Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review
Abstract
:1. Introduction
- Planning problems: lack of detailed resource assessment, incorrect estimates and focus problems in defining activities.
- Communication failures: communication problems between contractors, stakeholders, suppliers, customers, and project teams.
- Inaccurate scope management: incorporation of unauthorized changes and lack of clarity in including crucial aspects of the project.
- Cost control problems: over-budget projects, poor initial estimates, and price inflation, among others.
- Risk-management difficulties: delivery delays, quality issues, safety issues, and lack of money, among others.
- Quality problems: Lack of clear standards, defects, complaints and low satisfaction. Non-compliance with established metrics.
2. Materials and Methods
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- General category: clarity of the objective (1) and selection of the appropriate method for the research question (2).
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- Sample selection category: Sufficient information to generate conclusions (3) and clarity in the context of the research (4).
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- Method category: The selected method is indicated (5), the method is argued (6) and variables affecting the process are considered (7).
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- Data analysis category: The data are adequately analyzed (8), the results are clearly presented (9), and the reliability and validity of the research are reported (10).
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- Conclusions category: The research question is answered based on empirical evidence (11).
3. Bibliometric Analysis
4. Categorical Analysis
4.1. Question 1—What Are the Technologies Most Used in the Articles Found in the Review?
Technology Type | Number of Articles | Articles |
---|---|---|
Artificial Intelligence | 12 | 1, 15, 16, 19, 23, 28, 30, 31, 35, 36, 37, 42. |
Big Data/Data Science | 9 | 2, 10, 17, 45, 46, 49, 50, 54, 57. |
Artificial Intelligence/Data Science | 9 | 4, 11, 18, 22, 25, 38, 39, 43, 47. |
Artificial Intelligence/Big Data/Data Science | 8 | 5, 20, 21, 24, 27, 33, 44, 56. |
Data Science | 7 | 7, 8, 12, 14, 29, 40, 41. |
Big Data | 7 | 13, 26, 32, 51, 52, 53, 55. |
Artificial Intelligence/Big Data | 5 | 3, 6, 9, 34, 48. |
4.2. Question 2—What Are the Most-Used Methodological Routes, and with What Distribution?
Research Method | Number of Articles | Articles |
---|---|---|
Quantitative | 22 | 2, 3, 4, 5, 6, 7, 13, 14, 15, 17, 18,19, 25, 26, 30, 31, 35, 37, 39, 43, 56. |
Qualitative | 15 | 10, 11, 16, 20, 23, 33, 34, 36, 38, 42, 44, 47, 52, 54, 57 |
Mixed | 12 | 8, 12, 22, 24, 27, 32, 40, 46, 48, 49, 50, 51. |
Literature review | 8 | 1, 9, 21, 28, 29,41, 44, 55. |
4.3. Question 3—What Are the Most-Used Specific Fields of Construction Project Management (from the Areas of Knowledge of the PMI)?
Project Management Knowledge Area | Number of Articles | Articles |
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Cost | 22 | 1, 6, 7, 10, 11, 13, 14, 15, 18, 20, 21, 22, 25, 30, 31, 32, 34, 36, 39, 41, 42, 55 |
Quality | 19 | 3, 4, 5, 6, 9, 11, 16, 17, 18, 20, 22, 29, 30, 33, 34, 41, 47, 53, 54. |
Time | 17 | 4, 13, 14, 15, 17, 19, 20, 22, 23, 26, 27, 28, 32, 38, 39, 43, 57. |
Scope | 15 | 2, 5, 12, 14, 28, 29, 32, 35, 37, 38, 40, 43, 44, 53, 55 |
Risk | 13 | 1, 2, 6, 10, 16, 20, 25, 27, 33, 37, 42, 51, 57. |
Integration | 10 | 8, 35, 43, 45, 46, 48, 49, 50, 52, 54. |
Procurement | 6 | 7, 9, 17, 21, 31, 40. |
Stakeholder | 4 | 2, 18, 24, 55. |
Communications | 2 | 10, 24 |
Human resource | 1 | 1 |
4.4. Question 4—What Are the Main Recommendations Regarding Implementing Emerging Technologies in the Construction Sector (AEC)?
- Collection, processing, storage, and analysis of large volumes of data generated in the industry, providing project managers with fundamental elements to make informed decisions. The records highlight the use of historical records, real-time data, sensor information, and stakeholder comments.
- Improved risk management, based on a wide range of data, allows risks to be identified, managed, and mitigated. This tool makes it possible to facilitate decision making regarding risks and the reduction in their negative impacts.
- Big data allow you to generate predictive analysis, pattern detection, trend identification, and correlation analysis to predict future events and optimize performance.
- Promotion of quality and customer satisfaction based on analyzing feedback from those interested in the project. The research highlights the analysis of surveys, contributions on social networks, and other channels. This contributes to customer satisfaction and improves the reputation of the project.
- Big data provide tools and platforms to share information, track progress, and facilitate communication and collaboration between project team members.
- It facilitates decision making by allowing the collection, analysis, and visualization of large amounts of data related to the project.
- Optimizing resource planning and allocation: through analyzing historical data and using optimization algorithms, data science can help project managers optimize the planning and allocation of resources.
- Improved productivity and efficiency: By using data science techniques, project managers can identify areas for improvement and optimization in the workflow. By analyzing performance data, such as task duration, resource usage, and bottlenecks, inefficiencies can be identified and addressed, leading to greater productivity and efficiency in project execution.
