Introduction

The Architectural, Engineering, and Construction (AEC) industry plays a crucial role in the socio-economic development of a country (Oladinrin et al., 2012), (Wells, 1985). In fact, the construction sector accounts for 6.7% and 7% of the Gross Domestic Product (GDP) of the United Kingdom and Canada, respectively (DBIS, 2013). Typically, this industry employs a significant portion of a nation's workforce, and thus, efficient management of the AEC industry has direct implications for the national economy. However, the AEC industry has been slow at studying and employing modern management tools, techniques, and practices and is criticized for not directing optimal efforts toward leveraging the collaborative work nature (Ahuja et al., 2010; Lam et al., 2010; Martínez-rojas et al., 2016; Sardroud, 2015).

The key bottom lines of the AEC industry are schedule, cost, quality, and safety considerations, which is a particularly accurate assumption in the context of construction project management (Doloi, 2013; Khademi, 2014; Memon et al., 2011). To efficiently resolve construction management challenges by executive relevant solutions, the AEC industry must explore and analyze innovative management solutions being successfully applied by other industries (e.g., manufacturing, oil, and gas). Other related industries, such as manufacturing, oil, and gas, have been successful at optimizing the efficiency of their processes by leveraging innovative decision support methods integrated with Enterprise Resource Planning (ERP) systems (Raicu et al., 2017).

In the recent past, modern technologies, such as Geographic Information Systems (GIS), Artificial Intelligence (AI), smart structural monitoring and sensing technologies, augmented reality, and virtual prototyping, have been quickly making their mark in the construction sector (Faghihi et al., 2015; Martinez-Rojas et al., 2018; Martínez-rojas et al., 2016). Modern innovations, such as digital twinning, 3D virtual reality systems, cloud solutions, mobile apps, and AI, among others, can potentially transform the future of the AEC industry (Blazquez, 2014; Demirkan, 2015; Pantano et al., 2017; Papagiannidis et al., 2013). However, the dynamic nature and the complexities surrounding the AEC industry, such as frequent changes in regulations (e.g., safety and sustainability), fluctuating workload, multiple role demands, conflicts, stressful workforce, inflation, learning curves, climate change, weather impacts, lack of workmanship, poor quality materials, and numerous change requests continue to pose challenges to the construction managers (Awwad et al., 2016; Dodanwala & Santoso, 2022; Dodanwala & Shrestha, 2021; Dodanwala et al., 2021, 2022a, b; Turner et al., 2008). Consequently, it is imperative to take into account an array of variables during the decision-making process within the construction environment, making it an arduous task.

Since construction projects involve multiple stakeholders, there is a constant exchange of a large amount of data within short periods of time (Crotty, 2012; Latiffi et al., 2014). In the face of reliable, accurate, and readily available data, the construction sector can transform into a “smarter industry,” expanding its data storage, monitoring techniques, and decision-making capacities. The lack of project data coordination (Chassiakos & Sakellaropoulos, 2008; Forcada et al., 2007), technological awareness, and state-of-the-art techniques in the construction industry (Adriaanse et al., 2010; Y. Lu et al., 2014a, 2014b; Martínez-rojas et al., 2016; Rezgui et al., 2011), make up the major challenges deterring the construction sector from leveraging smart, state-of-the-art technologies. Building Information Modeling (BIM) is the most optimal solution for the mobilization of the construction sector to the extent that is comparable to other related industries. BIM has played a pivotal role in the recent paradigm shift that emerged in the construction sector. BIM is a tool capable of digitally representing the physical and functional characteristics of buildings, systems, and components that positively contribute to the life cycle management of construction projects (Eastman, 1999; Popov et al., 2010; Sacks et al., 2008). Moreover, BIM provides a multidisciplinary environment wherein all project participants collaborate on a common platform, thereby simplifying the decision-making process and facilitating a collaborative and real-time decision-making culture (Benjaoran, 2009; Fu et al., 2006; Vanlande et al., 2008), (Fu et al., 2006). The BIM platform is an information repository equipped with the functions required to support construction managers in their decision-making process, enhancing the efficiency and effectiveness of the projects.

The use of predictive and smart techniques in the BIM platform can be considered in the context of the decision-making process in the construction environment. Predictive decision-making includes complex functions and their optimization and evidence-based decision modeling, heavily depending on the reliability and accuracy of data (Wang et al., 2020). Predictive decision-making methods act as tools to minimize the gap between the known and the unknown. Incorporating predictive techniques can make the construction management decision-making processes more efficient and reliable. Prior to the development of BIM, there was an absence of a reliable basis capable of supporting predictive decision-making in construction. BIM can store physical and functional data using its multi-dimensional nature along with geometry (3D), schedule (4D), cost (5D), operation (6D), sustainability (7D), and safety (8D), which enables the extraction of project-specific data for further analysis and prediction when there are multiple dimensions within a project (Azhar et al., 2008; Hardin, 2009; Kymmell, 2008; Latiffi et al., 2014; Sebastian, 2011). There has been a growing interest in the use of BIM for schedule management, cost management, safety management, and quality management in the AEC industry (Azhar et al., 2008; Latiffi et al., 2014). Currently, research pertaining to the numerous capabilities of BIM is highly dispersed; there is a need to merge them and identify innovative research, particularly in the context of construction management.

There is no dearth of review articles focusing on BIM adaptation in the construction sector, with scholars focusing on different contexts underlying this industry. Evaluations of building sustainability (Ansah et al., 2019; Ayman et al., 2020; Chang & Hsieh, 2020; Kwok et al., 2019; Lu et al., 2017; Santos et al., 2019), building fire safety (Davidson & Gales, 2021), automation of code checking process (Ismail, 2017), are some of the common themes touched upon in literature review papers. Furthermore, efforts are being made by the researchers to further explore the possibility of BIM adoption in the construction industry (Eadie et al., 2013; Smith, 2014) along with BIM-based site collaboration (Liu et al., 2017; Oraee et al., 2017), and specific management practices related to BIM (Wong et al., 2014). Hence, the need for a critical review of BIM-based construction management decision-making is glaringly evident to pave the way for future researchers.

The objective of this paper is to critically review BIM-based platforms as tools for predictive decision-making in construction management. This review was performed based on four challenges plaguing construction management: schedule, cost, quality, and safety management. In this review, several contemporary BIM-based solutions were identified to offset these challenges. The knowledge derived from this extensive review was utilized to devise a strategical roadmap to support predictive decision-making in construction projects. This map discusses how BIM can support predictive decision-making in key stages of a project life cycle, i.e., conceptual development, design, procurement, and construction. The findings of this research aim to inform construction management researchers and assist them in their future research. Among the far-reaching benefits of predictive decision-making are reduced schedule delays, project cost reduction, and improved project quality and safety. Thus, the construction industry will also benefit from the potential strategies outlined in this paper.

