The results also suggest some association between the frequency of engagement in the DfS practices and designers' DfS awareness, training and education. DfS related regulations, industry guidance, formal education and training are considered by design professionals to have the greatest influence on DfS implementation in Kuwait.

These emerging findings both mirror and contradict aspects of the outcomes of previous DfS studies in other countries. Furthermore, the findings from this study provide insights into a less investigated area regarding work-related health and safety in the GCC region. It offers new and additional information and insights into the current state-of-the-art DfS implementation in the construction industry in Kuwait. In view of the findings, joined-up efforts by government, industry and academia are needed to enhance DfS implementation by design professionals in Kuwait.

The construction industry is widely recognized as one of the most hazardous sectors in the world. Despite extensive research on safety management, a critical issue remains that insufficient attention is devoted to safety practices in rural areas. Notably, accidents frequently occur during the construction of rural self-built houses (RSH) in China. Safety management tends to be overlooked due to the perceived simplicity of the construction process. Furthermore, it is essential to acknowledge that China currently lacks comprehensive laws and regulations governing safety management in RSH construction. This paper aims to analyze the behavior of key stakeholders (including households, workmen, rural village committee and the government) and propose recommendations to mitigate safety risks associated with RSH construction.

This paper applies evolutionary game theory to analyze the symbiotic evolution among households, workmen and rural village committee, in situations with or without government participation. Additionally, numerical simulation is utilized to examine the outcomes of various strategies implemented by the government.

Without government participation, households, workmen, and rural village committee tend to prioritize maximizing apparent benefits, often overlooking the potential safety risks. Numerical simulations reveal that while government involvement can guide these parties towards safer decisions, achieving the desired outcomes necessitates the adoption of reasonable and effective strategies. Thus, the government needs to offer targeted subsidies to these stakeholders.

Considering that during the construction phase, stakeholders are the main administrators accountable for safety management. However, there exists insufficient research examining the impact of stakeholder behavior on RSH construction safety. This study aims to analyze the behavior of stakeholders about how to reduce the safety risks in building RSH. Thus, the authors intend to contribute to knowledge in this area by establishing evolutionary game model. Firstly, this study carried out a theoretical by using tripartite evolutionary game to reveal the reasons for the high safety risk during building RSH. Practically, this research points out the important role of households, workmen and rural village committee in improving safety management in rural areas. Besides, some suggestions are proposed to the government about how to reduce construction safety risks in rural areas.

This study aims to develop workplace well-being indexes for construction sites of different project types (infrastructure, high-rise and low-rise). Accordingly, the study objectives are to identify the critical factors that affect workplace well-being at construction sites, compare the critical factors between different project types, categorize the critical factors into subgroups and compute indexes for the critical factors and subgroups.

Data from a systematic literature review and semi-structured interviews with construction industry professionals were used to extract 19 potential factors that affect workplace well-being. Then, a structured questionnaire survey was distributed, and 169 valid responses were collected. Finally, the data were analyzed using normalized mean analysis, agreement analysis, factor analysis and fuzzy synthetic evaluation.

The study findings revealed that there are 11, 11, 8 and 12 critical factors across overall infrastructure, high-rise and low-rise construction projects. Out of those, six critical factors are overlapping across project types, including “general safety and health monitoring,” “salary package,” “timeline of salary payment,” “working hours,” “communication between workers” and “planning of the project.” Accordingly, the critical factors can be categorized into two subgroups within each project type. Finally, the development of indexes shows that infrastructure construction projects have the greatest index compared to other project types.

This study contributes to filling the current knowledge gap by developing workplace well-being indexes at construction sites across different project types. The indexes would assist decision-makers in understanding the current state of workplace well-being. This increases the commitment and recognition of well-being across different construction project types.

The staggering demand for construction projects to meet a spectrum of public needs is projected to outstrip the industry’s supply capability. The modern methods of construction (MMC) offers wider control due to shifting key construction processes offsite. Public clients play a significant role due to their purchasing power; however, their uptake of MMC is low, despite the benefits. The purpose of this study is to reveal the reasoning behind such low adoption. The research gap, herewith, is our lack of understanding of the influence of public clients perceptions on their adoption’s indecision.

This study used a qualitative approach to investigate the motives behind the public sector’s low MMC adoption. Semi-structured interviews with 14 of the United Kingdom’s public sector decision-makers, industry leaders and experts have been conducted. Perspectives were argued against the diffusion of innovation (DOI) theory.

Overall, the innovation’s attributes informed the authors of the positive perceptions from the public sector, demonstrating that the low adoption of MMC is not linked to any embedded issues with the innovation itself rather being predominantly related to the dynamics between supply and demand. The former (supply), reflected a failure in communicating confidence, and the latter (demand), attained characteristics that are limiting wider uptake.

To the best of the authors’ knowledge, this is the first study to apply the DOI theory to reveal the relationship between UK public clients’ perceptions and their decision-making. Moreover, this paper addresses the scant attention to the use of theories to explain the flow of innovations in the construction context.

This study aims to analyze and understand mutual relationships between lean and agile enabling factors in the context of the construction domain and put forth the most driving and dependent enablers for a hybrid lean–agile system (HLAS).

A threefold research flow is adopted. First, the content analysis of literature stemmed from the identification of preliminary enablers (n = 26). Second, a questionnaire survey (n = 101) is administered to downsize enablers to ten using the relative importance index. Further, in the third phase, interpretive structural modeling (ISM) and the cross-impact matrix multiplication applied to classification analysis were performed amid the ten key enablers through pairwise interactions from subject matter experts (n = 19). Hierarchical representation of enablers has been presented, which facilitates a conceptual model for application in the construction industry.

The ISM model demonstrates a three leveled classification structure that showcases direct and transitive relationships among various enablers. “Use of project management tools” is the driving enabler, followed by “collaborative and common data platform working” and “institution of team training” are critical enablers to HLAS adoption.

A structured way of identification of interdependent among lean–agile is a vital unique contribution to this study. The results serve as a knowledge base for practical adoption of HLAS and may prove to be influential in developing a framework for easier adoption in developing countries.

Many economic, political and socio-cultural events in the 2020s have been strong headwinds for architecture, engineering and construction (AEC). Nevertheless, technological advancements (e.g. artificial intelligence (AI), big data and robotics) provide promising avenues for the development of AEC. This study aims to map the state of the literature on automation in AEC and thereby be of value not only to those researching automation and its composition of a variety of distinct technological and system classes within AEC, but also to practitioners and policymakers in shaping the future of AEC.

This review adopts scientometric methods, which have been effective in the research of large intra and interdisciplinary domains in the past decades. The full dataset consists of 1,871 articles on automation in AEC.

This overarching scientometric review offers three interdisciplinary streams of research: technological frontiers, project monitoring and applied research in AEC. To support the scientometric analysis, the authors offer a critical integrative review of the literature to proffer a multilevel, multistage framework of automation in AEC, which demonstrates an abundance of technological paradigm discussions and the inherent need for a holistic managerial approach to automation in AEC.

The authors underline employee well-being, business sustainability and social growth outcomes of automation and provide several managerial implications, such as the strategic management approach, ethical management view and human resource management perspective. In doing so, the authors seek to respond to the Sustainable Development Goals proposed by the United Nations as this becomes more prevalent for the industry and all levels of society in general.