- Scope management and change control: Data science can help in project scope management and changing control. By analyzing data related to changes in scope, requests for changes, and these changes’ impact on the project, managers can assess the risks and benefits of the proposed changes and make informed decisions about their approval or rejection.
- Optimization in project planning and scheduling, using artificial intelligence algorithms from different sources.
- Identifying the most critical tasks and possibly allocating resources using artificial intelligence algorithms.
- Early detection of problems that can be converted into risks through data analysis and incorporating predictive algorithms.
- Improved decision making, allowing the identification of project patterns and trends. It is an excellent support for project managers to select routes based on detailed information.
- Automation in developing repetitive tasks to improve efficiency and reduce human errors. This is associated with the execution of more appropriate strategic planning, and it saves time and effort.
- Communication between team members, providing tools and platforms for sharing information, managing tasks, and encouraging effective communication. This also appears to be a trend towards developing project groups in different locations.
5. Discussion and Conclusions
6. Specific Contributions to the Literature
7. Limitations and Future Research Directions
8. Practical Implications of the Review
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Oxford Economics. Future of Construction; Oxford Economics: London, UK, 2021; p. 62. [Google Scholar]
- Cooke, B.; Williams, P. Construction Planning, Programming and Control; John Wiley & Sons: Hoboken, NJ, USA, 2013; ISBN 1-118-65867-1. [Google Scholar]
- Saltz, J.S. The Need for New Processes, Methodologies and Tools to Support Big Data Teams and Improve Big Data Project Effectiveness. In Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 29 October–1 November 2015; pp. 2066–2071. [Google Scholar]
- Akbari, S.; Khanzadi, M.; Gholamian, M.R. Building a Rough Sets-Based Prediction Model for Classifying Large-Scale Construction Projects Based on Sustainable Success Index. ECAM 2018, 25, 534–558. [Google Scholar] [CrossRef]
- Larson, E.; Gray, C. Project Management: The Managerial Process 6e; McGraw Hill: New York NY, USA, 2014; ISBN 0-07-717006-7. [Google Scholar]
- Lester, A. Project Management, Planning and Control: Managing Engineering, Construction and Manufacturing Projects to PMI, APM and BSI Standards; Elsevier Science: Amsterdam, The Netherlands, 2013; p. 24. [Google Scholar]
- Netscher, P. Successful Construction Project Management: The Practical Guide; Panet Publications: New York, NY, USA, 2014; ISBN 1-4973-4441-7. [Google Scholar]
- Arashpour, M.; Bai, Y.; Aranda-mena, G.; Bab-Hadiashar, A.; Hosseini, R.; Kalutara, P. Optimizing Decisions in Advanced Manufacturing of Prefabricated Products: Theorizing Supply Chain Configurations in off-Site Construction. Autom. Constr. 2017, 84, 146–153. [Google Scholar] [CrossRef]
- Chen, K.; Lu, W.; Peng, Y.; Rowlinson, S.; Huang, G.Q. Bridging BIM and Building: From a Literature Review to an Integrated Conceptual Framework. Int. J. Proj. Manag. 2015, 33, 1405–1416. [Google Scholar] [CrossRef]
- Pospieszny, P.; Czarnacka-Chrobot, B.; Kobylinski, A. An Effective Approach for Software Project Effort and Duration Estimation with Machine Learning Algorithms. J. Syst. Softw. 2018, 137, 184–196. [Google Scholar] [CrossRef]
- Sacks, R.; Brilakis, I.; Pikas, E.; Xie, H.S.; Girolami, M. Construction with Digital Twin Information Systems. Data-Centric Eng. 2020, 1, e14. [Google Scholar] [CrossRef]
- Zhang, J.; El-Gohary, N.M. Integrating Semantic NLP and Logic Reasoning into a Unified System for Fully-Automated Code Checking. Autom. Constr. 2017, 73, 45–57. [Google Scholar] [CrossRef]
- Gupta, D.; Rani, R. A Study of Big Data Evolution and Research Challenges. J. Inf. Sci. 2019, 45, 322–340. [Google Scholar] [CrossRef]
- Chang, W.; Grady, N. NIST Big Data Interoperability Framework: Volume 1, Definitions; National Institute of Standards and Technology: Gaithersburg, MD, USA, 2019. [Google Scholar]
- International Data Corporation. IDC’s Worldwide Software Taxonomy; International Data Corporation: San Mateo, CA, USA, 2020; pp. 1–95. [Google Scholar]
- Mayer-Schönberger, V.; Cukier, K. Big Data: A Revolution That Will Transform How We Live, Work, and Think; Houghton Mifflin Harcourt: Boston, MA, USA, 2013; ISBN 0-544-00269-5. [Google Scholar]
- Motoa-Grajales, C.; Gomez-Peña, M.; Zabala-Vargas, S. Emerging Technologies (Big-Data, Data Science and Artificial Intelligence) in Project Management. An Initial Overview; Universidad Francisco de Paula Santander: San Francisco, CA, USA, 2023. [Google Scholar]