Methodology

Keyword search in academic databases has been a popular research method used in several review studies (Lin & Shen, 2007; Xue et al., 2010; Zheng et al., 2013). In this study, the literature search was conducted across the Scopus Engineering Village and Google Scholar databases, which are among the most prominent and extensively utilized research databases renowned for their wide range of coverage. As a first step of the study, a rigorous search was conducted using the database’s title/abstract/keyword field. The study utilized various keyword combinations during this process (e.g., BIM, schedule, cost, construction safety, quality management, and construction) and employed journal articles, conference proceedings, books, and book chapters that were published in English for the bibliometric review process. The journal articles published from 1974 to 2021 were chosen as a filtering criterion. Any articles published prior to 1974 were omitted since BIM is a concept that has been extensively researched only in the last couple of decades. Finally, a content analysis was conducted against the abstracts of the filtered journal articles, and 385 articles were shortlisted due to their potential relevance to the present study. Finally, after a detailed content analysis of the 385 articles, 175 were chosen for the present review. These articles were reviewed to identify BIM-based construction management strategies. Table 1 presents a summary of published literature on this subject domain.

Table 1 Summary of the published literature
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Construction management challenges

Schedule management challenges

To create a schedule, a wide range of information is necessary, although the final schedule does not explain the process and assumptions behind the activity sequences (Aredah et al., 2019). To devise an effective schedule, details, such as resource and equipment allocation, time–cost trade-offs, constructability issues, and optimum productivity, should be taken into account (Koo & Fischer, 2000). The precision of a project schedule may vary based on the construction manager's experience and knowledge (Cherneff et al., 1991). A project planner is important to the process of construction since it determines the sequence of construction activities enabling the efficient allocation of resources and the effective use of limited site spaces. Currently, construction management decision-making is performed through reliance on prior experience that drives the analogous, parametric, and triple-point scheduling mechanisms. When decision-making is informed by prior experience, some challenges faced include the handling of a large volume of schedule information and the inability to perform detailed analysis for different scenarios (e.g., what-if analysis) from the data acquired from the repositories and experts (Behzadan et al., 2015). The scheduling tools that are in use today require complex and time-consuming data entry to reflect the changes incorporated (Davis et al., 1974). Furthermore, the final schedule should be synchronized with all project participants and must take into account any potential uncertainty to make it more accurate (Davis et al., 1974). Traditional scheduling methods are error-prone and could be daunting in these instances (Martínez-rojas et al., 2016).

Cost management challenges

Cost estimators use historical cost data and market trends to estimate project costs (Chou, 2011). The cost estimation challenges are similar to the aforementioned challenges in the schedule management section. In addition, data limitations, lack of proper data management, repository maintenance and handling, and incorporation of cost uncertainties further complicate the process (Baloi & Price, 2003; Ji et al., 2011a, 2011b; Lawrence et al., 2014; Staub-french et al., 2002). Other hurdles for cost management include poor site management, information delays, changes in scope, aggressive competition, slow decision-making due to its isolated nature, and the usage of traditional cost accounting methods and software (Iyer & Jha, 2005; Shane et al., 2009; Trost et al., 2003; Williams & Gong, 2014). Traditional estimating software links elements from a building design to cost items listed in a cost database. However, to optimize project costs, the cost estimation process must also consider the estimator’s rationale. However, traditional estimating methods lack the functionalities to interlink the estimator’s rationale and the design and cost information. Complexities in modern construction include excessive design changes as dictated by client requirements, which lead to project cost overruns. Therefore, there is a need for automated technology capable of storing and using the estimator’s rationale to incorporate any potential uncertainties (Timberline, 2001).

Safety management challenges

Safety incidents adversely affect project efficiency and the economy (Zhang et al., 2013). For effective safety management, potential hazards and risks must be determined. Hazard identification can commence from pre-construction and span throughout the operational stage (). However, hazard identification is not devoid of challenges ranging from inaccurate data sharing among project participants to decision-making based on uncertain information, as well as non-standard procedures and unorganized tasks (Martínez-Rojas et al., 2018; Zhang et al., 2013). Identifying the cause-effect relationship for safety incidents will provide a plausible basis for safety management (Zhang et al., 2013). Historical data-based accidents analysis presents generic data for identifying trends and causes underlying safety incidents in the construction sector and incorporates or suggests potential remedies to decision-makers to enhance project safety. Therefore, it becomes possible to implement preventive measures to avoid or reduce the impacts of safety incidents (Han et al., 2014).

Quality management challenges

Construction quality management is an umbrella term used to refer to control measures that avoid defects, errors, rework, and failure (Mills et al., 2009). Quality issues in construction projects result in cost overrun, time overrun, loss of potential business to the organization, and non-conformance to requirements (Chin et al., 2004). Construction quality issues may arise due to multiple reasons, such as documentation errors (Cusack, 1992), poor managerial practices (Rounce, 1998), construction error, change and omission, and poor workmanship and materials (Construction Industry Institute, 2003). Quality management is a data-intensive process. Traditional methods utilize data gathered from on-site inspection and regulation, wherein managers manually record paper-based documents, leading to inefficient management. Thus, similar to the previous sections, predictive decision-making methods and automated data capturing and monitoring are necessary to avoid defects in construction projects. Figure 1 summarizes the above-mentioned challenges in construction management pertaining to schedule, cost, safety, and quality management aspects.

Fig. 1
figure 1

Construction management challenges

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Predictive decision-making

Industries and organizations around the world are incorporating predictive decision-making methods to enhance the economy and productivity of organizational operations (Doloi, 2013). Predictive decision-making includes complex functions, optimization, and evidence-based decision modeling, significantly depending on the reliability and accuracy of data (Wang et al., 2020). This approach has emerged from data science, where it identifies patterns in big data to forecast any uncertainties and risks. Moreover, the potential implications of decisions must also be analyzed to optimize the solution (Mosavi et al., 2018).

Predictive decision-making is helpful in construction projects that require handling large amounts of data with several conflicting attributes (Jato-espino et al., 2014). This mechanism aids managers in analyzing complex scenarios and enables them to undertake appropriate precautions prior to the commencement of a project (proactive approach as opposed to a conventional corrective approach) (Dziadosz & Konczak, 2016). For example, predictive decision-making is a handy mechanism that can help with on-time project completion without exceeding the planned budget (Mohamed et al., 2009). The application of the predictive decision models increases the accuracy of estimates at the pre-project planning stage. This accuracy, in turn, facilitates the completion of construction projects under the predetermined cost and time baselines (PASC, 2020).

