A holistic review of off-site construction literature published between 2008 and 2018

Off-site construction (i.e., OSC) has become an emerging research domain in the recent decade. Through a three-step holistic review approach incorporating bibliometric search, scientometric analysis, and in-depth qualitative discussion, this study contributes to the body of knowledge in OSC by critically reviewing and summarizing: 1) the latest research keywords and main research topics in OSC; 2) the performance of OSC compared to that of conventional construction approach; 3) current research gaps in integrating OSC with other emerging construction concepts; and 4) future research directions in OSC. OSC is a domain that can be extended to cross-disciplinary research from the perspectives of engineering, management, and technology. Existing research have been focusing on many research disciplines, such as structural behaviors and joint connections of prefabricated components, scheduling and planning of off-site activities, as well as performance evaluation of OSC. However, further research is needed in integrating the emerging digital construction technology, integrated project delivery method, lean construction, and issues of sustainability of OSC. There are still limited studies linking OSC to the concept of Design for Manufacturing and Assembly. Future research should also adopt a larger database and allow for comprehensive evaluation of OSC performance

Decision factors for the feasibility study of developing a prefabrication plant

A feasibility study is one of the key phases for developing a prefabrication plant, which requires an analysis of many influencing factors. This research aims to identify the main decision factors for the feasibility study of developing a prefabrication plant. To this end, a thorough literature review and semi-structured interviews with the subject matter experts (SMEs) from different countries were conducted. Analyzing the findings, this study identified 35 decision factors for the feasibility study, such as upfront costs, operation and maintenance costs, market demands, government incentives, design flexibility, plant location, logistics management and costs, and technological constraints. The results of this study will help the industry practitioners, who are seeking to develop prefabrication plants, in their decision-making process during the feasibility study phase

Automated Monitoring of Construction Operations for Data-Driven Decision Making

The continuous improvement of construction operations requires a systematic approach of monitoring and making appropriate control actions. However, the lack of real-time information hinders this workflow and eventually compromises timely and effective decision-making. Project managers spend a great deal of time and effort to solve problems emerging from lack of timely information, poor coordination, and inaccurate out-of-date data. Emerging technologies like advanced data analytics, the internet of things, and superior computational power can aid in obtaining real-time information and actionable insight from the construction site. This is reflected in the growing use of emerging technologies to automatically monitor construction activities to improve the efficiency of construction management. The overarching research goal of this dissertation is to advance the body of knowledge and practice by integrating emerging technologies with project monitoring for data-driven decision-making. Specifically, this research develops a systematic framework to automatically identify activities performed by construction resources and then uses this real-time information to optimize the operations for data-driven decision-making. The methods developed in this study are applied to two different types of construction operations: heavy civil construction, and prefabricated construction. For both types of operations, first consumer-grade sensors, such as inertial measurement units (IMUs), microphones, RFID sensors were used to automatically identify, and track activities performed in the construction site utilizing machine learning and deep learning algorithms. Then the output from the activity identification framework was used as inputs to simulation models for dynamic productivity estimation and optimization of the operation to enable data-driven decision-making. This study contributes to the body of knowledge by providing a means for automated monitoring of construction operations using emerging technologies and assessing the use of simulation modeling for data-driven decision-making

BIM no planejamento de empreendimentos com sistemas de painéis pré-fabricados: uma alternativa para obras de habitação social

A pré-fabricação na construção ou construção off-site é apontada por pesquisadores como uma alternativa de industrializar o processo construtivo e atingir melhores níveis de desempenho. A utilização dos sistemas de painéis pré-fabricados, por sua vez, é considerada uma opção mais flexível e de execução mais simples dentre os diversos tipos de construção off-site existentes. Estes sistemas também são apontados como ideais para a construção de habitações de interesse social, em razão do seu alto desempenho sustentável e execução simples. Além disso, devido ao déficit habitacional verificado em países menos desenvolvidos, novos métodos construtivos que tornem a construção de habitações populares mais rápida e barata é necessária. Portanto, o presente artigo busca investigar, por meio da metodologia de Revisão Sistemática da Literatura, a aplicação de Building Information Modeling (BIM) na construção com painéis pré-fabricados, de modo a chamar atenção para o potencial de uso dessas tecnologias como forma de suprir a demanda por moradia em países emergentes e subdesenvolvidos

Robotization and digitalisation in the construction industry

Abstract. Industry 4.0 has emerged as a famous concept in the last few years to describe the significance of digitisation and robotization in the smart manufacturing environment. The advancements in robotics, digital software, and smart technologies have allowed a new wave in the construction industry. The construction industry is the major economic pillar and provides a significant impact on the overall GDP of the country. Despite the predominant pillar, it is considered as the poor innovator and late adopter of new technologies, which ends up with delays and cost overruns in their construction projects. Considering this aspect, the research emphasises the importance of adopting the latest technologies in the construction industry in order to enhance the productivity and efficiency of various processes. This study seeks to examine existing robotization and digitalisation practices in the leading construction companies and intends to provide the required improvement ideas in this research domain. The empirical results revealed that the majority of the case companies lack basis to implement the latest technologies in their construction activities. They believe that effective use of the available technologies is an asset, but it is a long process to be achieved. Thus, the thesis is concluded by providing the critical information regarding the adoption of latest technologies and proposes a framework that can help to enhance the robotization and digitisation practices to improve the performance of the construction activities. The mentioned framework mainly focusses on elements that this research found as a potential need for companies to implement. This framework has a future scope for validation and also key elements of the framework can be utilised for further research

Developing a skill profile prediction model for typologies of offsite construction

The aim of the current research was to develop a skill profile prediction model for the typologies of offsite construction (OSC). This research aim was achieved via five research objectives, from which the key findings and research outcomes were generated. OSC is perceived as an effective solution that can be implemented to address the issues evident in traditional construction. Some of the benefits OSC generated include better working conditions, improved productivity, efficiency, reduced wastage, and improved sustainability. Industry 4.0 has promoted OSC as a way to improve the uptake of new technologies in factory-based manufacturing and onsite assembly processes. Such technological advancements can have a significant impact on the skills used in OSC, as some of the existing skills in the construction industry may be eliminated or substituted (e.g., with those in other industries), and new skills may emerge based on industry needs. The magnitude of these possible OSC skill variations has not been a focus in previous studies on OSC. As such, the current research aimed to develop a skill profile prediction model for the typologies of OSC, through the adoption of a case-study based, qualitative research method. The research generated several significant outcomes: the validated OSC typology, the OSC skill classification developed through a logical approach, and a preliminary model for OSC skill prediction. The model can assist in forecasting future OSC skill requirements. Apart from the abovementioned outcomes, deriving a unit of measurement for skill prediction and identifying the complex, non-linear relationships between OSC types and skill variations represent the key outcomes of the research. As such, the research contributes to the current body of knowledge through its development of a unique OSC typology, a master list of onsite and offsite skills, an OSC skill prediction model and a methodology for the prediction of OSC skills. The focus on OSC elements in buildings rather than infrastructure projects, incorporating a limited number of case studies and developing a preliminary model rather than a market-ready product for OSC skill prediction represent the limitations of the research. Future research directions that could be taken to expand on the findings of the current research are as follows: evaluating the skill variations of different building types in the context of varying predominant materials and conducting a fundamentally quantitative study for OSC skills prediction