- García, S.; Ramírez-Gallego, S.; Luengo, J.; Benítez, J.M.; Herrera, F. Big Data Preprocessing: Methods and Prospects. Big Data Anal. 2016, 1, 9. [Google Scholar] [CrossRef]
- Sun, A.Y.; Scanlon, B.R. How Can Big Data and Machine Learning Benefit Environment and Water Management: A Survey of Methods, Applications, and Future Directions. Environ. Res. Lett. 2019, 14, 073001. [Google Scholar] [CrossRef]
- Davila Delgado, J.M.; Oyedele, L.; Bilal, M.; Ajayi, A.; Akanbi, L.; Akinade, O. Big Data Analytics System for Costing Power Transmission Projects. J. Constr. Eng. Manag. 2020, 146, 05019017. [Google Scholar] [CrossRef]
- Omran, B.A.; Chen, Q. Trend on the Implementation of Analytical Techniques for Big Data in Construction Research (2000–2014). In Construction Research Congress; ASCE: Reston, Virginia, 2016; pp. 990–999. [Google Scholar]
- Wang, K.; Guo, F.; Zhang, C.; Hao, J.; Schaefer, D. Digital Technology in Architecture, Engineering, and Construction (AEC) Industry: Research Trends and Practical Status toward Construction 4.0. In Construction Research Congress; ASCE: Reston, Virginia, 2022; pp. 983–992. [Google Scholar]
- AlChaer, E.; Issa, C. Costs and Benefits of Efficiency Measurement for the AEC Industry. In Computing in Civil Engineering; ASCE: Reston, Virginia, 2021; pp. 851–858. [Google Scholar]
- Hu, Z.; Wang, F.; Tang, Y. Scenario-oriented Repetitive Project Scheduling Optimization. Comput. -Aided Civ. Infrastruct. Eng. 2023, 38, 1239–1273. [Google Scholar] [CrossRef]
- Tao, S.; Wu, C.; Hu, S.; Xu, F. Construction Project Scheduling under Workspace Interference. Comput. -Aided Civ. Infrastruct. Eng. 2020, 35, 923–946. [Google Scholar] [CrossRef]
- Haider, M. Getting Started with Data Science: Making Sense of Data with Analytics; IBM Press: New York, NY, USA, 2015; ISBN 0-13-399123-7. [Google Scholar]
- Hariri, R.H.; Fredericks, E.M.; Bowers, K.M. Uncertainty in Big Data Analytics: Survey, Opportunities, and Challenges. J. Big Data 2019, 6, 44. [Google Scholar] [CrossRef]
- Mišić, V.V.; Perakis, G. Data Analytics in Operations Management: A Review. Manuf. Serv. Oper. Manag. 2020, 22, 158–169. [Google Scholar] [CrossRef]
- Kelleher, J.D.; Tierney, B. Data Science; MIT Press: Cambridge, MA, USA, 2018; ISBN 0-262-34703-2. [Google Scholar]
- Saura, J.R. Using Data Sciences in Digital Marketing: Framework, Methods, and Performance Metrics. J. Innov. Knowl. 2021, 6, 92–102. [Google Scholar] [CrossRef]
- Sang, L.; Yu, M.; Lin, H.; Zhang, Z.; Jin, R. Big Data, Technology Capability and Construction Project Quality: A Cross-Level Investigation. Eng. Constr. Archit. Manag. 2021, 28, 706–727. [Google Scholar] [CrossRef]
- Meng, Q.; Zhang, Y.; Li, Z.; Shi, W.; Wang, J.; Sun, Y.; Xu, L.; Wang, X. A Review of Integrated Applications of BIM and Related Technologies in Whole Building Life Cycle. Eng. Constr. Archit. Manag. 2020, 27, 1647–1677. [Google Scholar] [CrossRef]
- Soman, R.K.; Whyte, J.K. Codification Challenges for Data Science in Construction. J. Constr. Eng. Manag. 2020, 146, 04020072. [Google Scholar] [CrossRef]
- Maqsoom, A.; Ali, U.; ul Basharat, M.; Naeem, M.H.; Irfan, M. Impact of Project Selection Criteria on Organizational Performance: A Machine Learning Approach. In ASCE Inspire; ASCE: Reston, Virginia, 2023; pp. 133–142. [Google Scholar]
- Gransberg, N.J.; Maraqa, S. Leveraging the Value of Project Scope Growth through Construction Manager-at-Risk Delivery of Public University Capital Improvement Projects. J. Leg. Aff. Disput. Resolut. Eng. Constr. 2022, 14, 04521042. [Google Scholar] [CrossRef]
- Darko, A.; Chan, A.P.C.; Adabre, M.A.; Edwards, D.J.; Hosseini, M.R.; Ameyaw, E.E. Artificial Intelligence in the AEC Industry: Scientometric Analysis and Visualization of Research Activities. Autom. Constr. 2020, 112, 103081. [Google Scholar] [CrossRef]
- Li, C.Z.; Zhao, Y.; Xiao, B.; Yu, B.; Tam, V.W.Y.; Chen, Z.; Ya, Y. Research Trend of the Application of Information Technologies in Construction and Demolition Waste Management. J. Clean. Prod. 2020, 263, 121458. [Google Scholar] [CrossRef]
- Angelov, P.P.; Soares, E.A.; Jiang, R.; Arnold, N.I.; Atkinson, P.M. Explainable Artificial Intelligence: An Analytical Review. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2021, 11, e1424. [Google Scholar] [CrossRef]
- Boden, M.A. Inteligencia Artificial; Turner: Sydney, Australia, 2017; ISBN 84-16714-90-8. [Google Scholar]
- Hulsen, T. Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare. AI 2023, 4, 652–666. [Google Scholar] [CrossRef]
- Rouhiainen, L. Inteligencia Artificial; Alienta Editorial: Madrid, Spain, 2018. [Google Scholar]
- Zhang, C.; Lu, Y. Study on Artificial Intelligence: The State of the Art and Future Prospects. J. Ind. Inf. Integr. 2021, 23, 100224. [Google Scholar] [CrossRef]
- Abioye, S.O.; Oyedele, L.O.; Akanbi, L.; Ajayi, A.; Delgado, J.M.D.; Bilal, M.; Akinade, O.O.; Ahmed, A. Artificial Intelligence in the Construction Industry: A Review of Present Status, Opportunities and Future Challenges. J. Build. Eng. 2021, 44, 103299. [Google Scholar] [CrossRef]