Decision-tree is a basic predictive decision support method that summarizes the potential events that could unfold due to a specific decision. Joshi employed a decision tree to analyze the labor productivity in construction projects (Joshi, 2010). In certain other projects, expert systems were used by some investigators as another predictive decision-making method (Bryant, 2009a). Some expert systems utilize Case-based Reasoning (CBR), which is an AI-based approach with a knowledge base containing information from real-world cases to imbibe the skills required to manage novel situations (Zhu, 2013). Among the most common methods adopted in CBR is Monte Carlo simulation (Kumar & Viswanadham, 2007). Conversely, some expert systems use Rule-based Reasoning (RBR), which defines a set of connected rules that lead to a given conclusion. This knowledge is most commonly represented with the assistance of "if–then" rules (Bryant, 2009b). Two of the most common RBR methods are the Fuzzy Inference System (FIS) and Adaptive neuro-fuzzy inference system (ANFIS). However, AI, Bayesian methods, and Neural Networks are also used for construction-based decision-making, although they are yet to be adopted on a larger scale.

BIM for predictive decision-making in construction management

As a repository of project life cycle data, BIM can be an effective tool in executing predictive decision-making (Lam et al., 2010; Wells, 1985). In the following sections, various methods used for BIM-based decision-making in construction management are discussed.

BIM-based schedule management

BIM-based schedule management is gaining significant traction today (Aredah et al., 2019; Curry et al., 2013; Liu et al., 2015; Moon et al., 2015; Song et al., 2012; Wang, 2019; Wang & Song, 2016). The Fourth Dimension (4D) of Building Information Modeling is a characteristic of BIM-based schedule management technology that is built on the information embedded in the model. Schedule management can model and analyze sequencing activities within a construction schedule with respect to time and space. In the domain of BIM-based schedule management, previous researchers have focused on the following three areas: i) minimizing schedule overlap and delay, ii) automated scheduling, and iii) detection of errors and missing data.

Overlaps in activities and potential delays in a construction site lead to changes in the project timeline and schedule. If specific project activities are delayed, it can risk deferring the completion date of the entire project (Dehghan & Ruwanpura, 2011). Overlapping tasks may emerge in both highly intricate project schedules as well as high-level summary schedules. Moon et al. developed an active construction schedule management system by integrating BIM with fuzzy-based risk analysis algorithms and genetic algorithms (Moon et al., 2015). This method was used to create an optimal schedule with minimum overlaps. Management of a construction project in the design and execution phase heavily depends on easy access to data (Chassiakos & Sakellaropoulos, 2008; Lu et al., 2014a, 2014b). The integration of BIM and GIS is of great significance in the geospatial section because of the rising research interest in smart cities, Internet-of-Things, and urbanization (Deng et al., 2019a, 2019b). Bansal and Pal’s early attempts at 4D visualization of construction sequence were carried out by linking GIS and project schedule with a 3D model of the project (Dehghan & Ruwanpura, 2011; Wang & Song, 2016). This process informed and educated less experienced project participants about what must be built and its scheduled time and location of installation. Irizarry and Karan integrated BIM and GIS to determine the optimal locations of tower cranes. Through this research, they solved the problem of identifying optimal locations for a minimal number of tower cranes to eliminate potential conflicts in the schedule (Irizarry & Karan, 2012).

Furthermore, Wilfredo et al. have conducted a study on a machine learning method used to parse descriptions and spatial time-phased data of a construction project, which were then animated with visual cues (Amir et al., 2019). This research may be viewed as an extension of 4D simulation with a large amount of data embedded for higher accuracy with information associated with activity, relationships, lags, and leads. However, the proposed method is a time-intensive process at earlier stages when data is mined from existing projects. In addition, it requires the development of a database in a standard format that can be automatically identified by a computer program.

Chen and Tang proposed a workflow design integrating BIM and digital programming to address schedule and cost uncertainties during the maintenance stage, which can prevent delays in building maintenance (Chen & Tang, 2019). Chen and Nguyen integrated BIM and Web Map Services (WMS) to simplify and optimize construction material selection (Chen & Nguyen, 2019). This approach provided substantial data related to material, such as sources or vendors, storage locations, availability, costs, and transportation methods that help the project team estimate the final cost and delivery time of the material to guarantee optimal inventory and timely delivery of materials. Integration of semantic web with BIM and GIS can assist the construction manager in improving access to data, eliminating paperwork, recording data in a multimedia format, removing inaccuracies in the documents, and improving collaboration among the project participants (Chassiakos & Sakellaropoulos, 2008). This approach reduces delays and extensive costs associated with traditional document management.

Automated scheduling has been a key domain in BIM-based schedule management research. The estimation of an activity duration is a challenge in the pre-construction stage. Accurate activity duration directly correlates with projecting management experience. Mikulakova et al. proposed a knowledge-based system for automated schedule generation (Mikulakova et al., 2010). This method combined BIM and construction processes with past data from successfully executed projects to determine the project duration (Mikulakova et al., 2010). Developing such knowledge-based systems for predicting work task durations has been a growing domain of research in the recent past (Elwakil & Zayed, 2012). A study conducted by Hexu et al. recommended an automated scheduling approach using an add-on to Autodesk Revit (Liu et al., 2015). The algorithm can define the optimal activity duration considering the attributes of the project and user-defined constraints on different activities. Getuli and Capone developed an ontology-based automatic scheduling system in the BIM platform that utilized construction scheduling knowledge as a foundation (Getuli & Capone, 2019). Knowledge-based scheduling ontology enhanced the semantic representation and efficiency of supporting processes in construction schedules (Getuli, 2020).

Moreover, Wang and Rezazadeh proposed a BIM-based framework to create project schedules of concrete-framed buildings. This method leverages prior knowledge to devise rules after considering building objects and their attributes and then generates a list of work packages, their duration, and sequences (Ziwei Wang, 2019). Li et al. applied BIM and Radio Frequency Identification Device (RFID) to enhance coordination between project stakeholders. This method analyzed the critical schedule risk factors and developed an automated RFID-enabled BIM platform that integrates various stakeholders and information flow to manage risk factors. This research attempted to ensure timely project completion in prefabricated housing construction by addressing schedule risks (Li et al., 2017). Cheng and Chang presented an optimization model for BIM-based site material layout planning (Cheng & Chang, 2019). This method addressed delays in logistics and enhanced the efficiency of construction.

