Towards 4D BIM: A Systematic Literature Review on Challenges, Strategies and Tools in Leveraging AI with BIM
Abstract
1. Introduction
1.1. Study Contributions
1.2. Study Road Map
2. Review Methodology
2.1. Research Questions
- RQ 1: What are the existing limitations in BIM?
- RQ 2: What challenges in AEC/digital construction have been addressed in the literature?
- RQ 3: What are the strategies that can be deployed towards 4D BIM?
- RQ 4: What tools, techniques, and software exist in the research for 4D BIM integration and 4D BIM related research?
- RQ 5: What are the predictive strategies applicable and how are they evaluated?
2.2. Search Process
2.3. Quality Assessment
2.4. Data Collection
2.5. Data Analysis
3. Background
- As a knowledge and information resource;
- As a collaborative tool;
- In terms of content;
- In terms of location;
- As a 3D digital representation, as well as other contexts.
4. Related Research
4.1. Digital Construction: BIM
Citations | Review Topic | BIM Type | Application Area | Research Method | Gaps and Limitations |
---|---|---|---|---|---|
Mesároš et al. [38] | The fifth dimension of BIM—implementation survey | 5D BIM |
| survey |
|
Abbasnejad et al. [34] |
| BIM |
| SLR |
|
Zhao and Taib [35] |
| Cloud-BIM |
| SLR |
|
Bastem and Cekmis [39] | Development of historic building information modeling: a systematic literature review | HBIM |
| SLR |
|
Alankarage et al. [40] | Organizational BIM maturity models and their applications: a systematic literature review. | BIMMM (BIM maturity model) |
| SLR |
|
Abanda et al. [41] | A literature review on BIM for cities distributed renewable and interactive energy systems. | BIM-Energy |
| SLR |
|
Wang and Meng [37] | Transformation from IT-based knowledge management into BIM-supported knowledge management: A literature review | BIM-KM (knowledge management) |
| SLR |
|
Zabin et al. [6] | Applications of machine learning to BIM: A systematic literature review | BIM-ML (machine learning) |
| SLR |
|
4.2. Lean Systems in BIM
- Digital lean systems;
- Traditional lean ideologies.
4.3. 4D BIM
4.4. BIM Ontology
BIM Ontology-Related Research
5. Challenges in Construction
6. Tools, Techniques and Software in 4D BIM
6.1. Classification of Instruments in 4D BIM
6.1.1. Tools
6.1.2. Techniques
6.1.3. Software
6.1.4. Integration
6.1.5. Methods
6.1.6. File Types
7. Predictive Modeling Strategies
7.1. Computer Vision
7.2. Natural Language Processing
7.3. General Machine Learning
7.4. Summary of Predictive Modeling Strategies
Technique Domain | Technique | AEC Domain/Processes | Citations |
---|---|---|---|
Computer vision | Stochastic gradient descent (SGD), YOLOv8m | Safety (workforce safety monitoring) | Kulinan et al. [130] |
SOLOv2, PointRend, Yolact, Cascade Mask R-CNN, Mask R-CNN | Status (Progress Monitoring) | Wei et al. [132] | |
Mask R-CNN, Cascade Mask R-CNN, CNN | Logistics, Safety, material | Yan et al. [131] | |
YOLOv5 + DeepSORT | Quality (crack defects detection for inspection) | Tan et al. [136] | |
DeepLabv3+ | Waste (construction waste) | Lu et al. [137] | |
Review study; ANN, SVM, CNN, etc. | Performance monitoring: equipment and worker activity recognition | Behnam et al. [135] | |
Support Vector Machines—SVMs; Histograms of Oriented Gradients— HOGs; Histograms of Optical Flow—HOFs; Motion Boundary Histogram—MBH | Action recognition | Yang et al. [133] | |
SVM, Naı¨ve Bayes, decision trees, k-nearest neighbors, regression, neural networks, boosting algorithms | Document classification | H et al. [138] | |
Natural Language Processing | Systematic scientometric analysis; BERT, GPT, Neural networks | Communication: Text reasoning | Y. Ding et al. [139] |