- Martínez-Rojas, M.; Marín, N.; Vila, M.A. The Role of Information Technologies to Address Data Handling in Construction Project Management. J. Comput. Civ. Eng. 2016, 30, 04015064. [Google Scholar] [CrossRef]
- Elmousalami, H.H. Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review. J. Constr. Eng. Manag. 2020, 146, 03119008. [Google Scholar] [CrossRef]
- Wu, C.; Li, X.; Jiang, R.; Guo, Y.; Wang, J.; Yang, Z. Graph-based Deep Learning Model for Knowledge Base Completion in Constraint Management of Construction Projects. Comput. -Aided Civ. Infrastruct. Eng. 2023, 38, 702–719. [Google Scholar] [CrossRef]
- Huang, Y.; Shi, Q.; Zuo, J.; Pena-Mora, F.; Chen, J. Research Status and Challenges of Data-Driven Construction Project Management in the Big Data Context. Adv. Civ. Eng. 2021, 2021, 6674980. [Google Scholar] [CrossRef]
- Petticrew, M.; Roberts, H. Systematic Reviews in the Social Sciences: A Practical Guide; Wiley-Blackwell: Oxford, UK, 2006; p. 352. ISBN 978-1-4051-2110-1. [Google Scholar]
- Zabala-Vargas, S.A.; Ardila-Segovia, D.A.; García-Mora, L.H.; Benito-Crosetti, B.L. de Game-Based Learning (GBL) Applied to the Teaching of Mathematics in Higher Education. A Systematic Review of the Literature. Form. Univ. 2020, 13, 13–26. [Google Scholar] [CrossRef]
- Gast, I.; Schildkamp, K.; Veen, J.T. van der Team-Based Professional Development Interventions in Higher Education: A Systematic Review. Rev. Educ. Res. 2017, 87, 736–767. [Google Scholar] [CrossRef] [PubMed]
- Andronie, M.; Lăzăroiu, G.; Ștefănescu, R.; Ionescu, L.; Cocoșatu, M. Neuromanagement Decision-Making and Cognitive Algorithmic Processes in the Technological Adoption of Mobile Commerce Apps. Oeconomia Copernic. 2021, 12, 1033–1062. [Google Scholar] [CrossRef]
- Balcerzak, A.P.; Nica, E.; Rogalska, E.; Poliak, M.; Klieštik, T.; Sabie, O.-M. Blockchain Technology and Smart Contracts in Decentralized Governance Systems. Adm. Sci. 2022, 12, 96. [Google Scholar] [CrossRef]
- Akinosho, T.D.; Oyedele, L.O.; Bilal, M.; Ajayi, A.O.; Delgado, M.D.; Akinade, O.O.; Ahmed, A.A. Deep Learning in the Construction Industry: A Review of Present Status and Future Innovations. J. Build. Eng. 2020, 32, 101827. [Google Scholar] [CrossRef]
- Arroyo, P.; Tommelein, I.D.; Ballard, G. Comparing AHP and CBA as Decision Methods to Resolve the Choosing Problem in Detailed Design. J. Constr. Eng. Manag. 2015, 141, 04014063. [Google Scholar] [CrossRef]
- You, Z.; Wu, C. A Framework for Data-Driven Informatization of the Construction Company. Adv. Eng. Inform. 2019, 39, 269–277. [Google Scholar] [CrossRef]
- Arashpour, M.; Heidarpour, A.; Akbar Nezhad, A.; Hosseinifard, Z.; Chileshe, N.; Hosseini, R. Performance-Based Control of Variability and Tolerance in off-Site Manufacture and Assembly: Optimization of Penalty on Poor Production Quality. Constr. Manag. Econ. 2020, 38, 502–514. [Google Scholar] [CrossRef]
- Xue, F.; Wu, L.; Lu, W. Semantic Enrichment of Building and City Information Models: A Ten-Year Review. Adv. Eng. Inform. 2021, 47, 101245. [Google Scholar] [CrossRef]
- Bilal, M.; Oyedele, L.O.; Kusimo, H.O.; Owolabi, H.A.; Akanbi, L.A.; Ajayi, A.O.; Akinade, O.O.; Davila Delgado, J.M. Investigating Profitability Performance of Construction Projects Using Big Data: A Project Analytics Approach. J. Build. Eng. 2019, 26, 100850. [Google Scholar] [CrossRef]
- Nekouvaght Tak, A.; Taghaddos, H.; Mousaei, A.; Hermann, U. (Rick) Evaluating Industrial Modularization Strategies: Local vs. Overseas Fabrication. Autom. Constr. 2020, 114, 103175. [Google Scholar] [CrossRef]
- Cao, Y.; Ashuri, B. Predicting the Volatility of Highway Construction Cost Index Using Long Short-Term Memory. J. Manag. Eng. 2020, 36, 04020020. [Google Scholar] [CrossRef]
- Hsu, H.-C.; Chang, S.; Chen, C.-C.; Wu, I.-C. Knowledge-Based System for Resolving Design Clashes in Building Information Models. Autom. Constr. 2020, 110, 103001. [Google Scholar] [CrossRef]
- Jiang, Y.; He, X. Overview of Applications of the Sensor Technologies for Construction Machinery. IEEE Access 2020, 8, 110324–110335. [Google Scholar] [CrossRef]
- Cheng, M.-Y.; Hoang, N.-D. Estimating Construction Duration of Diaphragm Wall Using Firefly-Tuned Least Squares Support Vector Machine. Neural Comput. Applic 2018, 30, 2489–2497. [Google Scholar] [CrossRef]
- Salem, T.; Dragomir, M. Options for and Challenges of Employing Digital Twins in Construction Management. Appl. Sci. 2022, 12, 2928. [Google Scholar] [CrossRef]
- Oliveira, B.A.S.; Neto, A.P.D.F.; Fernandino, R.M.A.; Carvalho, R.F.; Fernandes, A.L.; Guimaraes, F.G. Guimarães Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning. IEEE Access 2021, 9, 19195–19207. [Google Scholar] [CrossRef]