Even though hundreds of studies are being conducted on project schedule management and BIM, only 30%-40% of companies are enjoying its benefits (Amir et al., 2019). The perception toward BIM, availability of practical applications, costs of implementation, and longer learning curves are major drawbacks deterring companies from implementing it. Studies pertaining to complex algorithms, such as machine learning, AI, etc., require large databases related to construction activities. This would be practical only if integrated with a sensor network and a real-time data monitoring platform, which is complex, expensive, and varies with local decision-making approaches. Refer to Table 2 in Appendix for an overview of published literature on BIM-based schedule management.

BIM-based cost management

A fully integrated BIM environment combined with cost estimation software, also known as the Level 03 BIM platform for embedding cost dimension (5D BIM), is an ideal solution for cost management in AEC projects (Eastman et al., 2011). Previous BIM-based cost management research entails three key research areas: i) automated cost estimation and prediction, ii) cost optimization, and iii) Financial risk management.

Automated cost estimation has been the most popular area of research in BIM-based cost estimations. Zhiliang et al. proposed an Industry Foundation Class (IFC)-based model for estimating construction costs. The extended IFC standards presented in this study provide division-item property sets, cost items, and mathematical relationships (Zhiliang et al., 2011). Lee et al. proposed an automated work item searching system developed through the integration of BIM with an ontological inference process. The proposed approach assists cost estimators in employing BIM data to find work items and their quantities. Moreover, it eliminates the need for the intervention of the cost estimator’s subjectivity (Lee et al., 2014). Xu et al. proposed a framework by integrating BIM with semantic web ontology and forward chain algorithm to establish new means of obtaining and deriving data from a BIM model. Such data was used for developing the essential items to perform the quantity take-off. The proposed framework helps the AEC industry with the development of an automated cost estimation system (Xu et al., 2016). Cheung et al. proposed a BIM-based cost estimation module to assess different aspects of building design in the early design stages. The multi-level cost estimation tool presented in this study enables users to automatically obtain measurements from 30 models and evaluate the functionality, economics, and performance of buildings (Cheung et al., 2012). Lawrence et al. integrated BIM with query language to propose a generic approach for creating and updating a cost estimate. The outcome of this approach adds flexibility to cost estimation and enables the estimator to encode a wide variety of relationships between the design and the estimate (Lawrence et al., 2014). Niknam and Karshenas integrated BIM and semantic web service technologies, as well as ontology inference processes to improve the accuracy of cost estimation (Niknam & Karshenas, 2015). Wang et al. proposed a method that utilizes BIM and cost-based progress curves to identify construction progress curves (Wang et al., 2016). This method identifies take-off objects to obtain quantities of cost items related to each activity. This study shows that this method can prevent errors that emerge when manually typing cost-item names (Wang et al., 2016).

Cost optimization leads to more profits with acceptable quality, safety, and schedule in AEC projects. Further, cost optimization ensures that the cost of construction does not exceed the budget and maximizes the profit in the design stages (Rajguru, 2016). Traditional cost accounting methods are often inaccurate and inefficient since the design undergoes frequent changes in the early phases. A study conducted by Pathirage et al. entailed the development of a BIM-based method to highlight change orders and minimize project costs (Pathirage & Underwood, 2015). Faghihi et al. proposed a cost optimization method based on the integration of BIM with Pareto Front analysis. This study presented a tool to help project managers to optimize project cost and scheduling (Faghihi et al., 2016). Eleftheriadis et al. integrated BIM with Genetic Algorithm (GA) and Finite Element Modeling (FEM) to develop a cost optimization approach and embodied carbon of reinforced concrete structures. This approach enables managers to make early design decisions considering the costs and environmental implications (Eleftheriadis et al., 2018). He et al. developed a five-dimensional construction cost optimization model by integrating BIM with GA. The proposed system provides solutions for managers to prevent cost and time overruns in construction projects (He et al., 2019).

Proper financial risk management is crucial for project success since it is meant to ensure that the project does not exceed the budget (Cooke, 1996). According to a study conducted by Huang, unforeseen costs can be reduced from 50 to 15% using BIM. Risk factors due to uncertainty and the inability to visualize the project are mainly addressed through the proposed approach (Huang, 2021). The literature reveals that Cha and Lee proposed a BIM-based framework to identify work items in construction sites and the relationships among activities to reduce human error and increase work efficiency (Cha & Lee, 2015). Sun et al. proposed a project cost and schedule risk early warning model by integrating BIM with Earned Value Analysis (EVA). This study also addresses the problems and challenges of traditional EVA methods that rely on the experience of project participants in construction management (Sun et al., 2015). Shan et al. developed a BIM-based approach for cost management across the processes in high-risk construction projects. This research proposes the reduction of pipeline clashes, reworks, and project costs as potential solutions to minimize the risk of high-risk AEC projects (Shan et al., 2018).

Reviewing BIM-based cost management literature revealed that automated cost estimation has begun to garner more attention in the recent past. Automated cost estimation increases the speed and accuracy of cost prediction (Mittas et al., 2015). BIM-based cost management offers many advantages, such as advanced and automated quantity take-off for cost estimation in highly dynamic environments, estimation based on big data, optimum output choices from different scenarios, and web-based collaboration. However, according to the literature, several challenges continue to deter the implementation of BIM, such as the inability to utilize the software tools to their maximum capacity, cost of implementation, as well as a hindrance in data sharing. Refer to Table 3 in Appendix for an overview of published literature on BIM-based cost management.

