The small and medium-sized enterprises (SMEs) which are the backbone of any economy are often on the disadvantaged side of the digital divide in the construction industry. With the advent of building information modelling (BIM), the SMEs are facing challenges and are slow with its uptake. Hitherto, extant research studies on BIM have focused primarily on the large firms and there is an observed trend of underrepresentation of the SMEs in BIM studies. Thus, this paper aims to investigate the major drivers of sustainable adoption of Building Information Modelling in SMEs and the dynamics of these drivers in developing countries using interpretive structural modelling approach and Matrice d’Impacts croises-multipication applique a classement (MICMAC) analysis. The findings reveal that organizational readiness is of utmost importance for the proliferation of BIM in SMEs. Also, the independent drivers which are the most important drivers consist of BIM characteristics, internal and external environment drivers and thus portray the BIM adoption as a complex socio-technical system. This study categorizes the drivers for easy intervention of SMEs’ managers and policymakers. It contributes to the nascent studies of BIM adoption in SMEs of developing countries.
Background: Three-dimensional (3D) modeling and visualization of temporary site facilities is instrumental in revealing potential space conflicts and refining time and cost estimates. This research focuses on implementation of photo-based 3D modeling in a time-dependent, dynamically-changing context. Methods: We propose a cost-effective modeling technique to obtain dynamic dimension measurements of a moving object. The methodology resulting from integrating photo-based 3D modeling and robotic total station tracking technologies better caters to the application needs of visualization and measurement in construction that are critical to operational safety and structural accuracy. The computational foundation of photogrammetry is first addressed then the modeling procedure and the system design described. Results: In a module assembly yard, a rigging system being lifted by a mobile crane was identified as the moving object. The length and the length changes of twelve slings on a newly-engineered rigging system at different stateswere measured in order to ensure quality and safety. Conclusion: The proposed technique relies on utilizing two robotic total stations and three cameras and provides a simple, safe and effective solution to monitor the dimensional changes of a temporary facility in the construction field.
Project risks must be managed to deliver construction projects on time and within budget. In recent years, the New Engineering Contract (NEC) provides an alternate contracting method for procuring construction projects. As stipulated in the NEC contract, NEC risk register must be used to record any project risks. The risk register is designed to record each risk item in the context of textual description, likelihood, and consequence. However, it is time-consuming to identify, quantify, and analyse NEC project risks based on experience, questionnaire, simulation, and data-mining approach. Any method to fully utilise the records of NEC risk registers of past projects for managing NEC project risks remains unexplored. As such, a data-driven approach is proposed to categorise common risks of NEC projects and to analyse risk rating of risk categories by combining the use of text mining analysis and decision tree analysis. A practical case study in Hong Kong is used to illustrate the method of application. Top four common types of NEC project risks, which are ground and utilities, design information, structures, and workmanship, were identified, quantified, and analysed. The new approach helps NEC project planners to identify, quantify, and analyse NEC project risks time-efficiently.
The construction industry relies heavily on the use of equipment. Equipment management for a single project is, in itself, challenging, and large contractors who want to achieve long-term success must also manage equipment at an intra-organizational level. While vast amounts of data are collected and updated dynamically to track equipment status within an organization, current practices do not consider these data during the decision-making process. Rather, companies often rely on a single metric, equipment utilization, for evaluating management performance. Inspired by the ability of social network analysis (SNA) to examine the interactions and relationships between people or objects, a SNA-based method for investigating equipment movements between project sites and equipment shops is proposed. This study proposes a novel performance metric, the direct dispatch index (DDI), which adds a distance weight to the clustering coefficient of SNA, to measure equipment dispatching performance from equipment logistics data.Historical equipment logistics data from the equipment and project management systems of a company in Alberta, Canada, were used to demonstrate the functionality and feasibility of the proposed approach. The methodology was found capable of evaluating the logistical effort associated with equipment dispatch and planning, thereby enhancing equipment management through improved decision-making.
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