Overview paper; Long Short-Term Memory Recurrent Neural Network LSTM-RNN, Graph Convolutional Neural Network (GCN), Transformer machine learning model | Schedule management | Singh et al. [141] | |
Support vector machine (SVM), latent semantic analysis (LSA) and latent Dirichlet allocation (LDA) | Text classification | Jung & Lee 2019 [140] | |
SVM, SVO tagging, PRINCO (Principal Component Analysis) and Random Forest | Design (Bid and tendering, error analysis, changes analysis), Supply chain, Maintenance | Kim et al. [142] | |
Ontology, Text summarization | Safety | Kim et al. [142] | |
Review paper, BERT, GPT, LSA, LDA, Word2Vec and its variants (e.g., Sentence2Vec, Doc2Vec), GloVe, and FastText | Construction | Chung et al. [150] | |
KeyBERT algorithm, BERT-Base, RoBERTa-Base, and fastText | Quality (building defect analysis) | Shooshtarian et al. [143] | |
GermaNet, SpaCy, or BERT. | Design, Sustainability (GHG) | Forth et al. [144] | |
Machine Learning | Review paper, ML, RDF, ANN, SVM, CONVNETS, GA, DT | Schedule creation, performance monitoring, cost estimation, risk identification, knowledge-based bim system, energy management system, localization, maintenance prediction | Zabin et al. [6] |
Random forest classifier, decision trees, frequent item set analysis ‘FIA’ | Facility management | McArthur et al. [145] | |
Random Forest (RF) and Stochastic Gradient Tree Boosting (SGTB), | Safety (injury prediction) | Tixier et al. [146] | |
CNN, SVM, RF | Risk monitoring | PourRahimian et al. [148] | |
SVM, KNN, RF. Ensemble techniques, CNN, FCN, WNN, DGCNN, RNN, MVCNN, RANSAC, PCA | Lifecycle safety analysis, quality, performance monitoring | Mirzaei et al. [149] | |
Multiple linear regression, multi-layer perceptron (MLR) | Design (implication of design changes) | Abdulfattah et al. [151] |
7.5. Evaluation Methods
Ref | Domain | Prediction Strategy | Cm | mAP | PR Curve | Acc % | Precision % | Recall % | F1 Score % |
---|---|---|---|---|---|---|---|---|---|
[152] | CV–CAD object classification | convolutional deep belief network (CDBN) | no | - | no | - | 42–62 | - | - |
[130] | CV–worker/ safety object classification | YOLOv8m | no | 92.3 | yes | - | 88.3 | 89.3 | - |
[70] | ML-work productivity prediction | MLP (adam), Histogram based gradient boosting, stacking MLP SGD, | yes | - | no | 86.2, 93.8, 90.8 | 86.2, 93.9, 91.1 | 86.2, 93.8, 90.8 | 86.2, 93.85, 90.95 |
[159] | CV-progress monitoring | Mask R-CNN | no | 92.6 | yes | - | - | - | - |
[140] | NLP case study classification | LSA, LDA, SVM | 100 | 27 | 41.82 | ||||
[157] | CV-Princeton modelnet leaderboard-volumetric/ geometric classification | Convolutional Deep Belief Network | yes | 68.26 | yes | 83.54 | - | - | - |
[155] | CV-volumetric/geometric classification | LP-3DCNN | no | - | no | 94.4 | - | - | - |
[154] | CV-Multiview recognition | pairwise | no | - | no | 0.907 | - | - | - |
[156] | CV-Varied orientation recognition | Orions | no | - | yes | 89.7 | - | - | - |
[153] | CV-Voxel-based representation for object recognition | VRN ensemble | no | - | no | 97.14 | - | - | - |
8. Discussion
8.1. RQ 1: What Are the Existing Limitations in BIM?
8.2. RQ 2: What Challenges in AEC/Digital Construction Have Been Addressed in the Literature?
8.3. RQ 3: What Are the Strategies That Can Be Deployed Towards 4D BIM
8.4. RQ 4: What Tools, Techniques, and Software Exist in the Research for the Integration of 4D BIM?
9. Open Issues
- Adoption and implementation of BIM is generally determined to be a limitation in BIM research. The barriers associated with the adoption of 4D BIM software should be further analyzed to achieve a consensus on how advancements in AI can facilitate automation, enhance training, and improve usability [160,161].