BIM-based safety management

Previous research on BIM-supported safety management has focused on (i) enhancing on-site communication, (ii) construction hazard detection, and (iii) safety planning in AEC projects (Zhang & Hu, 2011). BIM supports communication and collaboration, enabling the project teams to share their knowledge and propose safety improvements throughout the project life cycle (Martínez-aires et al., 2018; Zhou et al., 2012). Dossick et al. investigated the role of BIM in augmenting coordination and collaboration in a construction project (Dossick et al., 2010). This study concluded that BIM is necessary to navigate a complex project hierarchy with a large volume of data and facilitate information exchange between project participants. Ganah and John suggested adopting BIM in toolbox talks (Ganah & John, 2015). The BIM-based visual aid will enhance the effectiveness of safety hazard identification and the team’s communication and collaboration. Lin et al. developed a BIM-based intelligent productivity and safety system to aid project stakeholders with the collaborative assessment of the safety performance prior to the project commencement (Teo et al., 2017). Golparvar-Fard et al. proposed the integration of BIM and 4-Dimensional Augment Reality (AR) to present a better visualization of construction operations and their sequences. The authors revealed that this model could provide easy-to-understand and detailed attributes for remote project monitoring (Golparvar-fard et al., 2011). Le and Hsing integrated BIM with a mobile web map service and GIS coordinates to support data exchange in real-time and manage any risks to adjacent buildings and the neighborhood (Le & Hsiung, 2014). Nawari integrated BIM with the Information Delivery Manual (IDM) to resolve problems associated with augmenting the national BIM standard to facilitate more reliable data exchange between project participants, which enhances information quality and ensures prompt communication (Nawari, 2012). Niu et al. presented a BIM-based framework for augmenting construction resources with technologies concerning autonomy, awareness, and the ability to interact with their vicinity to function as smart construction objects. The proposed method enables a safer, greener, more efficient, and more effective construction system (Niu et al., 2016). Park and Kim integrated BIM with AR, location tracking, and a game engine to improve the real-time collaboration between managers and workers. This method helps project safety managers monitor their workers in a safe manner during the construction phase of projects (Park & Kim, 2013). Getuli et al. implemented a BIM and VR-based safety training protocol and safety-oriented planning approach for the construction industry, further enhancing BIM utilization in the safety management process (Getuli et al., 2020a, 2020b). Chen et al. improved and augmented fire safety and safety upskilling through the integration of BIM, the Internet-of-Things, and AR/VR technologies (Chen et al., 2021). Ciribini et al. developed a 4D BIM-based interoperable procedure to conduct safety-based code checking and analyze the construction phase. This proposed process enables managers to ensure construction worker safety (Ciribini et al., 2016).

Identification of causality of project hazards can help with enhancing site safety (Zhang et al., 2015b). Researchers are now focusing on detecting project hazards with BIM. Safety management of a project should be considered starting from the conceptual and design stages to identify and minimize risk factors that would persist throughout the lifecycle. Malekitabar et al. detected more than 40% of potential fatalities in construction projects through five sets of safety risk drivers as construction incident sub-causes to help managers. These safety risk sub-causes can be derived from a BIM model (Malekitabar et al., 2016). BIM has been an effective method for safety management in confined workspaces. Moon et al. developed a BIM-based methodology to identify scheduling and work-space conflict (Moon et al., 2014a). Park et al. used BIM with Bluetooth Low-Energy (BLE)-based location detection to identify unsafe conditions and analyze labor routes by taking into account potential safety hazards. Chavada et al. used BIM in conjunction with the Critical Path Method (CPM) to accurately manage workspaces on construction sites and prevent workplace incidents (Chavada et al., 2012). In the above study, the integrated system enables safety managers to monitor construction workers and prevent safety incidents using BIM-supported decision-making platforms (Jeewoong Park et al., 2017) and allows construction managers to undertake preventive measures during the pre-construction stage (Qi et al., 2014a).

Furthermore, several researchers have used BIM as the data domain to perform safety checks. Qi et al. and Zhang et al. proposed BIM-based methods to check fall hazards (Qi et al., 2014b; Zhang et al., 2015b). Within this context, BIM was integrated with other technologies, such as real-time locating systems, wireless sensing, and real-time audio warning for safety-focused applications. Wang et al. used BIM with range point cloud data to detect fall hazards in geotechnical projects (Wang et al., 2015a, 2015b). Protective measures for falls were proposed upon identifying the fall hazards. Akula et al. presented a method based on the integration of BIM with 3D imaging technologies to identify safety hazards when placing embeds into existing reinforced concrete structures (Akula et al., 2013). Ding et al. used BIM and semantic web technology to model construction risk and develop risk responses. This framework produced a risk map and recommended a risk prevention plan (Ding et al., 2016). Golovina et al. investigated hazard causes related to construction equipment and proposed a GPS and BIM-based method for recording, detecting, and analyzing interactive, hazardous near-miss situations between workers on foot and heavy construction equipment (Golovina et al., 2016). Hu et al. presented a BIM-based framework to detect construction collisions for site entities (Hu et al., 2010a). This algorithm used boundary representation (B-rep) to detect collisions (Hu et al., 2010a). Hu et al. developed a 4D BIM model that provides comprehensive information on dynamic connections between scaffolding systems and the construction process (Hu et al., 2010b). This model was used to analyze the safety of scaffolding and worker behaviors (Hu et al., 2010b). Mihic et al. linked BIM with a construction hazards database for early hazard detection (Mihić et al., 2018). Bannier et al. proposed a BIM-based approach to address the safety challenges associated with limited work-space for piping and steel trades crews (Bannier & Goodrum, 2016). Kim et al. prepared a query set for a BIM model that automatically identifies similar accidents using a project management information system (Kim et al., 2015). Kim et al. integrated BIM with automated data collection and a real-time locating system to reduce the labor exposure time to hazards (Kim et al., 2016a, 2016b). Proactive Behavior-Based Safety (PBBS) is the combination of traditional behavior-based safety management with the Proactive Construction Management System (PCMS). This method enables managers to identify potential causes of unsafe behaviors at the execution stages before an accident occurs by automatically monitoring location-based worker behaviors (Li et al., 2015). Li et al. extended the above study to include PBBS for a BIM model to automatically monitor location-based behaviors, identify the primary causes of unsafe behaviors, and enhance the safety of the construction project (Li et al., 2015). Luo et al. developed a BIM-based method to check the code compliance of the high-risk deep foundation construction projects (Luo & Gong, 2015). Riaz et al. linked BIM and wireless sensors to monitor workers working in confined spaces. The proposed system reduces the safety risk for workers in confined spaces (Riaz et al., 2014). Zhang et al. integrated BIM and GPS to identify and visualize potentially congested workspaces to prevent suffocation hazards for workers (Zhang et al., 2015c).

In addition to the aforementioned studies, many researchers attempted to integrate BIM and Unmanned Aerial Vehicle (UAV) to track worker behaviors in mega construction sites. Teizer et al. used BIM with UAVs and laser scanning technology to automatically track construction workers and identify and prevent potential hazards (Teizer, 2015). Liu et al. employed BIM and UAV technology to enhance the level of safety inspection during the construction phase to enhance site safety (Liu et al., 2019). Cheung et al. developed a system to monitor the safety status via a spatial-colored interface and automatically remove any hazardous gas from the construction site based on the integration of BIM with a wireless sensor network (Cheung et al., 2018). This system uses wireless sensor nodes placed on underground construction sites to collect hazardous gas levels.

