| Construction 4.0 (Hong et al., 2021; Kumar, 2018; Li et al., 2020; Wu, 2018) | BIM Level 2 “BIM supported simulations and collaboration” is implemented as collaborative working and information exchange methods | 4.15 | 0.894 | 9 |
| Cloud-based common data environment is one of the main challenges for the full implementation of BIM Level 3 “BIM integrated with IoT” | 3.88 | 1.060 | 14 |
| BIM clash detection and resolution reduce rework and delayed schedules | 4.25 | 0.947 | 5 |
| 4D BIM planning and scheduling are used for progress monitoring | 3.71 | 1.289 | 18 |
| Reality capture technologies are used for automated data collection from site | 3.71 | 1.073 | 18 |
| From 3D point clouds to as-built models are used to revise design and update the progress of construction | 3.88 | 1.215 | 14 |
| Digital reality improves efficiencies by making smart and informed decisions | 4.37 | 0.841 | 3 |
| Digital twin (Alizadehsalehi and Yitmen, 2021; Deng et al., 2021; Meža et al., 2021; Yitmen and Alizadehsalehi, 2021a, b; Yitmen et al., 2021) | DT helps to formulate a data-centric mode of smart planning and construction | 4.21 | 0.848 | 6 |
| DT facilitates generative design process by using AI | 3.79 | 1.073 | 16 |
| DT helps to optimize creativity in design process | 3.85 | 1.073 | 15 |
| DT helps support visualizations, simulations and scenario generations applications | 4.42 | 0.801 | 1 |
| DT leverages the data streaming from a variety of site monitoring technologies and artificially intelligent functions | 4.13 | 0.950 | 10 |
| DT provides accurate status information to proactively analyze and optimize ongoing design, planning and construction | 4.13 | 0.971 | 10 |
| A DT provides real-time and effective decisions based on well-informed and reliable what-if scenario assessments | 4.27 | 0.910 | 4 |
| DT provides long-term feedback for design and planning | 4.23 | 0.942 | 6 |
| Internet of things (Gamil et al., 2020; Mahmud et al., 2018; Oke and Arowoiya, 2021; Woodhead et al., 2018) | IoT is included in DT as connecting the physical and virtual worlds | 4.17 | 1.004 | 8 |
| IoT facilitate to integrate and share data by a network of interconnected physical devices | 4.19 | 0.841 | 7 |
| IoT promote the efficiency of the data collection, data transmission, data processing based on cloud computing | 4.38 | 0.771 | 2 |
| IoT solution support real-time data transformation and instantaneous data analysis | 4.25 | 0.860 | 5 |
| IoT-based sensing systems help feasibly track the progress and monitor the worksite | 3.96 | 1.102 | 13 |
| IoT help to automate the real-time decision making | 4.19 | 0.886 | 8 |
| Deep learning (Akanbi et al., 2020; Darko et al., 2020; Hou et al., 2021a; Zhang et al., 2019) | Current challenges promote exploiting the use of DL in tackling construction problems | 3.87 | 0.950 | 13 |
| DL facilitate the huge amount of recorded data to analyzed and offer actinal insights for better supervision and decision-making | 4.02 | 0.918 | 8 |
| DL is required to construct a self-reliant, self-updatable and self-learning DT | 3.96 | 0.928 | 13 |
| DL enables autonomous DT that generates predictive insights and foster continuous optimization processes | 4.10 | 0.934 | 11 |
| DL facilitate to automate generative design process | 3.79 | 0.997 | 16 |
| DL support to solve cash flow prediction, onsite safety and project risk mitigated analysis in construction | 4.06 | 0.873 | 10 |
| Process Optimization (Akanbi et al., 2020; Matar et al., 2013; Oprach et al., 2019) | The simulation, prediction and optimization abilities of a DT are inter-dependent and act in unison in solving the problems in planning, design and construction | 4.02 | 0.874 | 12 |
| DT facilitates dynamic optimization technologies that enable real-time system reflections and automatic model evolvement with updated data feed | 4.13 | 0.841 | 10 |
| Optimization process depends on the simulated prediction (“what will happen?”) applied for planning, or various other construction management objectives | 4.25 | 0.813 | 5 |
| DT enables pro-active modeling, tracking and optimization of construction processes and their associated off- and on-site resources | 4.02 | 0.939 | 12 |
| Optimization would be fully entrusted to the DT's goals and learning patterns in agent-driven socio/technical platforms | 3.73 | 0.931 | 17 |
| | OM | 4.064 | | |