- Existing use cases of 4D BIM are visualization, performance monitoring, safety, progress monitoring, scheduling and planning, decision making, and integration [5,50,51]. However, in construction, there are other unexplored aspects such as instant risk insights, project continuation/closure, and evaluation, project benefit evaluation-earned value analysis, project securing (i.e., automated bid and tendering), etc. These areas should be further explored with real project pilot case studies for validation.
- Discussions regarding digitized support and combination of technologies such as AI, IoT, Cloud storage, AR, and VR are common [162,163]. However, there is not enough analytical research detailing the outcomes of each of the combinations and their importance for the sustainability and progress of digital construction.
- Other gaps in 4D BIM-related research:
- (a)
- Inability to generalize 4D BIM solutions: This is a recurrent limitation in most 4D BIM studies across different projects, contexts and organizations. Factors leading to such limitations include complexities and uniqueness of projects embarked on in construction, tailored schedules that do not match 3D BIM information, software compatibility, and inconsistent data standards, leading to data losses and nonquality data [51,58,59,60,61].
- (b)
- Lack of Integrated Decision-Making Support in 4D BIM Systems: The majority of businesses in AEC, projects, processes, and domains operate with siloed data that comprise different formats and types; for example, schedules and 3D drawings are created independently without collaborative efforts between designers, engineers, and planners. These disparate data sources only lead to fragmented information, making it difficult to draw instant project or business insights. Thus, analytical capabilities are limited and can be prone to undue errors and inconsistencies [51,62].
- (c)
- Limited research documentation and regulation in 4D BIM: The small amount of research documentation of 4D BIM, coupled with unavailable standardized protocols that are non-existent in many regions of the world, are determined as additional limitations in this regard. Going forward, it is important to promote academic research in this area and enhance the promotion of using standardized data formats alongside APIs [59,60].
- (d)
- Lack of automation (AI) in 4D BIM: The manual method of data integration and lack of automation in 4D BIM is a major limitation that impedes accuracy, consistency, and effectiveness in project management. Without AI, it is impossible to take a proactive approach and generate actionable insights instantly. It is, however, important to take an AI-driven approach to building 4D BIM solutions [5,60].
- BIM ontology reviews indicate the need for further research and development. Although several BIM modules have standardized and validated ontological, structures such as time, dimension, GIS, safety, quality, planning, and other modules that are developed and validated by standard regulatory bodies, there are other lower-level modules or domains such as predictive maintenance systems for energy systems, predictive failure modules, automated machinery systems and other relatively new models which require integration with BIM to function. Also, other linkages, such as safety-schedule-BIM ontology, schedule-BIM ontology, etc., are still unexplored. This shows that there is a need for further research to overcome the continuous integration challenge experienced across the board in digital construction.
- The level of detail (LOD) in 4D BIM is unexplored [164,165,166]. There is a need for the analysis of LOD in 4D BIM solutions for entity integration, data extraction, design and implementation workflow analysis, automation, production control, visual management, etc. It is important to systematically and analytically determine the level of detail for all process steps regarding 4D BIM and all integrated digital construction systems.
10. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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SubTopic | Search String | Search Field |
---|---|---|
BIM | “3d bim” OR “bim” OR “building information modeling” OR “building information management” AND “survey” OR “literature review”. | Title |
Lean systems in BIM | ”lean” OR “lean construction” OR “lean methodology” AND “bim” or “building information modeling” or “digital construction” or “AEC *” | Title |