The third major area of research in this context is safety management through safety-focused planning and scheduling of the project, which can be considered at the planning stages of the project. Project safety can be enhanced through simulation-based scheduling and planning (Andradóttir et al., 1997). Moon et al. integrated BIM with a genetic algorithm to develop an active simulation for minimizing the simultaneous interference level of the schedule-work-space (Moon et al., 2014b). This approach aids managers in solving schedule-work-space interference and preventing safety hazards. Zhang and Hu proposed a BIM-based framework to analyze conflicts and structural safety problems during the planning stage of the project. They argued that this machine is capable of preventing safety issues and accidents from occurring during the construction phase of the project (Zhang & Hu, 2011). Similarly, Bansal et al. utilized information from the GIS-based activity database in a 4D BIM model to detect safety-based logical errors in the construction phase (Bansal & Pal, 2014). Zheng et al. proposed an ontology-based semantic BIM modeling to promote holistic inquiry of safety knowledge. With its automated safety planning for the analysis of construction site hazards, this system can efficiently prevent workplace safety incidents (Zhang et al., 2015a). Choi et al. proposed a BIM-based framework to handle space planning in pre-construction management. This research proposed a decision support tool to resolve safety issues related to workers in construction sites (Choi et al., 2014a). Mirhadi et al. developed a tool that enables designers to optimize the building layout that supports occupant safety during evacuation (Mirahadi et al., 2019). Similarly, Marzouk and Daour proposed using BIM to simulate evacuation routes for laborers during an emergency (Marzouk & Al Daour, 2018).

Attempts to analyze the fire risk of buildings during the design stage with the help of BIM is a new domain that scholars have been focusing on. Code compliance, fire risk assessment with BIM-based coding applications (Ex- Dynamo, C + , Python, etc.), and plug-ins were created to identify potential risks in the designs and minimize such iterations to ensure the safety of construction (Kincelova et al., 2020; Zhang, 2020). Rania Wehbe et al. developed a mobile app-based evacuation system to respond to fire hazards in a building (Wehbe & Shahrour, 2021). This solution integrated fire spread models and an optimum evacuation path for a safe exit from a building. Most fire and BIM-related researchers have focused on safety issue identification during the design phases of a building.

Based on the above review, it is clear that most studies have focused on the improvement of the health and safety of construction workers, while a few studies deal with resident safety. Many studies continue to focus on the operational stage of a building or the full building life cycle. A significant proportion of previous research has focused on hazard detection. Some studies presented preventive methods in the planning stage, while some others have developed a system for monitoring workers during the construction phase. Table 4 in the Appendix summarizes the published literature on BIM-based strategies used for safety management in construction projects.

BIM-based quality management

BIM-based quality management primarily focuses on three research domains: (i) Lean construction, (ii) Collaboration and communication improvement, and (iii) Automated progress monitoring. BIM is effective in implementing modern management techniques, such as lean construction to reduce defects, clashes, and wastage of time during construction projects (Chassiakos & Sakellaropoulos, 2008; Forcada et al., 2007). Sheikhkhoshkar et al. proposed automated and cost-effective planning to resolve the problem of the conventional construction joint design process (Sheikhkhoshkar et al., 2019). Porwal et al. proposed a BIM-based method for estimating construction waste from change orders. This method can help reduce construction waste by 25% (Porwal et al., 2020). Kim et al. developed a BIM-based framework for estimating demolition wastes in the design phase (Kim et al., 2017). This method estimates the C&D waste generation and waste management costs and provides data for recycling practices, environmental impact assessment, and disaster preparedness.

BIM-based defect identification and management have been frequently researched in the past. Technologies, such as digital twins, AR, and image-matching, have been integrated with BIM to support clash detection and defect identification. Park et al. proposed a framework based on the integration of BIM with AR and an ontology-based data collection template to decrease defects occurring during the construction process (Park et al., 2013). Kwon et al. presented a method that integrated BIM with image-matching and AR to identify omissions and errors at real job sites to enhance defect management (Kwon et al., 2014). Lin et al. presented a methodology that integrated BIM and web-based technology to help site managers track and manage defects. The results of this study showed that the proposed method supports effective visual defect management (Lin et al., 2016). Lin et al. proposed an approach based on the integration of BIM with the trapezoid structural transfer layer to detect collisions, optimize the construction process, and enhance project quality control (Lin et al., 2017). Lin proposed the integration of BIM with web-based systems to facilitate the updating and transferring of data in the BIM environment. This method aids users in mitigating defects during the pre-construction phase (Lin, 2015). Hamledari et al. developed a technique to automatically transfer site data (based on site observation for inspected building elements) to the BIM model. This system identifies discrepancies between the as-built and as-designed object conditions; therefore, it can assist with defect management (Hamledari et al., 2018). Elbeltagi and Dawood employed the integration of BIM with GIS to develop a method that can evaluate and visualize construction performance against time. The proposed method reduces potential defects of repetitive actions during the decision-making process (Elbeltagi & Dawood, 2011). Park et al. proposed a method for automated registration of daily photos to 4D BIM, which helps identify BIM objects associated with corresponding photos to understand the contents and context (Park et al., 2018). Biagini et al. used laser scanning and BIM for construction management in historical building restoration projects by reconstructing a digital twin of the building (Biagini et al., 2016). The proposed method supports the preservation of historical buildings.

Facilitating seamless collaboration and communication among project participants enhances the project synergies while advancing the quality management process. Kubicki et al. integrated BIM with Smart White Board systems to create synchronous interactive devices to enhance coordination between project participants. It promotes better engagement of team members to make project decisions after considering multi-stakeholder concerns (Kubicki et al., 2019). Chen and Lu developed a BIM-based method for improving information quantity, quality, and accessibility (Chen & Lu, 2019). The results demonstrated improved data exchange between project participants. Lin and Yang developed a BIM-based collaboration management method to reduce the time required to complete the model checking process. The case study results indicate the potential held by the proposed approaches in collaborative BIM model creation for general contractors (Lin & Yang, 2018). Ma et al. used BIM with indoor positioning technology to resolve the omission of check items and initiate the process of digitizing inspection results. The outcomes of this study demonstrated the improvement of collaboration among the construction stakeholders (Ma et al., 2018). Oh et al. presented a BIM-based integrated system to aid collaborative design, which addresses the challenges associated with employing various BIM-based software when collaborating during the design phase and resolving pertinent issues, such as loss of data and difficulty in communication (Oh et al., 2015). Koseoglu et al. proposed a framework based on the integration of BIM with lean construction principles (Koseoglu et al., 2018). Larsen developed a BIM-based framework to address the challenges associated with traditional progress reporting. This framework consists of three steps for minimizing manual reporting and improving communication during the reporting process (Mejlænder-larsen, 2018). Park et al. integrated BIM with web-based systems to enable real-time information sharing of daily 4D BIM. This method improves the collaborations and communications among project participants regarding daily construction operations, aiding project managers in making appropriate decisions (Jaehyun Park et al., 2017). The quality Assurance and Quality Control (QA/QC) process that is typically practiced at construction site tend to comply with designers’ drawings and specifications. This process typically utilizes manual checklists. Several studies demonstrate the advantages of using mobile-based BIM to comply with lean construction practices and build quality-assured construction (Donato et al., 2018). Web and mobile-based checklists, defects, or errors in communications during QA/QC procedures illustrate success with real case projects according to the studies mentioned above.