- Amer, F.; Jung, Y.; Golparvar-Fard, M. Transformer Machine Learning Language Model for Auto-Alignment of Long-Term and Short-Term Plans in Construction. Autom. Constr. 2021, 132, 103929. [Google Scholar] [CrossRef]
- Li, W.; Duan, P.; Su, J. The Effectiveness of Project Management Construction with Data Mining and Blockchain Consensus. J. Ambient. Intell. Humaniz. Comput. 2021, 1, 1–10. [Google Scholar] [CrossRef]
- Cheng, M.-Y.; Cao, M.-T.; Herianto, J.G. Symbiotic Organisms Search-Optimized Deep Learning Technique for Mapping Construction Cash Flow Considering Complexity of Project. Chaos Solitons Fractals 2020, 138, 109869. [Google Scholar] [CrossRef]
- Xu, J.; Lu, W.; Ye, M.; Webster, C.; Xue, F. An Anatomy of Waste Generation Flows in Construction Projects Using Passive Bigger Data. Waste Manag. 2020, 106, 162–172. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Y.; Hu, Z.-Z.; Zhang, W.-Z. Development and Application of an Industry Foundation Classes-Based Metro Protection Information Model. Math. Probl. Eng. 2018, 2018, 1820631. [Google Scholar] [CrossRef]
- Pan, M.; Yang, Y.; Zheng, Z.; Pan, W. Artificial Intelligence and Robotics for Prefabricated and Modular Construction: A Systematic Literature Review. J. Constr. Eng. Manag. 2022, 148, 03122004. [Google Scholar] [CrossRef]
- Zhang, S.; Bogus, S.M.; Lippitt, C.D.; Kamat, V.; Lee, S. Implementing Remote-Sensing Methodologies for Construction Research: An Unoccupied Airborne System Perspective. J. Constr. Eng. Manag. 2022, 148, 03122005. [Google Scholar] [CrossRef]
- Ronghui, S.; Liangrong, N. An Intelligent Fuzzy-Based Hybrid Metaheuristic Algorithm for Analysis the Strength, Energy and Cost Optimization of Building Material in Construction Management. Eng. Comput. 2022, 38, 2663–2680. [Google Scholar] [CrossRef]
- Kanyilmaz, A.; Tichell, P.R.N.; Loiacono, D. A Genetic Algorithm Tool for Conceptual Structural Design with Cost and Embodied Carbon Optimization. Eng. Appl. Artif. Intell. 2022, 112, 104711. [Google Scholar] [CrossRef]
- Chen, S. Construction Project Cost Management and Control System Based on Big Data. Mob. Inf. Syst. 2022, 2022, 7908649. [Google Scholar] [CrossRef]
- Fang, L.; Mei, B.; Jiang, L.; Sun, J. Investigation of Intelligent Safety Management Information System for Nuclear Power Construction Projects. In Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering, Hunan, China, 3–5 December 2020; pp. 607–611. [Google Scholar]
- Jianfeng, Z.; Yechao, J.; Fang, L. Construction of Intelligent Building Design System Based on BIM and AI. In Proceedings of the 2020 5th International Conference on Smart Grid and Electrical Automation (ICSGEA), Zhangjiajie, China, 13–14 June 2020; pp. 277–280. [Google Scholar]
- Rampini, L.; Re Cecconi, F. Artificial Intelligence in Construction Asset Management: A Review of Present Status, Challenges and Future Opportunities. J. Inf. Technol. Constr. 2022, 27, 884–913. [Google Scholar] [CrossRef]
- Ali, Z.; Burhan, A.; Kassim, M.; Al-Khafaji, Z. Developing an Integrative Data Intelligence Model for Construction Cost Estimation. Complexity 2022, 2022, 4285328. [Google Scholar] [CrossRef]
- Chenya, L.; Aminudin, E.; Mohd, S.; Yap, L.S. Intelligent Risk Management in Construction Projects: Systematic Literature Review. IEEE Access 2022, 10, 72936–72954. [Google Scholar] [CrossRef]
- Edayadiyil, J.B.; Greeshma, A.S. Automated Progress Monitoring of Construction Projects Using Machine Learning and Image Processing Approach. Mater. Today: Proc. 2022, 65, 554–563. [Google Scholar] [CrossRef]
- Igwe, U.S.; Mohamed, S.F.; Dzahir Azwarie, M.B.M.; Ugulu, R.A.; Ajayi, O. Acceptance of Contemporary Technologies for Cost Management of Construction Projects. J. Inf. Technol. Constr. 2022, 27, 864–883. [Google Scholar] [CrossRef]
- Feng, N. The Influence Mechanism of BIM on Green Building Engineering Project Management under the Background of Big Data. Appl. Bionics Biomech. 2022, 2022, 8227930. [Google Scholar] [CrossRef]
- Tang, D.; Liu, K. Exploring the Application of BIM Technology in the Whole Process of Construction Cost Management with Computational Intelligence. Comput. Intell. Neurosci. 2022, 2022, 4080879. [Google Scholar] [CrossRef]
- Chen, S.; Cui, Y.; Zhu, Y.; Song, G.; Shi, Y. Development of Economic Evaluation System for Building Project Based on Computer Technology. Mob. Inf. Syst. 2022, 2022, 2363669. [Google Scholar] [CrossRef]
- Wang, H.; Hu, Y. Artificial Intelligence Technology Based on Deep Learning in Building Construction Management System Modeling. Adv. Multimed. 2022, 2022, 5602842. [Google Scholar] [CrossRef]
- Ruperto, F.; Strappini, S. Complex Works Project Management Enhanced by Digital Technologies. Build. Inf. Model. (BIM) Des. Constr. Oper. IV 2021, 205, 235. [Google Scholar]
- Wang, T. Research on Detailed Design of Prefabricated Building Based on BIM and Big Data; IOP Publishing: Bristol, UK, 2021; Volume 2037, p. 012133. [Google Scholar]