Challenges in construction | ”fragmentation” OR “Integration” OR “Adoption” OR “Implementation” OR “Cost” OR “Communication” OR “Collaboration” OR “Interoperability” OR “Sustainability” OR “Security” OR “Complexity” OR “Design” OR “Data” OR “Legal” OR “regulatory” OR “Standard” OR “Compliance” OR “Stakeholder” OR “Automation” OR “Skill” OR “Training” AND “Challenge” OR “Barrier” OR “Obstacle” OR “Issue” OR “Hurdle” OR “Limitation” OR “Problem” OR “Difficulty” | No field |
“construction *” OR “construction industry” OR “construction sector” | Subject | |
“bim” OR “building information modeling” OR “building information *” OR “Information technology” OR “ICT” AND “Bim” OR “building information modeling” OR “building information *” OR “information technology” OR “ICT” | All text | |
4D BIM | 4D BIM | Title |
BIM Ontology | ontolog * AND “bim” or “building information modeling” or “digital construction” or “AEC *” | Title |
Predictive strategies in 4D BIM | “Computer vision” OR “natural language processing” OR “NLP” OR “machine learning” OR “ML” AND OR “building information modeling” OR “AEC” OR “BIM” | Title |
Subtopics | Exclusion | Inclusion |
---|---|---|
BIM | (1) Non-survey/review papers or case study application studies (2) Papers outside language scope (English) | (1) Survey or review papers regarding BIMs, its adoption, challenges, history, digital progression, etc. |
Lean systems in BIM | (1) Articles not contributing to bridging the divide between lean construction and BIM (2) Studies including other technologies other than lean and BIM. (3) Paper outside language scope (English) | (1) Studies discussing on lean systems and application in AEC or BIM (2) Studies discussing existing products bridging the gap between lean BIM. |
Challenges in construction | (1) Short paper columns that was published as article was excluded. (2) Papers from different fields/subject terms such as education, health user acceptance, microbial fuel cells, psychology, ecology, ethics were excluded (3) Surveys or review articles that did not have a specific challenge motivating research to achieve solutions and overcome such challenges were excluded (4) Articles with challenges not pertaining to construction or AEC were excluded (5) Articles written regarding other types of construction asides construction for built assets and physical building are not considered. Material construction, test construction, etc., are disregarded. | (1) Research work with a specific problem that spurs and motivates research (2) Research areas within subject area of construction and construction technology |
4D BIM | (1) Articles without 4D BIM as main subject (2) Paper outside language scope (English) | (1) Studies discussing integration methods and frameworks (2) Studies detailing tools, software and techniques |
Predictive strategies in 4D BIM | (1) Incoherent or poorly organized study documentation | (1) All studies showcasing AI-based technique in AEC phases, design, digital construction, BIM, etc. (2) All studies detailing evaluation basis |
Subtopic | Full Text | Peer-Reviewed | Language | Academic Journal | Ref. Available | Year | Total |
---|---|---|---|---|---|---|---|
BIM | x | x | x | x | 10 | ||
Lean systems in BIM | x | x | x | x | 19 | ||
Challenges in construction | x | x | x | x | x | 63 | |
Four-dimensional BIM | x | x | x | x | 2015 | 16 | |
BIM Ontology | x | x | x | 20 | |||
Predictive strategies in 4D BIM | x | x | x | 2020 | 21 |
Context | Building Information Modeling Description | Citations |
---|---|---|
Knowledge resource | NBIMS-US defines BIM as a digital representation of physical appearance and functional capability of a built asset | [2] |
Collaborative tool | BIM is a virtual system that encompasses all aspects, disciplines, and elements of a facility within a single virtual workspace allowing team members to collaborate more accurately and efficiently than using traditional processes | [1] |
Content | A BIM comprises the location, geometric details, spatial relationships, quantities and properties of building components with schedules, resource availability, and cost estimates | [1] |
Geographical location | In the UK, BIM is regarded more as a process than a technology or software. BIM is described as the process of designing, constructing, and operating a building or infrastructure using an object-oriented information system (The British Standards Institution, 2015) | [1,11] |
Other contexts | Three-dimensional digital representation (model), coordination of design activities, supply chain management system, management method for planning, and life cycle monitoring of products | [11] |