The third approach to quality management studies is related to progress monitoring. The success of progress monitoring in a construction project depends on detailed and efficient tracking, analysis, and visualization of the actual status of buildings under construction (Golparvar-fard et al., 2015). To accomplish this, BIM has been integrated with techniques, such as GIS, UAV, RFID, AR, and laser scanning. The use of laser scanning has been a popular research topic in the recent past. Wang et al. used BIM and Laser Scanning Display (LSD) to automatically estimate the dimensions of precast concrete bridge deck panels and thus, improve the quality inspection of panels (Wang et al., 2018). Similarly, Bosché presented a method based on integrating BIM with laser scanning to automatically recognize 3D CAD model objects and calculate as-built dimensions. The results of this study showcased that this system enhances the level of quality inspection during the construction phase (Bosché, 2010). Bosche et al. used BIM with laser scanning to present automated object recognition, which indicated its potential in controlling as-built dimension calculation (Bosche et al., 2009). Bosché et al. integrated BIM with laser scanning for structural work monitoring. The proposed system provides accurate information from the construction site to improve the progress monitoring process (Bosché et al., 2014). Dimitrov and Golparvar proposed a vision-based material recognition method based on the integration of BIM with point cloud data. This method can generate a BIM model from unordered site image collections, which can significantly improve the automated monitoring of construction progress (Dimitrov & Golparvar-fard, 2014). Han et al. developed an appearance-based recognition method using BIM with 3D point cloud models to determine construction progress (Han & Golparvar-fard, 2015). Braun et al. proposed automated progress monitoring with photogrammetric and BIM (Braun et al., 2015). The real-time point cloud of a construction project is generated through a large number of images captured by a camera. The study proposes an iterative step for construction planning from real-time progress. However, the inability to capture clear images of the whole building, the requirement of large volumes of data to build a point cloud and implementation costs remain the disadvantages of this study. Costin et al. integrated BIM with RFID to enable real-time tracking and monitoring of construction workers. This integrated method, as demonstrated by the results, can maintain building protocol control (Costin et al., 2015). Choi et al. used BIM to perform path analysis to enhance monitoring workspaces (Choi et al., 2014b). The proposed framework enhanced the work-space planning process. Shahi et al. used BIM with imaging and Ultra-Wide Band to track the progress of construction activities (Shahi et al., 2015). Braun and Borrman developed a method based on the integration of BIM with inverse photogrammetry technique to automatically label construction images. This system enhances image-based object detection, which is the basis of construction progress monitoring (Braun & Borrmann, 2019). Asadi et al. proposed a BIM-based method to facilitate as-built and as-planned data comparison. This system automatically registers real-time images to a BIM model. Therefore, it aids managers with monitoring construction work indoors (Asadi et al., 2019).

Tezel et al. studied BIM and lean construction and revealed that significantly lesser research attention had been directed toward Small and Medium Enterprises (SMEs), whose contribution to the industry is substantially greater (Tezel et al., 2020). The paper invites future scholars to further explore BIM implementation in SMEs to deliver a positive impact in the context of lean construction. Table 5 in Appendix provides an overview of BIM-based articles related to quality management.

Roadmap for BIM-based predictive construction management

Knowledge generated from this review was leveraged to develop a strategy map to support predictive decision-making in construction projects. Figure 2 illustrates the proposed strategy map and how BIM can support predictive decision-making in key stages of the project life cycle, i.e., conceptual development, design, procurement, and construction.

Fig. 2
figure 2

Roadmap of predictive decision-making improvement. Note: API: Application Programming Interface, AR: Augmented Reality, CBA: Cost Benefit Analysis, CBIR: Content-Based Image Retrieval, CBR: Case-Based Reasoning, DES: Discrete Event Simulation, D4AR: Four-dimensional Augmented Reality, EVA: Earned Value Analysis, FST: Fuzzy Set Theory, GA: Genetic Algorithm, GIS: Geographic Information System, GPS: Global Positioning System, LCC: Life Cycle Costing, LPS: Local Positioning System, LSD: Laser Scanning Display, PFA: Pareto Front Analysis, RBR: Rule-Based Reasoning, RFID: Radio Frequency Identification Device, SW: Semantic Web, SWB: Smart Whiteboard, UAV: Unmanned Aerial Vehicle, UWB: Ultra-Wideband, VR: Virtual Reality, WMS: Web Map Service, WSN: Wireless Sensor Network, and 3PCM: 3D Point Cloud Models

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This roadmap indicates that a number of organizational processes are required for the proper implementation of BIM in the construction industry. These processes are (i) Investments in BIM-related software packages and training, (ii) Mandating BIM-supported planning and monitoring mechanisms for mega projects, (iii) Expanding the BIM training system to increase awareness and develop a skilled workforce, and (iv) Storage and maintenance of big databases related to construction projects, employing them to create automated decision support systems.

BIM can be integrated with multiple software and platforms. However, it is necessary to identify the exact requirement and choose the optimal software for such an integration for the project decision-making. The proposed roadmap illustrates BIM functionality in each stage of a construction project and in each section of construction management. During the conceptualization phase, BIM-based approaches, such as safety planning and scheduling, risk management, and lean construction, could help project investors, contractors, and managers to develop a more accurate visualization, class detection, finance and supply chain planning, and risk identification. Moreover, it is also possible to create multiple design alternatives with BIM computational designs, which can be compared with the base case to identify potential safety, quality, schedule, and cost issues. Subsequently, the project investors and clients can select the most desirable project based on their organizational strategies. Furthermore, this process can be augmented by integrating real-time communication with the help of the BIM model using Virtual Reality and Augmented Reality-based techniques. By creating visual models at their planned locations, this feature can help them identify any negative implications for the neighboring buildings and infrastructures. Moreover, this mechanism also comes in handy to witness the esthetic appearance of the building. Besides, as shown in the roadmap, some techniques such as GIS must be integrated with BIM to reach the aforementioned outcomes.