- Qian, Z.; Yang, X.; Xu, Z.; Cai, W. Research on Key Construction Technology of Building Engineering under the Background of Big Data; IOP Publishing: Bristol, UK, 2021; Volume 1802, p. 032003. [Google Scholar]
- Wang, N.; Issa, R.; Anumba, C. Query Answering System for Building Information Modeling Using BERT NN Algorithm and NLG. In Computing in Civil Engineering; ACSE: Reston, VA, USA, 2022; p. 432. [Google Scholar]
- Pan, J.; Rao, Y. Research on Digital Collaborative Management Model of Engineering Projects Based on BIM and IPD. In Proceedings of the 2021 2nd International Conference on Big Data Economy and Information Management (BDEIM), Sanya, China, 3–5 December 2021; pp. 52–58. [Google Scholar]
- Li, C.; Chen, L.; Wang, J.; Xia, T. A Method for Image Big Data Utilization: Automated Progress Monitoring Based on Image Data for Large Construction Site. In Proceedings of the Big Data and Security; Tian, Y., Ma, T., Khan, M.K., Eds.; Springer: Singapore, 2021; pp. 299–313. [Google Scholar]
- Pfahlsberger, L.; Mendling, J. Design of a Process Mining Alignment Method for Building Big Data Analytics Capabilities. In Proceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS, Kauai Island, HI, USA, 5–8 January 2021. [Google Scholar]
- Górecki, J. Big Data as a Project Risk Management Tool. In Risk Management Treatise for Engineering Practitioners; IntechOpen: Rijeka, Croatia, 2018; ISBN 978-1-78984-600-3. [Google Scholar]
- Llave, M.R. Data Lakes in Business Intelligence: Reporting from the Trenches. Procedia Comput. Sci. 2018, 138, 516–524. [Google Scholar] [CrossRef]
- Han, Z.; Wang, Y. The Applied Exploration of Big Data Technology in Prefabricated Construction Project Management. In ICCREM; American Society of Civil Engineers: Guangzhou, China, 2017; pp. 71–78. [Google Scholar]
- Honcharenko, T.; Kyivska, K.; Serpinska, O.; Savenko, V.; Kysliuk, D.; Orlyk, Y. Digital Transformation of the Construction Design Based on the Building Information Modeling and Internet of Things. In Proceedings of the ITTAP, Ternopil, Ukraine, 16–18 November 2021; pp. 267–279. [Google Scholar]
- Boton, C.; Halin, G.; Kubicki, S.; Forgues, D. Challenges of Big Data in the Age of Building Information Modeling: A High-Level Conceptual Pipeline. In Proceedings of the Cooperative Design, Visualization, and Engineering: 12th International Conference, CDVE 2015, Mallorca, Spain, 20–23 September 2015; pp. 48–56. [Google Scholar]
- Yuan, X.; Chen, Y.-W.; Fan, H.; He, W.-H.; Ming, X.G. Collaborative Construction Industry Integrated Management Service System Framework Based on Big Data. In Proceedings of the 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, 15–18 December 2019; pp. 1521–1525. [Google Scholar]
- Project Management Institute. Guía de Los Fundamentos Para La Dirección de Proyectos (Guía Del Pmbok), 6th ed.; Project Management Institute: Newtown Square, PA, USA, 2017; p. 589. [Google Scholar]
- Zandi, Y.; Issakhov, A.; Roco Videla, Á.; Wakil, K.; Wang, Q.; Cao, Y.; Selmi, A.; Agdas, A.S.; Fu, L.; Qian, X. A Review Study of Application of Artificial Intelligence in Construction Management and Composite Beams; University of California Santa Cruz: Santa Cruz, CA, USA, 2021. [Google Scholar]
- Fletcher, D. Internet of Things. In The Internet of Things (IoT)—Essential IoT Business Guide; Springer: Berlin/Heidelberg, Germany, 2015; pp. 19–32. [Google Scholar] [CrossRef]
- Shen, X.; Lin, X.; Zhang, K. (Eds.) Wireless Sensor Network. In Encyclopedia of Wireless Networks; Springer International Publishing: Cham, Switzerland, 2020; p. 1496. ISBN 978-3-319-78262-1. [Google Scholar]
- Yao, H.; Guizani, M. Intelligent Internet of Things Networking Architecture. In Intelligent Internet of Things Networks; Springer: Berlin/Heidelberg, Germany, 2023; pp. 23–35. [Google Scholar]
ID | Title | Year | Type | Method | Technology |
---|---|---|---|---|---|
1 | Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities [36]. | 2020 | Review | Literature Review | Artificial Intelligence |
2 | Bridging BIM and building: From a literature review to an integrated conceptual framework [9]. | 2015 | Article | Quantitative | Big Data/Data Science |
3 | Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking [12]. | 2017 | Article | Quantitative | Artificial Intelligence /Big Data |
4 | An effective approach for software project effort and duration estimation with machine learning algorithms [10]. | 2018 | Article | Quantitative | Artificial Intelligence/Data Science |
5 | Construction with digital twin information systems [11]. | 2020 | Article | Quantitative | Artificial Intelligence/Big Data/Data Science |
6 | Deep learning in the construction industry: A review of present status and future innovations [53]. | 2020 | Article | Quantitative | Artificial Intelligence /Big Data |