Citations | Field-Topic | Vis | Perf | Saf | Prom | Sch | Decm | Itgn |
---|---|---|---|---|---|---|---|---|
Charlesraj & Dinesh [54] | Status of 4D BIM Implementation in Indian Construction | x | x | x | ||||
Umar et al. [55] | 4D BIM Application in AEC Industry: Impact on Integrated Project Delivery | x | x | x | ||||
Park & Cai [56,57] | (1) Framework of Dynamic Daily 4D BIM for Tracking (2) Database-Supported and Web-Based Visualization for Daily 4D BIM | x | x | x | ||||
Vtt [58] | 4D-BIM for Construction Safety Planning | x | x | |||||
Montaser & Moselhi [59] | Methodology for Automated Generation of 4D-BIM | x | ||||||
Raut [61] | Improve the Productivity of Building Project using BIM Based 4D Simulation Model | x | ||||||
Doukari et al. [60] | The Creation of Construction Schedules in 4D BIM: A Comparison of Conventional and Automated Approaches | x | ||||||
Bolshakova et al. [62] | Identification of relevant project documents to 4D BIM uses for a synchronous collaborative decision support | x | x | x | x | |||
Crowther & Ajayi [5] | Impacts of 4D BIM on construction project performance | x | x | x | ||||
Salman & Hamade [63] | The Integration of Virtual Design and Construction (VDC) With the Fourth Dimension of Building Information Modeling | x | ||||||
Harris et al. [64] | 4D Building Information Modeling and Field Operations: An Exploratory Study | x | x | |||||
Martins et al. [65] | Evaluation of 4D BIM tools applicability in construction planning efficiency | x | x |
Processes | Citations | Krs | Std | Sch | Infr |
---|---|---|---|---|---|
Communication | Kwofie et al. [108] | x | |||
Cost management | Lee et al. [97]; Ren et al. [98] | x | x | x | |
Design | Ma and Liu [106] | x | x | x | x |
Integration | Tchouanguem et al. [82]; Shi et al. [103] | x | x | x | x |
Knowledge management | Lee and Jeong [107]; Niknam and Karshenas [100]; Zhou et al. [92] | x | x | x | |
Maintenance | Mignard and Nicolle [101]; Hosseini Gourabpasi and Nik-Bakht [8]; Pauen et al. [91] | x | x | x | |
Monitoring and compliance | Zhong et al. [102]; Jiang et al. [105] | x | x | x | x |
Planning | Tavakolan et al. [96]; Zheng et al. [85] | x | x | ||
Quality management | Park et al. [95]; Lee et al. [94] | x | x | ||
Quantity (multiplicity) | Liu et al. [99] | x | x | ||
Risk management | Ding et al. [93] | x |
Phase/Stage | Related Digital Challenges | Citations |
---|---|---|
General AEC |
| [1,33,113,114,116] |
Digital construction |
| [117] |
Planning and control | Fast-tracked project issues include
| [1] |
Visualization |
| [9] |
Supply chain |
| [118] |
Pre-construction |
| [119,120] |
Construction phase |
| [121] |
Post-construction |
| [115] |
Classification | Software/Tools | Citations |
---|---|---|
Model authoring software | Revit, Bentley MicroStation, Tekla, Solibri Model Checker, ArchiCAD | [65,66,67,69,73,75,122,123,124] |
4D BIM software | Synchro Pro, Naviswork, Fuzor | [65,66,67,75,77,123,124,125,126] |
Modeling and Design | Autodesk Maya, Autodesk 3ds, Blender SketchUp, McNeel Rhinoceros (Rhino) | [65,66,73,75,125] |
Scanners (data capturing) | Faro Focus, Recap | [66,122] |
Schedulers | Microsoft project, primavera p6, Excel | [65,67,68,71,75,77,123,124,126] |
API | Autodesk Forge, Trimble Connect, Dynamo, Revit API, BIM 360 API, COMSOL API | [65,66,68,69,73,124] |
Databases | ODBC, structured query languages SQL, schemas | [66,122] |
Classifiers and Algorithms | RANSAC, NSGA-II algorithm, multiobjective optimization using GA | [68,71,76,122,123] |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Awe, M.; Malhi, A.; Budka, M.; Mavengere, N.; Dave, B. Towards 4D BIM: A Systematic Literature Review on Challenges, Strategies and Tools in Leveraging AI with BIM. Buildings 2025, 15, 1072. https://doi.org/10.3390/buildings15071072
Awe M, Malhi A, Budka M, Mavengere N, Dave B. Towards 4D BIM: A Systematic Literature Review on Challenges, Strategies and Tools in Leveraging AI with BIM. Buildings. 2025; 15(7):1072. https://doi.org/10.3390/buildings15071072
Chicago/Turabian StyleAwe, Michael, Avleen Malhi, Marcin Budka, Nicholas Mavengere, and Bhargav Dave. 2025. "Towards 4D BIM: A Systematic Literature Review on Challenges, Strategies and Tools in Leveraging AI with BIM" Buildings 15, no. 7: 1072. https://doi.org/10.3390/buildings15071072
APA StyleAwe, M., Malhi, A., Budka, M., Mavengere, N., & Dave, B. (2025). Towards 4D BIM: A Systematic Literature Review on Challenges, Strategies and Tools in Leveraging AI with BIM. Buildings, 15(7), 1072. https://doi.org/10.3390/buildings15071072