In the design phase, BIM aids with predictive decision-making in multiple ways. Feasibility checking and preparing contract documents are two of the issues that must be checked in the design phase. The collaboration feature of BIM helps with the accurate management of project design schedules as well as design optimization due to the rich data utilization. The proposed techniques in the roadmap assist managers with accurate cost estimation and time planning, which will not skew from the actual project cost and duration. Moreover, BIM can be integrated with expert systems to enable managers to detect hazards during the design phase and prevent accidents for both construction workers and building occupants. Better coordination of building elements, such as envelope, mechanical and electrical, etc., can be used to identify possible clashes and implement the necessary modifications to avoid potential project delays. 6D BIM also enables managers to perform energy analysis and improve sustainability performance.

In the procurement phase, BIM-based approaches such as automated schedule models enable managers to conduct accurate quantity take-offs and determine the required equipment. The required quantities of materials and equipment can be accurately calculated in the pre-construction phase of a construction project. Accordingly, contractors can purchase and order materials and equipment in a timely manner without any unnecessary delays while maintaining optimal inventory levels. Improving this mechanism will also help decision-makers to leverage lean methods such as Just in Time-based inventories for locally available materials (for long-term fix price products) and hedging mechanisms for materials with frequent price fluctuations.

The construction phase of a project benefits from BIM-based accurate progress monitoring with features of automation and web-based platforms. The quality control and assurance process can be digitalized with the aid of mobile extensions in BIM. Furthermore, a large number of stakeholders can communicate seamlessly via the same BIM environment during the construction phase. A novel approach integrates the EVA method within the BIM database. This would enhance the efficiency and effectiveness of decision-making and provide a visual platform to compare the planned development with the actual progress.

Conclusion

This study conducted a critical review to investigate how construction management researchers have adopted and employed BIM to support predictive decision-making. The research framework in this study incorporated the main construction management challenges (i.e., schedule management, cost management, safety management, and quality management). After analyzing several articles published addressing each of the construction management challenges, safety management has been identified as the most popular research focus. The increased emphasis on improving construction safety with state-of-the-art technology can be considered a positive development. The growth of BIM-related literature during the past five years suggests that BIM has garnered the interest of construction management researchers who have recognized its significance. The non-adoption of BIM in various projects was attributed to the lack of expertise of the project team and the external organizations. The focus of academia on BIM is a positive sign indicating that the aforementioned challenges may now be addressed.

Published literature demonstrated numerous trends in BIM-based construction management research. Developing automated approaches for cost, schedule, quality, and safety management has been one of the most popular research focuses. As an example, the greatest number of articles addressing BIM-based schedule management was based on automated scheduling. Similarly, articles on automated cost estimation, automated hazard detection models, and automated progress monitoring models were the greatest in number under BIM-based cost management, safety management, and quality management, respectively.

BIM and GIS integration has been a popular research area in the recent past, with multiple projects attempting to improve the interoperability between BIM and GIS. This would enable BIM users to use the building and geospatial data from web-based data providers without any hassles. Besides, facility management systems have been integrated with BIM and GIS to provide a better understanding of facility management. Recent studies are applying the integration of BIM and GIS in the geospatial section because of the rising research interest in smart cities, Internet-of-Things, and urbanization.

There are several cutting-edge technologies that could assist with BIM-based predictive decision-making, such as digital twin technology made up of AR and VR. It can create a bridge between the physical and the digital world. It can accelerate risk assessment and production time, enable businesses to schedule predictive maintenance accurately, and facilitate real-time remote monitoring. Although this research identified automated models as one of the BIM-based approaches, it is important to note that automated and intelligent systems are still lacking in the industry. The systems are flexible to adapt to the estimator’s rationale and consider a comprehensive set of parameters that affect the decision-making process. AI could be utilized by researchers to develop robust systems in the construction industry. Data storage and sharing of construction activity among project teams is vital to creating big data analysis methodologies with the aid of BIM.

Although this review has presented some useful BIM-based construction management approaches, a number of knowledge gaps have been identified. The gaps and suggestions for future research in each section are as follows:

Schedule management: At first, given the current gaps related to the reliability of short-term construction planning, further research is required to gain hands-on experience in mobile applications and device specifications needed for the process. Second, recognizing that the vast range of forms and geometrics of plan layouts, multiple beam sections, and various floor levels tend to be severely complex and error-prone. Therefore, an on-site sensor-based twinning approach can enhance project efficiency. Third, to reduce the technical limitations and potential errors arising from manual work and to enhance automation, BIM can be integrated with AI. To improve the level of data consistency among software systems, advanced open file formats should be developed.

Cost Management: Construction costs are associated with numerous uncertainties. Using a comprehensive machine learning model that considers all possible factors, it is possible to improve the accuracy of cost prediction. The database of this model must be developed for all types of buildings, such as residential, commercial, and factories. BIM-based clash detection plays an indispensable role in minimizing construction costs. Future researchers are encouraged to extend the previous research schema by adding new factors, such as varying project sizes and design complexities, to demonstrate BIM capabilities in clash detection. Fast analysis capabilities of different project alternatives are another future avenue studies should focus on to maximize BIM capabilities.

Safety Management: As evidenced by this study, numerous research has dealt with construction workers’ health and safety. However, safety management lacks a BIM integrated system to assess the risk of any project. Therefore, a method must be developed to derive construction safety data from a BIM model and assess the risk of construction hazards as well as the total risk of a construction project. The safety of buildings can be considered at the design stage in terms of fire safety and public safety, where regulations are imposed by authorities. BIM-based frameworks seem like an ideal solution for such code-complying activities where the designer is benefited through the prevention of later changes while authorities benefit from efficient communication with designers. This study sheds light upon the above avenues for future scholars seeking to study its practical aspects specifically.

Quality Management: Developing automated systems for monitoring construction processes could be an appropriate subject for future research. This BIM-based system could be integrated with some image recognition and processing methods that are specifically designed to process pixel data. This process would aid with the integration of the industrial Internet-of-Things into the construction industry. The introduction of mobile-based solutions for the QA/QC process is a potential avenue for research in future.

A significant portion of the literature reviewed by the authors demonstrates that both testing and commercial software tools are available for the approaches discussed in this study. However, there seems to be a lag in their utilization and practical application in construction when compared to industries like manufacturing. Apart from selected management sections, BIM implementation must be researched more thoroughly to understand the cost of BIM implementation, Resistance from SMEs, amendments needed in government regulations, and the reduction of complexity of BIM.

In terms of the construction management challenges reviewed in this study, it can be concluded that state-of-the-art technologies and techniques are indispensable to addressing any potential challenges before initiating construction projects. Predictive decision-making helps managers prevent cost and time overruns, identify and assess safety risks, and resolve any existing inaccuracies during the pre-construction stage. Therefore, it is vital for managers to adopt and implement cutting-edge BIM-based methods to optimize construction management.