7 | Optimizing decisions in advanced manufacturing of prefabricated products: Theorizing supply chain configurations in off-site construction [8]. | 2017 | Article | Quantitative | Data Science |
8 | Comparing AHP and CBA as decision methods to resolve the choosing problem in detailed design [54]. | 2015 | Article | Mixed | Data Science |
9 | Research trend of the application of information technologies in construction and demolition waste management [37]. | 2020 | Review | Literature Review | Artificial Intelligence /Big Data |
10 | A framework for data-driven informatization of the construction company [55]. | 2019 | Article | Qualitative | Big Data/Data Science |
11 | Performance-based control of variability and tolerance in off-site manufacture and assembly: optimization of penalty on poor production quality [56]. | 2020 | Article | Qualitative | Artificial Intelligence/Data Science |
12 | Semantic enrichment of building and city information models: A ten-year review [57]. | 2021 | Article | Mixed | Data Science |
13 | Investigating profitability performance of construction projects using big data: A project analytics approach [58]. | 2019 | Article | Quantitative | Big Data |
14 | Evaluating industrial modularization strategies: Local vs. overseas [59]. | 2020 | Article | Quantitative | Data Science |
15 | Predicting the Volatility of Highway Construction Cost Index Using Long Short-Term Memory [60]. | 2020 | Article | Quantitative | Artificial Intelligence |
16 | Knowledge-based system for resolving design clashes in building information models [61]. | 2020 | Article | Qualitative | Artificial Intelligence |
17 | Overview of Applications of the Sensor Technologies for Construction Machinery [62]. | 2020 | Article | Quantitative | Big Data/Data Science |
18 | Building a rough sets-based prediction model for classifying large-scale construction projects based on sustainable success index [4]. | 2018 | Article | Quantitative | Artificial Intelligence/Data Science |
19 | Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine [63]. | 2018 | Article | Quantitative | Artificial Intelligence |
20 | Options for and Challenges of Employing Digital Twins in Construction Management [64]. | 2022 | Article | Qualitative | Artificial Intelligence/Big Data/Data Science |
21 | Research Status and Challenges of Data-Driven Construction Project Management in the Big Data Context [47]. | 2021 | Review | Literature Review | Artificial Intelligence/Big Data/Data Science |
22 | Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning [65]. | 2021 | Article | Mixed | Artificial Intelligence/Data Science |
23 | Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction [66]. | 2021 | Article | Qualitative | Artificial Intelligence |
24 | The effectiveness of project management construction with data mining and blockchain consensus [67]. | 2021 | Article | Mixed | Artificial Intelligence/Big Data/Data Science |
25 | Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project [68]. | 2020 | Article | Quantitative | Artificial Intelligence/Data Science |
26 | An anatomy of waste generation flows in construction projects using passive bigger data [69]. | 2020 | Article | Quantitative | Big Data |
27 | Development and Application of an Industry Foundation Classes-Based Metro Protection Information Model [70]. | 2018 | Article | Mixed | Artificial Intelligence/Big Data/Data Science |
28 | Artificial Intelligence and Robotics for Prefabricated and Modular Construction: A Systematic Literature Review [71]. | 2022 | Review | Literature Review | Artificial Intelligence |
29 | Implementing Remote-Sensing Methodologies for Construction Research: An Unoccupied Airborne System Perspective [72]. | 2022 | Review | Literature Review | Data Science |
30 | An intelligent fuzzy-based hybrid metaheuristic algorithm for analysis the strength, energy and cost optimization of building material in construction management [73]. | 2021 | Article | Quantitative | Artificial Intelligence |
31 | A genetic algorithm tool for conceptual structural design with cost and embodied carbon optimization [74]. | 2022 | Article | Quantitative | Artificial Intelligence |
32 | Construction Project Cost Management and Control System Based on Big Data [75]. | 2022 | Article | Mixed | Big Data |
33 | Investigation of intelligent safety management information system for nuclear power construction projects [76]. | 2020 | Conference Paper | Qualitative | Artificial Intelligence/Big Data/Data Science |
34 | Construction of Intelligent Building Design System Based on BIM and AI [77]. | 2020 | Conference Paper | Qualitative | Artificial Intelligence/Big Data |
35 | Artificial Intelligence In Construction Asset Management: A Review Of Present Status, Challenges And Future Opportunities [78]. | 2022 | Article | Quantitative | Artificial Intelligence |
36 | Developing an Integrative Data Intelligence Model for Construction Cost Estimation [79]. | 2022 | Article | Qualitative | Artificial Intelligence |
