This paper presents a systematic review of Construction 4.0 in the context of the building information modeling (BIM) 4.0 premise. It comprises a review of the industry in the pre-fourth industrial revolution (4IR) age, the current and anticipated development of the 4IR, Construction 4.0’s origin and applications, and the synergy of its main drivers, i.e., the synergy of BIM with the internet of things (IoT) and big data (BD). The main aim of the paper is to determine the Construction 4.0 drivers and to what extent are they initialized by the 4IR, their development and their synergy with BIM, and the direction of BIM’s implementation in the construction phase. It was found that the main drivers of Construction 4.0, which originated from the 4IR, are BIM, IoT, and BD, but with specific implementations. The results of the analysis of BIM with IoT and/or BD revealed that the integrative approaches combining the aforementioned drivers show signs of project enhancement by providing significant benefits, such as improved real-time monitoring, data exchange and analysis, construction planning, and modeling. Furthermore, it was revealed that the main drivers are mostly applied in the project’s preconstruction phase, which is continuously developing and becoming more automated. The state-of-the-art review presented in this paper suggests that BIM is in transition, adopting Construction 4.0 to become BIM 4.0.

The fourth construction industry revolution (i.e., Construction 4.0), driven by the fourth industrial revolution, introduces technological novelties to the construction industry in the direction of utilizing automation and digitalization potential. Various levels of maturity and adoption of these technologies have been identified separately in previous studies. In this study, a state-of-the art literature review is presented with the aim of determining the genesis and current levels of digitalization and automation, as well as their interoperability, among the main construction projects’ life-cycle phases. The results revealed that the construction project life-cycle phases are indeed at significantly different digitalization and automation levels. The initiation phase was found to be at a low level of digitalization and automation, the design and planning phase at a high level of digitalization with a low level of automation, and the execution phase at low-level digitalization with a higher level of automation. Since the topic is continuously developing, this research could be conducted in the near future to determine the advancements in comparison to the current conclusions.

The Fourth Industrial Revolution (4IR) introduced positive changes to some industries, while for most construction industries it is still enthusiastically anticipated. The 4IR focusing on the construction industry in literature is known as Construction 4.0. The Construction 4.0 concept is invoked to transform the current ways the construction industry operates while ensuring benefits, such as reduced overall construction projects’ costs and duration, improved quality and work safety, etc. Due to the increasing web usage, it is anticipated that the 4IR technologies will achieve full potentials by the uprising of the fifth generation technology standard for broadband cellular networks (i.e. 5G). One of the most important 4IR technologies is found to be the Internet of Things (IoT). In this perspective, a construction monitoring approach, more precisely a model for construction detection and object spatial/time positioning, is presented in this paper. While still in its initial phase, the model was tested and verified in the laboratory environment for small-scale object detection. It was found that the quality of the model will be significantly improved with the use of the 5G network, while the objects’ pool, as big data required for the model’s deep learning, is highly dependent on the IoT.

PurposeReady-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.Design/methodology/approachThe presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.FindingsThe system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.Originality/valueThe suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.

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