37 | Intelligent Risk Management in Construction Projects: Systematic Literature Review [80]. | 2022 | Article | Quantitative | Artificial Intelligence |
38 | Automated progress monitoring of construction projects using Machine learning and image processing approach [81]. | 2022 | Article | Qualitative | Artificial Intelligence/Data Science |
39 | Acceptance Of Contemporary Technologies For Cost Management Of Construction Projects [82]. | 2022 | Article | Quantitative | Artificial Intelligence/Data Science |
40 | The Influence Mechanism of BIM on Green Building Engineering Project Management under the Background of Big Data [83]. | 2022 | Article | Mixed | Data Science |
41 | Exploring the Application of BIM Technology in the Whole Process of Construction Cost Management with Computational Intelligence [84]. | 2022 | Review | Literature Review | Data Science |
42 | Development of Economic Evaluation System for Building Project Based on Computer Technology [85]. | 2022 | Article | Qualitative | Artificial Intelligence |
43 | Artificial Intelligence Technology Based on Deep Learning in Building Construction Management System Modeling [86]. | 2022 | Article | Quantitative | Artificial Intelligence/Data Science |
44 | Complex works project management enhanced by Digital Technologies [87]. | 2022 | Conference Paper | Qualitative | Artificial Intelligence/Big Data/Data Sciencei |
45 | Research on detailed design of prefabricated building based on BIM and big data [88]. | 2021 | Conference Paper | Literature Review | Big Data/Data Science |
46 | Research on Key Construction Technology of Building Engineering under the Background of Big Data [89]. | 2021 | Conference Paper | Mixed | Big Data/Data Science |
47 | Query Answering System for Building Information Modeling Using BERT NN Algorithm and NLG [90]. | 2021 | Conference Paper | Qualitative | Artificial Intelligence/Data Science |
48 | Research on digital collaborative management model of engineering projects based on BIM and IPD [91]. | 2021 | Conference Paper | Mixed | Inteligencia artifical/Big Data |
49 | A Method for Image Big Data Utilization: Automated Progress Monitoring Based on Image Data for Large Construction Site [92]. | 2021 | Conference Paper | Mixed | Big Data/Data Science |
50 | Design of a process mining alignment method for building big data analytics capabilities [93]. | 2021 | Conference Paper | Mixed | Big Data/Data Science |
51 | Big Data as a Project Risk Management Tool [94]. | 2018 | Conference Paper | Mixed | Big Data |
52 | Data lakes in business intelligence: Reporting from the trenches [95] | 2018 | Conference Paper | Qualitative | Big Data |
53 | The Applied Exploration of Big Data Technology in Prefabricated Construction Project Management [96] | 2017 | Conference Paper | Qualitative | Big Data/Data Science |
54 | Digital transformation of the construction design based on the building information modeling and internet of things [97] | 2021 | Conference Paper | Qualitative | Big Data/Data Science |
55 | Challenges of big data in the age of building information modeling: A high-level conceptual pipeline [98] | 2015 | Conference Paper | Literature Review | Big Data |
56 | Digital Technology in Architecture, Engineering, and Construction (AEC) Industry: Research Trends and Practical Status toward Construction 4.0 [22] | 2022 | Conference Paper | Quantitative | Artificial Intelligence/Big Data/Data Science |
57 | Collaborative Construction Industry Integrated Management Service System Framework Based on Big Data [99] | 2019 | Conference Paper | Qualitative | Big Data/Data Science |
Type | Title | ISSN/ISBN | Quartile (Scopus) | H-Index | Number of Articles |
---|---|---|---|---|---|
Conference | ACM International Conference Proceeding Series | N/A | N/A | 137 | 11 |
Conference | Procedia Computer Science | 18770509 | N/A | 109 | 11 |
Journal | Advances in Intelligent Systems and Computing | 21945365 | Q4 | 58 | 8 |
Journal | Automation in Construction | 9265805 | Q1 | 157 | 7 |
Conference | Communications in Computer and Information Science | 18650937 | Q4 | 62 | 6 |
Conference | Journal of Physics: Conference Series | 17426588 | Q4 | 91 | 5 |
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Zabala-Vargas, S.; Jaimes-Quintanilla, M.; Jimenez-Barrera, M.H. Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review. Buildings 2023, 13, 2944. https://doi.org/10.3390/buildings13122944
Zabala-Vargas S, Jaimes-Quintanilla M, Jimenez-Barrera MH. Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review. Buildings. 2023; 13(12):2944. https://doi.org/10.3390/buildings13122944
Chicago/Turabian StyleZabala-Vargas, Sergio, María Jaimes-Quintanilla, and Miguel Hernán Jimenez-Barrera. 2023. "Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review" Buildings 13, no. 12: 2944. https://doi.org/10.3390/buildings13122944